#SQL query from a Text File

[lazeecomet]

The Story of KLogs: What happens when an Mechanical Engineer codes

Since i no longer work at Wearhouse Automation Startup (WAS for short) and havnt for many years i feel as though i should recount the tale of the most bonkers program i ever wrote, but we need to establish some background

WAS has its HQ very far away from the big customer site and i worked as a Field Service Engineer (FSE) on site. so i learned early on that if a problem needed to be solved fast, WE had to do it. we never got many updates on what was coming down the pipeline for us or what issues were being worked on. this made us very independent

As such, we got good at reading the robot logs ourselves. it took too much time to send the logs off to HQ for analysis and get back what the problem was. we can read. now GETTING the logs is another thing.

the early robots we cut our teeth on used 2.4 gHz wifi to communicate with FSE's so dumping the logs was as simple as pushing a button in a little application and it would spit out a txt file

later on our robots were upgraded to use a 2.4 mHz xbee radio to communicate with us. which was FUCKING SLOW. and log dumping became a much more tedious process. you had to connect, go to logging mode, and then the robot would vomit all the logs in the past 2 min OR the entirety of its memory bank (only 2 options) into a terminal window. you would then save the terminal window and open it in a text editor to read them. it could take up to 5 min to dump the entire log file and if you didnt dump fast enough, the ACK messages from the control server would fill up the logs and erase the error as the memory overwrote itself.

this missing logs problem was a Big Deal for software who now weren't getting every log from every error so a NEW method of saving logs was devised: the robot would just vomit the log data in real time over a DIFFERENT radio and we would save it to a KQL server. Thanks Daddy Microsoft.

now whats KQL you may be asking. why, its Microsofts very own SQL clone! its Kusto Query Language. never mind that the system uses a SQL database for daily operations. lets use this proprietary Microsoft thing because they are paying us

so yay, problem solved. we now never miss the logs. so how do we read them if they are split up line by line in a database? why with a query of course!

select * from tbLogs where RobotUID = [64CharLongString] and timestamp > [UnixTimeCode]

if this makes no sense to you, CONGRATULATIONS! you found the problem with this setup. Most FSE's were BAD at SQL which meant they didnt read logs anymore. If you do understand what the query is, CONGRATULATIONS! you see why this is Very Stupid.

You could not search by robot name. each robot had some arbitrarily assigned 64 character long string as an identifier and the timestamps were not set to local time. so you had run a lookup query to find the right name and do some time zone math to figure out what part of the logs to read. oh yeah and you had to download KQL to view them. so now we had both SQL and KQL on our computers

NOBODY in the field like this.

But Daddy Microsoft comes to the rescue

see we didnt JUST get KQL with part of that deal. we got the entire Microsoft cloud suite. and some people (like me) had been automating emails and stuff with Power Automate

This is Microsoft Power Automate. its Microsoft's version of Scratch but it has hooks into everything Microsoft. SharePoint, Teams, Outlook, Excel, it can integrate with all of it. i had been using it to send an email once a day with a list of all the robots in maintenance.

this gave me an idea

and i checked

and Power Automate had hooks for KQL

KLogs is actually short for Kusto Logs

I did not know how to program in Power Automate but damn it anything is better then writing KQL queries. so i got to work. and about 2 months later i had a BEHEMOTH of a Power Automate program. it lagged the webpage and many times when i tried to edit something my changes wouldn't take and i would have to click in very specific ways to ensure none of my variables were getting nuked. i dont think this was the intended purpose of Power Automate but this is what it did

the KLogger would watch a list of Teams chats and when someone typed "klogs" or pasted a copy of an ERROR mesage, it would spring into action.

it extracted the robot name from the message and timestamp from teams

it would lookup the name in the database to find the 64 long string UID and the location that robot was assigned too

it would reply to the message in teams saying it found a robot name and was getting logs

it would run a KQL query for the database and get the control system logs then export then into a CSV

it would save the CSV with the a .xls extension into a folder in ShairPoint (it would make a new folder for each day and location if it didnt have one already)

it would send ANOTHER message in teams with a LINK to the file in SharePoint

it would then enter a loop and scour the robot logs looking for the keyword ESTOP to find the error. (it did this because Kusto was SLOWER then the xbee radio and had up to a 10 min delay on syncing)

if it found the error, it would adjust its start and end timestamps to capture it and export the robot logs book-ended from the event by ~ 1 min. if it didnt, it would use the timestamp from when it was triggered +/- 5 min

it saved THOSE logs to SharePoint the same way as before

it would send ANOTHER message in teams with a link to the files

it would then check if the error was 1 of 3 very specific type of error with the camera. if it was it extracted the base64 jpg image saved in KQL as a byte array, do the math to convert it, and save that as a jpg in SharePoint (and link it of course)

and then it would terminate. and if it encountered an error anywhere in all of this, i had logic where it would spit back an error message in Teams as plaintext explaining what step failed and the program would close gracefully

I deployed it without asking anyone at one of the sites that was struggling. i just pointed it at their chat and turned it on. it had a bit of a rocky start (spammed chat) but man did the FSE's LOVE IT.

about 6 months later software deployed their answer to reading the logs: a webpage that acted as a nice GUI to the KQL database. much better then an CSV file

it still needed you to scroll though a big drop-down of robot names and enter a timestamp, but i noticed something. all that did was just change part of the URL and refresh the webpage

SO I MADE KLOGS 2 AND HAD IT GENERATE THE URL FOR YOU AND REPLY TO YOUR MESSAGE WITH IT. (it also still did the control server and jpg stuff). Theres a non-zero chance that klogs was still in use long after i left that job

now i dont recommend anyone use power automate like this. its clunky and weird. i had to make a variable called "Carrage Return" which was a blank text box that i pressed enter one time in because it was incapable of understanding /n or generating a new line in any capacity OTHER then this (thanks support forum).

im also sure this probably is giving the actual programmer people anxiety. imagine working at a company and then some rando you've never seen but only heard about as "the FSE whos really good at root causing stuff", in a department that does not do any coding, managed to, in their spare time, build and release and entire workflow piggybacking on your work without any oversight, code review, or permission.....and everyone liked it

[xaltius]

From Data to Stories: How Code Agents are Revolutionizing KPI Narratives

In the bustling world of business, Key Performance Indicators (KPIs) are the lifeblood. They tell us if we're winning, losing, or just holding steady. But the true value of a KPI isn't just the number itself; it's the story behind that number. Why did sales dip last quarter? What drove that spike in customer engagement? How do these trends impact our strategic goals?

Traditionally, extracting these narratives from raw data is a laborious, time-consuming, and often inconsistent process. Data analysts spend countless hours querying databases, generating charts, and then manually crafting explanations that are clear, concise, and actionable for non-technical stakeholders.

Enter the game-changer: Code Agents.

The Challenge: Bridging the Data-Narrative Gap

The journey from a spreadsheet full of numbers to a compelling business narrative is fraught with challenges:

Time-Consuming: Manual analysis and writing for every KPI update drains valuable analyst time.

Inconsistency: Different analysts might highlight different aspects or use varying tones, leading to fragmented insights.

Lack of Depth: Surface-level explanations often miss the underlying drivers or complex interdependencies.

Actionability Gap: Numbers without context or clear recommendations can leave decision-makers scratching their heads.

Data Silos: The narrative often requires pulling data from multiple, disparate sources, adding complexity.

This is where the magic of AI, specifically AI "Code Agents," comes into play.

What are Code Agents? Your Automated Storytellers

A Code Agent is an advanced Artificial Intelligence (typically built on a Large Language Model) that can do more than just generate text. It possesses the unique ability to:

Generate Code: Write programming scripts (e.g., Python with Pandas, SQL queries).

Execute Code: Run those scripts against real data.

Interpret Results: Understand the output of the executed code.

Reason & Debug: Adjust its approach if the code fails or the results are not what's needed.

Generate Narrative: Translate the data insights derived from code execution into natural language.

Unlike a simple chatbot that might "hallucinate" a story, a Code Agent operates with data-backed precision. It proves its narrative by fetching and analyzing the data itself.

How Code Agents Weave KPI Narratives: The Automated Workflow

Imagine a seamless process where your KPIs practically narrate their own stories:

Data Access & Understanding: The Code Agent is given secure access to your data sources – be it a SQL database, a data warehouse, flat files, or APIs. It understands the schema and table relationships.

Dynamic Analysis & Root Cause Identification:

When a KPI (e.g., "Monthly Active Users") changes significantly, the agent is prompted to investigate.

It dynamically writes and executes SQL queries or Python scripts to slice and dice the data by relevant dimensions (e.g., region, acquisition channel, product feature, time period).

It identifies trends, outliers, correlations, and deviations from targets or historical norms.

It can even perform deeper causal analysis by looking at related metrics (e.g., if sales dropped, did website traffic also drop? Did a marketing campaign underperform?).

Narrative Generation:

Based on its data analysis, the Code Agent constructs a coherent narrative.

It starts with what happened (the KPI change), explains why it happened (the drivers identified), discusses the impact on the business, and even suggests potential recommendations for action.

The narrative can be tailored for different audiences – a concise executive summary for leadership, or a more detailed breakdown for functional teams.

Iteration and Refinement: If the initial narrative isn't quite right, you can provide feedback, and the agent will refine its analysis and explanation. "Tell me more about the regional differences," or "Focus on actionable steps for the marketing team."

Powerful Benefits of Code Agents for KPI Narratives

Implementing Code Agents for KPI storytelling brings a wealth of advantages:

Unprecedented Speed & Efficiency: Generate comprehensive KPI narratives in minutes, not hours or days, enabling faster decision-making cycles.

Consistent Accuracy & Reliability: Narratives are directly derived from live data and consistent analytical logic, minimizing human error and ensuring data integrity.

Deeper, More Nuanced Insights: Agents can perform complex, multi-variate analyses that might be too time-consuming for manual execution, uncovering hidden drivers and subtle trends.

Reduced Analytical Bottlenecks: Free up your valuable data professionals from repetitive reporting tasks, allowing them to focus on strategic thinking, complex modeling, and innovative problem-solving.

Democratization of Insights: Make rich, data-backed narratives accessible to more stakeholders across the organization, fostering a truly data-driven culture.

Proactive Problem Solving: By quickly identifying the "why" behind KPI movements, teams can react faster to challenges and seize opportunities.

Getting Started with Code Agents

While the technology is advanced, integrating Code Agents into your workflow is becoming increasingly accessible:

Leverage AI Platforms: Many leading AI platforms now offer advanced LLMs with code execution capabilities (e.g., Anthropic's Claude 4, Google's Gemini, OpenAI's models with code interpreter tools).

Ensure Data Governance & Security: Provide secure, read-only access to necessary data sources. Robust data governance and privacy protocols are paramount.

Human Oversight is Key: Code Agents are powerful tools, not infallible decision-makers. Always review their generated narratives for accuracy, nuance, and strategic alignment before dissemination. They are co-pilots, not replacements.

The ability to automatically turn raw data into compelling, actionable stories is no longer a futuristic dream. Code Agents are here, transforming how businesses understand their performance, enabling faster, smarter decisions, and truly empowering data to speak for itself. The age of automated KPI narratives has arrived.

[promptlyspeedyandroid]

Complete PHP Tutorial: Learn PHP from Scratch in 7 Days

Are you looking to learn backend web development and build dynamic websites with real functionality? You’re in the right place. Welcome to the Complete PHP Tutorial: Learn PHP from Scratch in 7 Days — a practical, beginner-friendly guide designed to help you master the fundamentals of PHP in just one week.

PHP, or Hypertext Preprocessor, is one of the most widely used server-side scripting languages on the web. It powers everything from small blogs to large-scale websites like Facebook and WordPress. Learning PHP opens up the door to back-end development, content management systems, and full-stack programming. Whether you're a complete beginner or have some experience with HTML/CSS, this tutorial is structured to help you learn PHP step by step with real-world examples.

Why Learn PHP?

Before diving into the tutorial, let’s understand why PHP is still relevant and worth learning in 2025:

Beginner-friendly: Easy syntax and wide support.

Open-source: Free to use with strong community support.

Cross-platform: Runs on Windows, macOS, Linux, and integrates with most servers.

Database integration: Works seamlessly with MySQL and other databases.

In-demand: Still heavily used in CMS platforms like WordPress, Joomla, and Drupal.

If you want to build contact forms, login systems, e-commerce platforms, or data-driven applications, PHP is a great place to start.

Day-by-Day Breakdown: Learn PHP from Scratch in 7 Days

Day 1: Introduction to PHP & Setup

Start by setting up your environment:

Install XAMPP or MAMP to create a local server.

Create your first .php file.

Learn how to embed PHP inside HTML.

Example:

<?php echo "Hello, PHP!"; ?>

What you’ll learn:

How PHP works on the server

Running PHP in your browser

Basic syntax and echo statement

Day 2: Variables, Data Types & Constants

Dive into PHP variables and data types:

$name = "John"; $age = 25; $is_student = true;

Key concepts:

Variable declaration and naming

Data types: String, Integer, Float, Boolean, Array

Constants and predefined variables ($_SERVER, $_GET, $_POST)

Day 3: Operators, Conditions & Control Flow

Learn how to make decisions in PHP:

if ($age > 18) { echo "You are an adult."; } else { echo "You are underage."; }

Topics covered:

Arithmetic, comparison, and logical operators

If-else, switch-case

Nesting conditions and best practices

Day 4: Loops and Arrays

Understand loops to perform repetitive tasks:

$fruits = ["Apple", "Banana", "Cherry"]; foreach ($fruits as $fruit) { echo $fruit. "<br>"; }

Learn about:

for, while, do...while, and foreach loops

Arrays: indexed, associative, and multidimensional

Array functions (count(), array_push(), etc.)

Day 5: Functions & Form Handling

Start writing reusable code and learn how to process user input from forms:

function greet($name) { return "Hello, $name!"; }

Skills you gain:

Defining and calling functions

Passing parameters and returning values

Handling HTML form data with $_POST and $_GET

Form validation and basic security tips

Day 6: Working with Files & Sessions

Build applications that remember users and work with files:

session_start(); $_SESSION["username"] = "admin";

Topics included:

File handling (fopen, fwrite, fread, etc.)

Reading and writing text files

Sessions and cookies

Login system basics using session variables

Day 7: PHP & MySQL – Database Connectivity

On the final day, you’ll connect PHP to a database and build a mini CRUD app:

$conn = new mysqli("localhost", "root", "", "mydatabase");

Learn how to:

Connect PHP to a MySQL database

Create and execute SQL queries

Insert, read, update, and delete (CRUD operations)

Display database data in HTML tables

Bonus Tips for Mastering PHP

Practice by building mini-projects (login form, guest book, blog)

Read official documentation at php.net

Use tools like phpMyAdmin to manage databases visually

Try MVC frameworks like Laravel or CodeIgniter once you're confident with core PHP

What You’ll Be Able to Build After This PHP Tutorial

After following this 7-day PHP tutorial, you’ll be able to:

Create dynamic web pages

Handle form submissions

Work with databases

Manage sessions and users

Understand the logic behind content management systems (CMS)

This gives you the foundation to become a full-stack developer, or even specialize in backend development using PHP and MySQL.

Final Thoughts

Learning PHP doesn’t have to be difficult or time-consuming. With the Complete PHP Tutorial: Learn PHP from Scratch in 7 Days, you’re taking a focused, structured path toward web development success. You’ll learn all the core concepts through clear explanations and hands-on examples that prepare you for real-world projects.

Whether you’re a student, freelancer, or aspiring developer, PHP remains a powerful and valuable skill to add to your web development toolkit.

So open up your code editor, start typing your first <?php ... ?> block, and begin your journey to building dynamic, powerful web applications — one day at a time.

[unini2]

Identify elements of a search solution

A search index contains your searchable content. In an Azure AI Search solution, you create a search index by moving data through the following indexing pipeline:

Start with a data source: the storage location of your original data artifacts, such as PDFs, video files, and images. For Azure AI Search, your data source could be files in Azure Storage, or text in a database such as Azure SQL Database or Azure Cosmos DB.

Indexer: automates the movement data from the data source through document cracking and enrichment to indexing. An indexer automates a portion of data ingestion and exports the original file type to JSON (in an action called JSON serialization).

Document cracking: the indexer opens files and extracts content.

Enrichment: the indexer moves data through AI enrichment, which implements Azure AI on your original data to extract more information. AI enrichment is achieved by adding and combining skills in a skillset. A skillset defines the operations that extract and enrich data to make it searchable. These AI skills can be either built-in skills, such as text translation or Optical Character Recognition (OCR), or custom skills that you provide. Examples of AI enrichment include adding captions to a photo and evaluating text sentiment. AI enriched content can be sent to a knowledge store, which persists output from an AI enrichment pipeline in tables and blobs in Azure Storage for independent analysis or downstream processing.

Push to index: the serialized JSON data populates the search index.

The result is a populated search index which can be explored through queries. When users make a search query such as "coffee", the search engine looks for that information in the search index. A search index has a structure similar to a table, known as the index schema. A typical search index schema contains fields, the field's data type (such as string), and field attributes. The fields store searchable text, and the field attributes allow for actions such as filtering and sorting. 

[govindhtech]

Mistral OCR 25.05, Mistral AI Le Chat Enterprise on Google

Google Cloud offers Mistral AI’s Le Chat Enterprise and OCR 25.05 models.

Google Cloud provides consumers with an open and adaptable AI environment to generate customised solutions. As part of this commitment, Google Cloud has upgraded AI solutions with Mistral AI.

Google Cloud has two Mistral AI products:

Google Cloud Marketplace’s Le Chat Enterprise

Vertex AI Mistral OCR 25.05

Google Cloud Marketplace Mistral AI Le Chat Enterprise

Le Chat Enterprise is a feature-rich generative AI work assistant. Available on Google Cloud Marketplace. Its main purpose is to boost productivity by integrating technologies and data.

Le Chat Enterprise offers many functions on one platform, including:

Custom data and tool integrations (Google Drive, Sharepoint, OneDrive, Google Calendar, and Gmail initially, with more to follow, including templates)

Enterprise search

Agents build

Users can create private document libraries to reference, extract, and analyse common documents from Drive, Sharepoint, and uploads.

Personalised models

Implementations hybrid

Further MCP support for corporate system connectivity; Auto Summary for fast file viewing and consumption; secure data, tool connections, and libraries

Mistral AI’s Medium 3 model powers Le Chat Enterprise. AI productivity on a single, flexible, and private platform is its goal. Flexible deployment choices like self-hosted, in your public or private cloud, or as a Mistral cloud service let you choose the optimal infrastructure without being locked in. Data is protected by privacy-first data connections and strict ACL adherence.

The stack is fully configurable, from models and platforms to interfaces. Customisation includes bespoke connectors with company data, platform/model features like user feedback loops for model self-improvement, and assistants with stored memories. Along with thorough audit logging and storage, it provides full security control. Mistral’s AI scientists and engineers help deliver value and improve solutioning.

Example Le Chat Enterprise use cases:

Agent creation: Users can develop and implement context-aware, no-code agents.

Accelerating research and analysis: Summarises large reports, extracts key information from documents, and conducts brief web searches.

Producing actionable insights: It can automate financial report production, produce text-to-SQL queries for financial research, and turn complex data into actionable insights for finance.

Accelerates software development: Code generation, review, technical documentation, debugging, and optimisation.

Canvas improves content production by letting marketers interact on visuals, campaign analysis, and writing.

For scalability and security, organisations can use Le Chat Enterprise on the Google Cloud Marketplace. It integrates to Google Cloud services like BigQuery and Cloud SQL and facilitates procurement.

Contact Mistral AI sales and visit the Le Chat Enterprise Google Cloud Marketplace page to use Mistral’s Le Chat Enterprise. The Mistral AI announcement has further details. Le Chat (chat.mistral.ai) and their mobile apps allow free trial use.

OCR 25.05 model llm Mistral

One new OCR API is Mistral OCR 25.05. Vertex AI Model Garden has it. This model excels at document comprehension. It raises the bar in this discipline and can cognitively interpret text, media, charts, tables, graphs, and equations in content-rich papers. From PDFs and photos, it retrieves organised interleaved text and visuals.

Cost of Mistral OCR?

With a Retrieval Augmented Generation (RAG) system that takes multimodal documents, Mistral OCR is considered the ideal model. Additionally, millions of Le Chat users use Mistral OCR as their default document interpretation model. Mistral’s Platform developer suite offers the Mistral-ocr-latest API, which will soon be offered on-premises and to cloud and inference partners. The API costs 1000 pages/$ (double with batch inference).

Highlights of Mistral OCR include:

Cutting-edge comprehension of complex papers, including mathematical formulas, tables, interleaved images, and LaTeX formatting, helps readers understand rich content like scientific articles.

This system is multilingual and multimodal, parsing, understanding, and transcribing thousands of scripts, fonts, and languages. This is crucial for global and hyperlocal businesses.

Excellent benchmarks: This model consistently outperforms top OCR models in rigorous benchmark tests. Compared to Google Document AI, Azure OCR, Gemini models, and GPT-4o, Mistral OCR 2503 scores highest in Overall, Math, Multilingual, Scanned, and Tables accuracy. It also has the highest Fuzzy Match in Generation and multilingual scores compared to Azure OCR, Google Doc AI, and Gemini-2.0-Flash-001. It extracts embedded images and text, unlike other LLMs in the benchmark.

The lightest and fastest in its class, processing 2000 pages per minute on a single node.

Structured output called “doc-as-prompt” uses documents as prompts for powerful, clear instructions. This allows data to be extracted and formatted into structured outputs like JSON, which may be linked into function calls to develop agents.

Organisations with high data protection needs for classified or sensitive information might self-host within their own infrastructure.

Example of Mistral OCR 25.05

Use cases for Mistral OCR 25.05 include:

Digitising scientific research: Making articles and journals AI-ready for downstream intelligence engines streamlines scientific procedures.

Preservation and accessibility can be achieved by digitising historical records and artefacts.

Simplifying customer support: indexing manuals and documentation to improve satisfaction and response times.

AI literature preparation in various fields: We help businesses convert technical literature, engineering drawings, lecture notes, presentations, regulatory filings, and more into indexed, answer-ready formats to gain insights and enhance productivity across vast document volumes.

Integrating Mistral OCR 25.05 as a MaaS on Vertex AI creates a full AI platform. It provides enterprise-grade security and compliance for confident growth and fully controlled infrastructure. The Vertex AI Model Garden includes over 200 foundation models, including Mistral OCR 25.05, so customers can choose the best one for their needs. Vertex AI now offers Mistral OCR 25.05, along with Anthropic models Claude Opus 4 and Claude Sonnet 4.

To develop using Mistral OCR 25.05 on Vertex AI, users must go to the model card in the Model Garden, click “Enable,” and follow the instructions. Platform users can access the API, and Le Chat users can try Mistral OCR for free.

[advancedexcelinstitute]

A Beginner’s Guide to NVH Testing in India’s Automotive Industry

In today’s fast-paced world of data analytics, staying relevant means knowing how to turn raw data into smart decisions—and fast. Sure, tools like Python, SQL, and Power BI are gaining popularity, but if there’s one tool that still stands strong in 2025, it’s Microsoft Excel.

Whether you’re just starting out or you’ve been crunching numbers for years, Excel for data analyst roles remains one of the most practical and in-demand skills. It strikes that perfect balance between simplicity and capability, making it the go-to for countless data tasks.

In this post, we’ll look at why Excel isn’t going anywhere, the most valuable Excel job skills right now, and how you can sharpen your expertise to keep up with the latest demands in data analytics.

The Modern-Day Data Analyst: More Than Just a Number Cruncher

Back in the day, data analysts were mostly behind the scenes—collecting numbers, making charts, and maybe sending the occasional report. Fast forward to 2025, and their role is far more central. Today’s analysts are storytellers, business advisors, and problem solvers.

Here’s what a typical day might include:

Pulling raw data from different platforms (think CRMs, ERPs, databases, web analytics tools)

Cleaning and organizing that data so it actually makes sense

Analyzing trends to help forecast what’s coming next

Creating reports and dashboards that communicate findings clearly

Presenting insights to decision-makers in a way that drives action

And you guessed it—Excel shows up in almost every one of these steps.

Why Excel Still Matters (a Lot)

Some might argue that Excel is “old-school,” but here’s the reality: it’s still everywhere. And for good reason.

1. It’s Familiar to Everyone

From finance teams to marketing departments, most professionals have at least a basic grasp of Excel. That makes collaboration easy—no need to explain a tool everyone’s already using.

2. Quick Results, No Coding Required

Need to filter a dataset or run a few calculations? You can do it in Excel in minutes. It’s great for ad-hoc analysis where speed matters and there’s no time to build complex code.

3. Plays Nice with Other Tools

Excel isn’t an island. It connects smoothly with SQL databases, Google Analytics, Power BI, and even Python. Power Query is especially useful when pulling in and reshaping data from different sources.

4. It’s on Every Work Computer

You don’t need to install anything or get IT involved. Excel is ready to go on pretty much every company laptop, which makes it incredibly convenient.

Top Excel Skills Every Data Analyst Needs in 2025

To really stand out, you’ll want to move past the basics. Employers today expect you to do more than just sum a column or build a pie chart. Here’s where to focus your energy:

1. Data Cleaning and Transformation

Use functions like CLEAN(), TRIM(), and Text to Columns to fix messy data.

Power Query is a game-changer—it lets you clean, merge, and reshape large datasets without writing a line of code.

2. Advanced Formulas

Learn how to use INDEX, MATCH, XLOOKUP, IFERROR, and dynamic arrays. These help you build smarter, more flexible spreadsheets.

Nesting formulas (formulas within formulas) is super helpful for building logic into your models.

3. PivotTables and PivotCharts

Still one of the fastest ways to analyze large data sets.

Great for grouping, summarizing, and drilling into data—all without writing any SQL.

4. Power Query and Power Pivot

These tools turn Excel into a mini-BI platform.

You can pull in data from multiple tables, define relationships, and use DAX for more advanced calculations.

5. Interactive Dashboards

Combine charts, slicers, and conditional formatting to build dashboards that update as data changes.

Form controls (like drop-downs or sliders) add a professional touch.

6. Automation with Macros and VBA

Automate tasks like data formatting, report generation, and file creation.

Even basic VBA scripts can save hours each week on repetitive tasks.

Real-World Excel Use Cases That Still Matter

Let’s get practical. Here’s how Excel is still making an impact across industries:

Sales & Marketing: Track campaign performance, customer engagement, and conversion rates—all in a single dashboard.

Finance: Build cash flow models, scenario forecasts, and budget reports that help CFOs make data-driven calls.

Healthcare: Monitor key performance indicators like patient wait times or readmission rates.

Logistics: Analyze delivery times, shipping costs, and supplier performance to streamline operations.

These aren’t theoretical use cases—they’re actual day-to-day tasks being done in Excel right now.

Excel vs. Other Tools

Let’s be real: no single tool does it all. Excel fits into a broader ecosystem of data tools. Here’s a quick breakdown:TaskBest ToolHow Excel ContributesQuick AnalysisExcelFast and easy to useDashboardsPower BI / TableauExcel dashboards are perfect for internal or lightweight reportsData CleaningSQL / Power QueryExcel connects and transforms with Power QueryBig DataPython / RUse Excel for summary views and visualizations of Python output

Excel’s strength lies in how easily it fits into your workflow—even when you’re working with more advanced tools.

How to Get Better at Excel in 2025

If you’re serious about leveling up, here’s how to grow your skills:

1. Take a Course That Focuses on Analytics

Pick one that emphasizes real business problems and gives you projects to work on. Case studies are gold.

2. Practice on Real Data

Websites like Kaggle, data.gov, or even your company’s historical data (with permission, of course) are great places to start.

3. Learn Keyboard Shortcuts

You’ll work faster and feel more confident. Start with common ones like Ctrl + Shift + L for filters or Alt + = for autosum.

4. Dive into Power Query and Power Pivot

Once you get the hang of them, you’ll wonder how you ever worked without them.

5. Build Mini Projects

Create dashboards or models that solve specific business problems—like tracking customer churn or sales performance. These can become portfolio pieces for your next job interview.

Conclusion

Excel isn’t going anywhere. It’s deeply woven into how businesses run, and in 2025, it’s still one of the best tools in a data analyst’s toolkit. It might not be as flashy as Python or as powerful as Tableau, but it gets the job done—and done well.

If you’re aiming to future-proof your career, investing in advanced Excel job skills is a smart move. From dashboards to data modeling, the possibilities are endless. And when paired with other tools, Excel helps you deliver even more value to your team.

So keep practicing, keep building, and remember—being great at Excel can set you apart in the data world.

FAQs

Is Excel still worth learning for data analysis in 2025?Yes! Excel remains one of the top skills hiring managers look for in data analyst roles. It’s everywhere—from startups to large enterprises.

What are the most useful Excel features for analysts? Advanced formulas, PivotTables, Power Query, Power Pivot, and dashboard design are the big ones. Knowing VBA is a bonus.

Can Excel handle big datasets?To an extent. While Excel has limits, features like Power Query and Power Pivot help it manage more data than it could in the past. For really massive data, combine it with tools like SQL or Power BI.

Should I learn Excel or Python?Both. Excel is great for quick analysis and reporting. Python is better for automation, data science, and machine learning. Together, they’re a powerful combo.

How can I show off my Excel skills to employers? Create dashboards or reports based on real data and include them in a portfolio. Show how you used Excel to solve actual business problems on your resume.

[souhaillaghchimdev]

Big Data Analysis Application Programming

Big data is not just a buzzword—it's a powerful asset that fuels innovation, business intelligence, and automation. With the rise of digital services and IoT devices, the volume of data generated every second is immense. In this post, we’ll explore how developers can build applications that process, analyze, and extract value from big data.

What is Big Data?

Big data refers to extremely large datasets that cannot be processed or analyzed using traditional methods. These datasets exhibit the 5 V's:

Volume: Massive amounts of data

Velocity: Speed of data generation and processing

Variety: Different formats (text, images, video, etc.)

Veracity: Trustworthiness and quality of data

Value: The insights gained from analysis

Popular Big Data Technologies

Apache Hadoop: Distributed storage and processing framework

Apache Spark: Fast, in-memory big data processing engine

Kafka: Distributed event streaming platform

NoSQL Databases: MongoDB, Cassandra, HBase

Data Lakes: Amazon S3, Azure Data Lake

Big Data Programming Languages

Python: Easy syntax, great for data analysis with libraries like Pandas, PySpark

Java & Scala: Often used with Hadoop and Spark

R: Popular for statistical analysis and visualization

SQL: Used for querying large datasets

Basic PySpark Example

from pyspark.sql import SparkSession # Create Spark session spark = SparkSession.builder.appName("BigDataApp").getOrCreate() # Load dataset data = spark.read.csv("large_dataset.csv", header=True, inferSchema=True) # Basic operations data.printSchema() data.select("age", "income").show(5) data.groupBy("city").count().show()

Steps to Build a Big Data Analysis App

Define data sources (logs, sensors, APIs, files)

Choose appropriate tools (Spark, Hadoop, Kafka, etc.)

Ingest and preprocess the data (ETL pipelines)

Analyze using statistical, machine learning, or real-time methods

Visualize results via dashboards or reports

Optimize and scale infrastructure as needed

Common Use Cases

Customer behavior analytics

Fraud detection

Predictive maintenance

Real-time recommendation systems

Financial and stock market analysis

Challenges in Big Data Development

Data quality and cleaning

Scalability and performance tuning

Security and compliance (GDPR, HIPAA)

Integration with legacy systems

Cost of infrastructure (cloud or on-premise)

Best Practices

Automate data pipelines for consistency

Use cloud services (AWS EMR, GCP Dataproc) for scalability

Use partitioning and caching for faster queries

Monitor and log data processing jobs

Secure data with access control and encryption

Conclusion

Big data analysis programming is a game-changer across industries. With the right tools and techniques, developers can build scalable applications that drive innovation and strategic decisions. Whether you're processing millions of rows or building a real-time data stream, the world of big data has endless potential. Dive in and start building smart, data-driven applications today!

[aiandblockchainchronicles]

Data Matters: How to Curate and Process Information for Your Private LLM

In the era of artificial intelligence, data is the lifeblood of any large language model (LLM). Whether you are building a private LLM for business intelligence, customer service, research, or any other application, the quality and structure of the data you provide significantly influence its accuracy and performance. Unlike publicly trained models, a private LLM requires careful curation and processing of data to ensure relevance, security, and efficiency.

This blog explores the best practices for curating and processing information for your private LLM, from data collection and cleaning to structuring and fine-tuning for optimal results.

Understanding Data Curation

Importance of Data Curation

Data curation involves the selection, organization, and maintenance of data to ensure it is accurate, relevant, and useful. Poorly curated data can lead to biased, irrelevant, or even harmful responses from an LLM. Effective curation helps improve model accuracy, reduce biases, enhance relevance and domain specificity, and strengthen security and compliance with regulations.

Identifying Relevant Data Sources

The first step in data curation is sourcing high-quality information. Depending on your use case, your data sources may include:

Internal Documents: Business reports, customer interactions, support tickets, and proprietary research.

Publicly Available Data: Open-access academic papers, government databases, and reputable news sources.

Structured Databases: Financial records, CRM data, and industry-specific repositories.

Unstructured Data: Emails, social media interactions, transcripts, and chat logs.

Before integrating any dataset, assess its credibility, relevance, and potential biases.

Filtering and Cleaning Data

Once you have identified data sources, the next step is cleaning and preprocessing. Raw data can contain errors, duplicates, and irrelevant information that can degrade model performance. Key cleaning steps include removing duplicates to ensure unique entries, correcting errors such as typos and incorrect formatting, handling missing data through interpolation techniques or removal, and eliminating noise such as spam, ads, and irrelevant content.

Data Structuring for LLM Training

Formatting and Tokenization

Data fed into an LLM should be in a structured format. This includes standardizing text formats by converting different document formats (PDFs, Word files, CSVs) into machine-readable text, tokenization to break down text into smaller units (words, subwords, or characters) for easier processing, and normalization by lowercasing text, removing special characters, and converting numbers and dates into standardized formats.

Labeling and Annotating Data

For supervised fine-tuning, labeled data is crucial. This involves categorizing text with metadata, such as entity recognition (identifying names, locations, dates), sentiment analysis (classifying text as positive, negative, or neutral), topic tagging (assigning categories based on content themes), and intent classification (recognizing user intent in chatbot applications). Annotation tools like Prodigy, Labelbox, or Doccano can facilitate this process.

Structuring Large Datasets

To improve retrieval and model efficiency, data should be stored in a structured format such as vector databases (using embeddings and vector search for fast retrieval like Pinecone, FAISS, Weaviate), relational databases (storing structured data in SQL-based systems), or NoSQL databases (storing semi-structured data like MongoDB, Elasticsearch). Using a hybrid approach can help balance flexibility and speed for different query types.

Processing Data for Model Training

Preprocessing Techniques

Before feeding data into an LLM, preprocessing is essential to ensure consistency and efficiency. This includes data augmentation (expanding datasets using paraphrasing, back-translation, and synthetic data generation), stopword removal (eliminating common but uninformative words like "the," "is"), stemming and lemmatization (reducing words to their base forms like "running" → "run"), and encoding and embedding (transforming text into numerical representations for model ingestion).

Splitting Data for Training

For effective training, data should be split into a training set (80%) used for model learning, a validation set (10%) used for tuning hyperparameters, and a test set (10%) used for final evaluation. Proper splitting ensures that the model generalizes well without overfitting.

Handling Bias and Ethical Considerations

Bias in training data can lead to unfair or inaccurate model predictions. To mitigate bias, ensure diverse data sources that provide a variety of perspectives and demographics, use bias detection tools such as IBM AI Fairness 360, and integrate human-in-the-loop review to manually assess model outputs for biases. Ethical AI principles should guide dataset selection and model training.

Fine-Tuning and Evaluating the Model

Transfer Learning and Fine-Tuning

Rather than training from scratch, private LLMs are often fine-tuned on top of pre-trained models (e.g., GPT, Llama, Mistral). Fine-tuning involves selecting a base model that aligns with your needs, using domain-specific data to specialize the model, and training with hyperparameter optimization by tweaking learning rates, batch sizes, and dropout rates.

Model Evaluation Metrics

Once the model is trained, its performance must be evaluated using metrics such as perplexity (measuring how well the model predicts the next word), BLEU/ROUGE scores (evaluating text generation quality), and human evaluation (assessing outputs for coherence, factual accuracy, and relevance). Continuous iteration and improvement are crucial for maintaining model quality.

Deployment and Maintenance

Deploying the Model

Once the LLM is fine-tuned, deployment considerations include choosing between cloud vs. on-premise hosting depending on data sensitivity, ensuring scalability to handle query loads, and integrating the LLM into applications via REST or GraphQL APIs.

Monitoring and Updating

Ongoing maintenance is necessary to keep the model effective. This includes continuous learning by regularly updating with new data, model drift detection to identify and correct performance degradation, and user feedback integration to use feedback loops to refine responses. A proactive approach to monitoring ensures sustained accuracy and reliability.

Conclusion

Curating and processing information for a private LLM is a meticulous yet rewarding endeavor. By carefully selecting, cleaning, structuring, and fine-tuning data, you can build a robust and efficient AI system tailored to your needs. Whether for business intelligence, customer support, or research, a well-trained private LLM can offer unparalleled insights and automation, transforming the way you interact with data.

Invest in quality data, and your model will yield quality results.

[harmonyos-next]

What is HarmonyOS NEXT - RelationalStore?

Relational databases provide a universal operational interface for applications, with SQLite as the persistent storage engine at the underlying level, supporting the database features of SQLite, including but not limited to transactions, indexes, views, triggers, foreign keys, parameterized queries, and precompiled SQL statements.

Applicable scenarios: In scenarios where complex relational data is stored, such as the student information of a class, which needs to include names, student IDs, subject grades, etc., or the employee information of a company, which needs to include names, job IDs, positions, etc. Due to the strong correspondence between data, the complexity is higher than that of key value data. In this case, a relational database needs to be used to persistently store the data.

constraint qualification ·The default logging mode of the system is WAL (Write Ahead Log) mode, and the default disk dropping mode is FULL mode. ·There are 4 read connections and 1 write connection in the database. When a thread obtains a free read connection, it can perform a read operation. When there is no free read connection and there is a free write connection, the write connection will be used as a read connection. ·To ensure data accuracy, the database can only support one write operation at a time. ·After the application is uninstalled, the relevant database files and temporary files on the device will be automatically cleared. ·Basic data types supported by ArkTS side: number、string、 Binary type data boolean。 ·To ensure successful insertion and reading of data, it is recommended that one piece of data should not exceed 2M. Exceeding this size, insertion successful, read failed.

Basic concepts: ·Predicate: A term used in a database to represent the properties, characteristics, or relationships between data entities, primarily used to define the operating conditions of the database. ·Result set: refers to the set of results obtained by the user after querying, which can be accessed for data. The result set provides a flexible way of accessing data, making it easier for users to obtain the data they want.

code example SQLiteUtil [code] export default class SQLiteUtil { static getCreateTableSql(tableName: string, columns: ColumnInfo[]): string { let sql = CREATE TABLE IF NOT EXISTS ${tableName} (; columns.forEach((element, index) => { if (index == 0) { //Splicing the first element, default as primary key sql += ${element.name} ${DataType[element.type]} PRIMARY KEY AUTOINCREMENT,; } else if (index == columns.length - 1) { //Last element concatenation statement sql += ${element.name} ${DataType[element.type]} NOT NULL);; } else { sql += ${element.name} ${DataType[element.type]} NOT NULL,; } }); return sql; } }

export interface ColumnInfo { name: string; type: DataType; }

export enum DataType { NULL = 'NULL', INTEGER = 'INTEGER', REAL = 'REAL', TEXT = 'TEXT', BLOB = 'BLOB' } [/code] RelationalStoreService [code] import SQLiteUtil, { ColumnInfo, DataType } from '../ChicKit/data/SQLiteUtil' import relationalStore from '@ohos.data.relationalStore' import { common } from '@kit.AbilityKit'; import Logger from '../utils/Logger'; import AppError from '../models/AppError'; import Schedule from '../entities/Schedule'; import { BusinessError } from '@kit.BasicServicesKit'; import { ValuesBucket, ValueType } from '@ohos.data.ValuesBucket'; import { DataModel } from '../ChicKit/data/DataModel'; import Target from '../entities/Target'; import Plan from '../entities/Plan';

const RelationalStoreName = 'shijianxu.db'

export default class RelationalStoreService { static rdbStore: relationalStore.RdbStore;

/**

Initialize relational database

@param context */ static init(context: common.UIAbilityContext) { // RelationalStore configuration let storeConfig: relationalStore.StoreConfig = { // Database file name name: RelationalStoreName, //security level securityLevel: relationalStore.SecurityLevel.S1 } relationalStore.getRdbStore(context, storeConfig, (err, store) => { if (err) { Logger.error(RelationalStoreService init error, error=${JSON.stringify(new AppError(err))}) return; } else { RelationalStoreService.rdbStore = store RelationalStoreService.createScheduleTable() RelationalStoreService.createTargetTable() RelationalStoreService.createPlanTable() } }); } /**

Create schedule table */ static createScheduleTable() { //Table Fields const columns: ColumnInfo[] = Schedule.getColumns() // Retrieve the SQL statement for creating a table const sql = SQLiteUtil.getCreateTableSql(Schedule.TableName, columns) // Create Data Table RelationalStoreService.rdbStore.executeSql(sql, (err) => { if (err) { Logger.error(RelationalStoreService createScheduleTable error, error=${JSON.stringify(new AppError(err))}) return; } }); } /**

Create target table */ static createTargetTable() { //表字段 const columns: ColumnInfo[] = Target.getColumns() // 获取创建表SQL语句 const sql = SQLiteUtil.getCreateTableSql(Target.TableName, columns) // 创建数据表 RelationalStoreService.rdbStore.executeSql(sql, (err) => { if (err) { Logger.error(RelationalStoreService createTargetTable error, error=${JSON.stringify(new AppError(err))}) return; } }); } /**

Create plan table */ static createPlanTable() { //表字段 const columns: ColumnInfo[] = Plan.getColumns() // 获取创建表SQL语句 const sql = SQLiteUtil.getCreateTableSql(Plan.TableName, columns) // 创建数据表 RelationalStoreService.rdbStore.executeSql(sql, (err) => { if (err) { Logger.error(RelationalStoreService createPlanTable error, error=${JSON.stringify(new AppError(err))}) return; } }); } /**

insert data

@param tableName

@param values */ static insert(tableName: string, values: ValuesBucket) { RelationalStoreService.rdbStore.insert(tableName, values, (err: BusinessError, rowId: number) => { if (err) { Logger.error(RelationalStoreService insert error, error=${JSON.stringify(new AppError(err))}) return; } else { return rowId } }) } /**

delete

@param predicates

@returns delete count */ static delete(predicates: relationalStore.RdbPredicates):number{ return RelationalStoreService.rdbStore.deleteSync(predicates) } /**

update

@param values

@param predicates

@returns update count */ static update(values: ValuesBucket,predicates: relationalStore.RdbPredicates):number{ let rows: number = RelationalStoreService.rdbStore.updateSync(values, predicates, relationalStore.ConflictResolution.ON_CONFLICT_REPLACE); return rows } static querySync(predicates: relationalStore.RdbPredicates, columns: ColumnInfo[]): DataModel[] { let dataList: DataModel[] = [] try { let columnsStringArray: string[] = [] columns.forEach(element => { columnsStringArray.push(element.name) }); const resultSet = RelationalStoreService.rdbStore.querySync(predicates, columnsStringArray) resultSet.columnNames // resultSet.getColumnName('') // resultSet.getValue() //循环处理结果,循环条件：当所在行不是最后一行 while (!resultSet.isAtLastRow) { //去往下一行 resultSet.goToNextRow() let schedule: DataModel = {} columns.forEach(element => { switch (element.type) { case DataType.INTEGER: schedule[element.name] = resultSet.getLong(resultSet.getColumnIndex(element.name)) break; case DataType.REAL: schedule[element.name] = resultSet.getDouble(resultSet.getColumnIndex(element.name)) break; case DataType.TEXT: schedule[element.name] = resultSet.getString(resultSet.getColumnIndex(element.name)) break; case DataType.BLOB: schedule[element.name] = resultSet.getBlob(resultSet.getColumnIndex(element.name)) break; } }) dataList.push(schedule) } } catch (err) { Logger.error(RelationalStoreService querySync error, error=${JSON.stringify(new AppError(err))}) } return dataList } } [/code]

[arobasetechnologies]

How to Develop a Website Using PHP

How to Develop a Website Using PHP

In today's digital era, websites play a crucial role in business growth and online presence. One of the most widely used server-side scripting languages for web development is PHP (Hypertext Preprocessor). It is open-source, easy to learn, and widely supported by web servers and databases. This guide will walk you through the step-by-step process of developing a website using PHP.

Why Choose PHP for Web Development?

Before diving into the development process, let’s explore some key reasons why PHP is a great choice for website development:

1. Open-Source: PHP is free to use, making it cost-effective for developers.

2. Cross-Platform Compatibility: Runs on Windows, Linux, and macOS without compatibility issues.

3. Database Support: Easily integrates with MySQL, PostgreSQL, and other databases.

4. Scalability: Suitable for both small websites and large-scale web applications.

5. Large Community Support: Extensive documentation and active developer communities for troubleshooting and learning.

Prerequisites for PHP Web Development

To develop a website using PHP, you need the following tools:

1. Text Editor or IDE: VS Code, Sublime Text, or PHPStorm.

2. Local Server: XAMPP, WAMP, or MAMP for running PHP scripts.

3. Database System: MySQL or PostgreSQL for data storage.

4. Web Browser: Chrome, Firefox, or Edge for testing the website.

Step-by-Step Guide to Developing a Website Using PHP

1. Setting Up Your Development Environment

To begin developing a PHP website, follow these steps:

· Install XAMPP (or WAMP/MAMP) to create a local server.

· Using the XAMPP Control Panel, launch Apache and MySQL.

· Navigate to htdocs in the XAMPP directory to store PHP project files.

2. Creating the Project Structure

Organizing your files properly enhances maintainability. A typical PHP project structure:

project-folder/

│-- index.php

│-- config.php

│-- assets/

│   ├── css/

│   ├── js/

│   ├── images/

│-- includes/

│   ├── header.php

│   ├── footer.php

│-- pages/

│   ├── about.php

│   ├── contact.php

│-- database/

│   ├── db_connect.php

3. Writing Your First PHP Script

Create an index.php file and add the following code:

<?php

  echo "Welcome to My PHP Website!";

?>

Save the file and access it in the browser by navigating to http://localhost/project-folder/.

4. Connecting PHP with MySQL Database

To manage dynamic content, connect PHP with a MySQL database.

Create a Database

1. Open phpMyAdmin from XAMPP.

2. Create a new database, e.g., my_website.

3. Add a users table with fields id, name, email, and password.

Database Connection Code (db_connect.php)

<?php

$servername = "localhost";

$username = "root";

$password = "";

dbname = "my_website";

$conn = new mysqli($servername, $username, $password, $dbname);

if ($conn->connect_error) {

    die("Connection failed: " . $conn->connect_error);

}

?>

5. Creating a User Registration System

A simple user registration form using PHP and MySQL.

Registration Form (register.php)

<form method="POST" action="register.php">

  <input type="text" name="name" placeholder="Full Name" required>

  <input type="email" name="email" placeholder="Email" required>

  <input type="password" name="password" placeholder="Password" required>

  <button type="submit" name="register">Register</button>

</form>

Handling User Registration (register.php)

<?php

include 'database/db_connect.php';

if(isset($_POST['register'])) {

    $name = $_POST['name'];

    $email = $_POST['email'];

    $password = password_hash($_POST['password'], PASSWORD_BCRYPT);

    $sql = "INSERT INTO users (name, email, password) VALUES ('$name', '$email', '$password')";

    if ($conn->query($sql) === TRUE) {

        echo "Registration successful!";

    } else {

        echo "Error: " . $conn->error;

    }

}

?>

6. Implementing User Login System

Login Form (login.php)

<form method="POST" action="login.php">

  <input type="email" name="email" placeholder="Email" required>

  <input type="password" name="password" placeholder="Password" required>

  <button type="submit" name="login">Login</button>

</form>

Handling Login Authentication (login.php)

<?php

session_start();

include 'database/db_connect.php';

if(isset($_POST['login'])) {

    $email = $_POST['email'];

    $password = $_POST['password'];

    $result = $conn->query("SELECT * FROM users WHERE email='$email'");

    $user = $result->fetch_assoc();

    if(password_verify($password, $user['password'])) {

        $_SESSION['user'] = $user;

        echo "Login successful!";

    } else {

        echo "Invalid credentials!";

    }

}

?>

7. Adding Navigation and Styling

· Use Bootstrap or CSS frameworks to improve UI.

· Include a header.php and footer.php for better navigation.

8. Deploying the PHP Website

Once development is complete, deploy your PHP website using:

· Shared Hosting with cPanel for easy management.

· Cloud Hosting (AWS, DigitalOcean) for high performance.

· Domain & SSL Certificate for a secure and professional website.

Conclusion

Developing a website using PHP is an efficient way to create dynamic and interactive websites. By following this step-by-step guide, you can build a PHP-based website from scratch, implement database interactions, user authentication, and deploy your project successfully. Start your PHP development journey today and create powerful web applications!

[helicalinsight]

Firebird to PostgreSQL Migration

In this article, we dive deep into the process of Firebird to PostgreSQL Migration, exploring the key steps and considerations to ensure a seamless transition. We cover essential topics such as data compatibility, schema conversion, and handling differences in SQL syntax between the two databases. With a focus on best practices and tools that streamline the migration process, this guide will help organizations move their data efficiently while minimizing downtime. Additionally, we discuss common challenges and provide solutions to overcome them, ensuring a smooth and successful migration experience.

What is Firebird

Firebird is an open-source relational database management system (RDBMS) known for its lightweight architecture, high performance, and scalability. It supports SQL, transactions, and multi-version concurrency control (MVCC), making it ideal for embedded applications and small to medium-sized projects. Firebird is compatible with a variety of platforms, including Windows, Linux, and macOS. Its flexibility allows developers to create efficient, low-cost solutions without sacrificing reliability or speed. Despite its smaller footprint compared to larger databases, Firebird offers powerful features, such as stored procedures and triggers, making it a versatile choice for many use cases.

What is PostgreSQL

PostgreSQL is an advanced, open-source relational database management system (RDBMS) known for its robustness, flexibility, and support for complex queries. Widely admired for its strong adherence to SQL standards, PostgreSQL goes beyond basic functionality, offering powerful features like multi-version concurrency control (MVCC), extensibility, and support for unstructured data types. It's a go-to choice for developers and organizations handling large-scale, high-performance applications. Whether you're building a web app, managing big data, or running analytics workloads, PostgreSQL is a trusted platform for its reliability, security, and active community of contributors, making it a leader in the world of open-source databases.

Advantages of Firebird to PostgreSQL Migration

Improved Performance: PostgreSQL offers better performance with support for larger datasets and complex queries.

Advanced Features: PostgreSQL provides advanced features like JSONB support, full-text search, and foreign data wrappers.

Scalability: PostgreSQL scales efficiently to handle high traffic applications and large volumes of data.

Stronger Security: PostgreSQL has robust security mechanisms, including encryption and fine-grained access control.

Active Community Support: PostgreSQL has a large, active community that ensures regular updates and bug fixes.

Cross-Platform Compatibility: PostgreSQL supports a wide range of operating systems, including Windows, Linux, and macOS.

SQL Compliance: PostgreSQL adheres strictly to SQL standards, reducing potential issues during migration.

Extensibility: With support for custom extensions, PostgreSQL allows businesses to tailor the database to their specific needs.

Better Documentation: PostgreSQL provides extensive, well-organized documentation, easing development and troubleshooting.

Method 1: Migrating Data from Firebird to PostgreSQL Using the Manual Method

Schema Analysis and Preparation: Analyze the Firebird schema, identifying tables, views, and stored procedures to be migrated, and prepare a corresponding schema in PostgreSQL.

Data Export: Use Firebird’s gbak utility or export data to CSV files for easy import into PostgreSQL.

Schema Creation in PostgreSQL: Manually create the PostgreSQL schema, mapping tables, data types, and relationships from Firebird to PostgreSQL.

Data Import: Use PostgreSQL’s COPY command or pgAdmin to import the data from the exported CSV files.

Stored Procedures and Application Updates: Convert Firebird-specific stored procedures to PostgreSQL’s PL/pgSQL, and update applications to connect to the new PostgreSQL database.

Testing and Optimization: Test the migration thoroughly, ensuring data integrity and application functionality, then optimize performance for PostgreSQL.

Disadvantages of Migrating Data from Firebird to PostgreSQL Using the Manual Method

High Error Risk: Manual migration increases the likelihood of human errors, affecting data integrity and process accuracy.

Complex Data Transformation: Achieving complex data transformations without automation can lead to inconsistencies and errors.

Dependency on Technical Resources: Skilled resources are essential, making the process time-consuming and costly.

Lack of Automation: The entire process requires manual effort, from data extraction to schema conversion, which is inefficient.

Limited Scalability: Manual methods become increasingly difficult to scale as data volume and complexity grow.

No Automated Error Handling or Notifications: Without automation, errors go undetected, causing potential delays or data loss.

No Automated Rollback Mechanism: In the event of a failure, there’s no automatic rollback, complicating recovery and increasing downtime.

Lack of Monitoring Tools: Without automated logs, tracking data transfer progress and ensuring completeness is challenging.

Inability to Handle Incremental Loads: Manual migration lacks automated methods for incremental data loading, complicating ongoing synchronization.

Method 2: Migrating Data from Firebird to PostgreSQL Using ETL Tools

There are certain advantages in case if you use an ETL tool to migrate the data

Automation of Data Migration: ETL tools automate the entire migration process, reducing human error and saving time.

Simplified Schema Mapping: Built-in connectors and functions handle schema differences between Firebird and PostgreSQL seamlessly.

Incremental Data Loads: ETL tools allow for scheduled data loads and incremental updates to keep migration continuous and up-to-date.

Real-Time Monitoring: ETL tools provide dashboards to track progress and alert users to any migration issues.

Scalability: ETL tools scale easily to handle large data volumes, ensuring consistent performance without manual intervention.

Inbuilt Error Handling and Notifications: These tools include automated error handling, logging, and email notifications for efficient issue resolution.

Comprehensive Monitoring and Analytics: ETL tools monitor key stats like data transfer volumes, data quality (DQ), and change data capture (CDC).

Need to do this activity again and again for every table.

Challenges of Using ETL Tools for Data Migration

Complex Setup: ETL tools require intricate configuration, making the initial setup time-consuming and technically demanding.

Data Transformation Issues: Handling complex data transformations and schema differences can be difficult and often requires custom scripting.

Performance Limitations: ETL tools may struggle with large datasets or high-velocity data streams, leading to slower performance.

Cost Considerations: Many ETL tools come with high licensing costs, which can strain budgets, especially for smaller organizations.

Dependency on Third-Party Tools: ETL tools often depend on external services, creating risks around compatibility and vendor support during migration.

Why Ask On Data is the Best Tool for Migrating Data from Firebird to Oracle

User-Friendly Interface: Ask On Data offers an intuitive interface that simplifies the migration process, making it easy for users of all skill levels.

Seamless Integration: The tool connects smoothly with both Firebird and Oracle, ensuring a hassle-free data transfer without complicated setups.

Automated Data Transformation: It automatically transforms and cleans your data, reducing the risk of errors and saving you time during migration.

Real-Time Monitoring: Ask On Data provides real-time monitoring of the migration process, allowing you to track progress and quickly address any issues.

Cost-Effective Solution: With a flexible pricing model, Ask On Data helps you manage migration costs without sacrificing quality or performance.

Usage of Ask On Data : A chat based AI powered Data Engineering Tool

Ask On Data is world’s first chat based AI powered data engineering tool. It is present as a free open source version as well as paid version. In free open source version, you can download from Github and deploy on your own servers, whereas with enterprise version, you can use Ask On Data as a managed service.

Advantages of using Ask On Data

Built using advanced AI and LLM, hence there is no learning curve.

Simply type and you can do the required transformations like cleaning, wrangling, transformations and loading

No dependence on technical resources

Super fast to implement (at the speed of typing)

No technical knowledge required to use

Below are the steps to do the data migration activity

Step 1: Connect to Firebird(which acts as source)

Step 2 : Connect to PostgreSQL (which acts as target)

Step 3: Create a new job. Select your source (Firebird) and select which all tables you would like to migrate.

Step 4 (OPTIONAL): If you would like to do any other tasks like data type conversion, data cleaning, transformations, calculations those also you can instruct to do in natural English. NO knowledge of SQL or python or spark etc required.

Step 5: Orchestrate/schedule this. While scheduling you can run it as one time load, or change data capture or truncate and load etc.

For more advanced users, Ask On Data is also providing options to write SQL, edit YAML, write PySpark code etc.

There are other functionalities like error logging, notifications, monitoring, logs etc which can provide more information like the amount of data transferred, logs, any error information if the job did not run and other kind of monitoring information etc.

Trying Ask On Data

You can reach out to us on mailto:support@askondata.com for a demo, POC, discussion and further pricing information. You can make use of our managed services or you can also download and install on your own servers our community edition from Github.

[techiegigs]

How to Move Your WordPress Site from Localhost to a Live Server

Developing a WordPress site on localhost is a great way to build and test your website in a controlled environment. However, the real challenge arises when it's time to move the site from your local server to a live hosting environment. If not done correctly, you could encounter broken links, missing images, or even database errors.

In this blog, we'll guide you through a step-by-step process to successfully move your WordPress site from localhost to a live server.

Step 1: Choose the Right Hosting Provider

Your first step is to select a reliable web hosting provider that meets your website’s needs. Look for:

Server Speed: Fast servers for better performance.

Uptime Guarantee: At least 99.9% uptime to ensure availability.

Ease of Use: User-friendly dashboards and tools.

WordPress Support: Hosting optimized for WordPress websites.

Popular options include Bluehost, SiteGround, and WP Engine.

Step 2: Export Your Local WordPress Database

The database is the backbone of your WordPress site. To export it:

Open phpMyAdmin on your local server (e.g., XAMPP or WAMP).

Select your WordPress database.

Click on the Export tab and choose the Quick Export method.

Save the .sql file to your computer.

Step 3: Upload Your WordPress Files to the Live Server

To move your files:

Compress Your WordPress Folder: Zip your local WordPress installation folder.

Access Your Hosting Account: Use a file manager or an FTP client like FileZilla.

Upload the Files: Transfer the zipped folder to your hosting server's root directory (usually public_html).

Unzip the Folder: Extract the files once uploaded.

Step 4: Create a Database on the Live Server

Now, set up a new database on your live hosting server:

Log in to your hosting control panel (e.g., cPanel).

Navigate to the MySQL Databases section.

Create a new database, database user, and password.

Assign the user to the database with full privileges.

Step 5: Import the Database to the Live Server

Open phpMyAdmin in your hosting control panel.

Select the new database you created.

Click the Import tab.

Choose the .sql file you exported from your localhost.

Click Go to import the database.

Step 6: Update the wp-config.php File

To connect your site to the live database:

Locate the wp-config.php file in your WordPress installation.

Open the file in a text editor.

Update the following lines: define('DB_NAME', 'your_live_database_name'); define('DB_USER', 'your_live_database_user'); define('DB_PASSWORD', 'your_live_database_password'); define('DB_HOST', 'localhost'); // Keep this unless your host specifies otherwise.

Save the file and upload it to your server via FTP.

Step 7: Update URLs in the Database

Your localhost URLs need to be replaced with your live site URLs.

Use a tool like Search Replace DB or run SQL queries in phpMyAdmin.

In phpMyAdmin, run the following query: UPDATE wp_options SET option_value = 'http://your-live-site.com' WHERE option_name = 'siteurl'; UPDATE wp_options SET option_value = 'http://your-live-site.com' WHERE option_name = 'home';

Step 8: Test Your Live Website

Once everything is uploaded and configured, check your website by entering its URL in a browser. Test for:

Broken Links: Fix them using plugins like Broken Link Checker.

Missing Images: Ensure media files were uploaded correctly.

Functionality: Verify forms, buttons, and features work as expected.

Step 9: Set Up Permalinks

To ensure proper URL structure:

Log in to your WordPress admin dashboard on the live site.

Go to Settings > Permalinks.

Choose your preferred permalink structure and click Save Changes.

Step 10: Secure Your Live Website

After migrating, secure your site to prevent vulnerabilities:

Install an SSL Certificate: Most hosting providers offer free SSL certificates.

Update Plugins and Themes: Ensure everything is up to date.

Set Up Backups: Use plugins like UpdraftPlus for regular backups.

Conclusion

Moving your WordPress site from localhost to a live server may seem daunting, but by following these steps, you can ensure a smooth and error-free migration. A successful move allows you to showcase your site to the world, engage your audience, and achieve your goals.

Start today and take your WordPress project live with confidence!

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Power BI Training | Power BI Online Training

Power BI Training: What Types of Data Can Power BI Handle?

Power BI Training equips individuals and businesses with the skills to transform raw data into actionable insights. One of the primary reasons Power BI has gained immense popularity in recent years is its unparalleled ability to handle a wide variety of data types. Whether you’re analyzing financial reports, visualizing customer trends, or integrating cloud data sources, Power BI is a versatile tool that supports diverse data formats and systems. With the growing demand for Power BI Online Training, users can now easily master the techniques to utilize its capabilities and integrate data from multiple sources seamlessly.

In this article, we will explore the types of data that Power BI can handle and highlight how the tool empowers users to extract value from various data streams. From relational databases to real-time cloud data, understanding Power BI's compatibility with different data formats is essential for both beginners and advanced users.

Relational Databases: The Foundation of Business Insights

Relational databases are among the most commonly used data sources in businesses. Power BI connects seamlessly with relational databases such as SQL Server, MySQL, Oracle Database, and PostgreSQL. These databases store structured data in tables, making them ideal for generating reports and dashboards. Power BI Training emphasizes leveraging these connections to perform efficient data modeling, create calculated columns, and design measures using DAX (Data Analysis Expressions).

By utilizing Power BI's built-in data connectors, users can import large volumes of structured data without compromising performance. Whether it’s sales data, inventory records, or employee metrics, Power BI empowers users to uncover insights through its interactive dashboards.

Excel and CSV Files: Simplicity Meets Functionality

Power BI is widely known for its compatibility with Excel and CSV files, making it a favourite tool among professionals who work with these formats. Many organizations rely on spreadsheets for budgeting, forecasting, and reporting. Power BI allows users to directly import and transform this data into visually appealing reports.

With Power BI Online Training, users can also learn how to automate the refresh of data from Excel files stored on One Drive or SharePoint. This ensures that reports and dashboards remain up-to-date without manual intervention. Its drag-and-drop interface simplifies the process of merging multiple datasets, enabling users to create comprehensive views of their business operations.

Cloud Data Sources: Real-Time Data at Your Fingertips

As businesses transition to cloud-based platforms, the ability to analyze real-time data has become critical. Power BI supports a wide range of cloud-based data sources, including Azure SQL Database, Google BigQuery, Amazon Redshift, and Salesforce. This capability allows users to connect to live datasets and monitor key performance indicators (KPIs) in real time.

Power BI Training often covers best practices for working with cloud data, including configuring gateways and optimizing queries for faster performance. This is particularly useful for businesses that rely on SaaS (Software as a Service) applications for their day-to-day operations. Real-time analytics in Power BI can drive immediate decision-making, helping businesses stay competitive in dynamic markets.

Unstructured Data: Making Sense of Complexity

While structured data is easy to analyze, unstructured data presents a unique challenge. Unstructured data includes formats like text files, PDFs, social media content, and multimedia files. With the help of tools like Power BI and its integration with Azure Cognitive Services, users can analyze and visualize unstructured data effectively.

For example, sentiment analysis can be performed on customer reviews or social media posts, providing businesses with valuable insights into consumer behaviour. Advanced Power BI Online Training delves into techniques for handling unstructured data, including natural language processing (NLP) and custom visualizations.

Big Data: Harnessing Volume, Variety, and Velocity

In the era of big data, organizations must process large datasets that often come from diverse sources. Power BI integrates seamlessly with big data platforms like Hadoop, Apache Spark, and Azure Data Lake. These integrations enable users to analyze massive datasets without requiring extensive technical expertise.

During Power BI Training, participants learn how to use DirectQuery and import modes to optimize big data workflows. DirectQuery allows users to query large datasets without importing them into Power BI, which is particularly useful for businesses dealing with high-volume data streams. These capabilities make Power BI an essential tool for industries such as finance, healthcare, and retail.

APIs and Custom Data Sources: Flexibility Redefined

Power BI's ability to connect with APIs (Application Programming Interfaces) opens up a world of possibilities for developers and data analysts. APIs allow Power BI to pull data from custom sources, including proprietary systems and third-party applications. By configuring API connections, users can create custom dashboards tailored to their specific needs.

Through Power BI Online Training, users can learn how to set up and manage these custom integrations. This feature is particularly beneficial for organizations that rely on niche software solutions for their operations. With Power BI, even the most complex data sources can be visualized and shared across teams.

IoT Data: Driving Innovation with Real-Time Insights

The Internet of Things (IoT) has revolutionized industries by providing real-time data from sensors, devices, and machines. Power BI supports IoT data integration through Azure IoT Hub, enabling users to monitor equipment performance, track energy consumption, or analyze logistics operations in real time.

Participants in Power BI Training gain hands-on experience in working with IoT data, learning how to build dynamic dashboards that capture and visualize live data streams. These capabilities are critical for industries like manufacturing, transportation, and energy, where timely insights can prevent downtime and improve efficiency.

Conclusion

Power BI's versatility lies in its ability to handle a wide range of data types, from structured and unstructured datasets to real-time IoT and big data sources. By integrating with relational databases, spreadsheets, cloud services, and APIs, Power BI has established itself as a comprehensive tool for data analysis and visualization.

For professionals and organizations looking to harness the full potential of this tool, Power BI Training provides the foundation needed to master its features. Whether you're a beginner or an experienced analyst, Power BI Online Training ensures that you can work efficiently with various data formats and create impactful reports. With Power BI, the possibilities for data-driven decision-making are virtually limitless.

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Ms Office

Microsoft Office: A Comprehensive Overview

Introduction

Microsoft Office, often referred to as MS Office, is a suite of productivity tools developed by Microsoft Corporation. First released in 1990, it has become an industry standard for office applications, with tools widely used in businesses, education, and homes worldwide. It includes various software designed to facilitate document creation, data analysis, presentations, communication, and other professional tasks. Over the years, Microsoft has continuously upgraded Office to meet modern requirements by adding cloud capabilities, collaborative tools, and enhanced security.

Core Components of MS Office

The suite offers a collection of applications, each serving a specific function:

Microsoft Word MS Word is a word processor used to create, edit, and format text documents. It supports advanced features such as tables, templates, mail merge, and collaborative editing, making it suitable for both personal and professional writing, from letters to research papers.

Microsoft Excel Excel is a powerful spreadsheet program primarily used for data analysis and financial modeling. It provides tools for calculations, graph creation, pivot tables, and conditional formatting. Excel also supports Visual Basic for Applications (VBA), a programming language that automates complex tasks.

Microsoft PowerPoint PowerPoint is a presentation software used to create slideshows with text, images, animations, and multimedia. It's a popular tool for business meetings, lectures, and marketing presentations, offering templates and design suggestions to enhance visuals.

Microsoft Outlook Outlook is an email client and personal information manager that combines email, calendar, task management, and contact management features. It is a critical tool for professional communication, especially in corporate environments.

Microsoft Access Access is a database management system used to store, organize, and retrieve data. It is often used for creating small to medium-sized databases and supports SQL queries, forms, and reports.

Microsoft OneNote OneNote is a digital note-taking application that allows users to capture and organize notes, drawings, web clippings, and more. It supports cloud synchronization, enabling users to access notes across devices.

Microsoft Teams Teams is a collaboration platform integrated with Office 365, offering chat, video conferencing, and file sharing. It has become essential in remote work environments, providing seamless integration with other MS Office apps.

Evolution to Microsoft 365

In recent years, Microsoft rebranded its subscription service from “Office 365” to Microsoft 365. This change reflects the broader scope of services beyond traditional office software. Microsoft 365 offers cloud-based versions of all core Office apps, along with additional tools like OneDrive (for cloud storage), SharePoint (for collaboration), and enterprise-level security features.

The cloud-based model allows for continuous updates, enabling users to access the latest features without reinstalling or manually updating software. It also facilitates real-time collaboration, allowing multiple users to edit documents simultaneously, regardless of location.

Editions and Availability

Microsoft Office is available in various editions tailored to specific audiences:

Home & Student: Basic apps like Word, Excel, and PowerPoint.

Business & Enterprise: Includes advanced tools like Outlook, Access, and Teams, along with cloud storage and collaboration services.

Education: Offers discounts or free access to students and educators through institutional partnerships.

The software can be installed on Windows, macOS, and mobile platforms like Android and iOS, ensuring cross-platform compatibility.

Key Features and Benefits

Productivity: MS Office helps users efficiently manage tasks, whether it’s creating reports in Word, analyzing data in Excel, or designing presentations in PowerPoint.

Collaboration: With Microsoft 365, multiple users can work on the same document in real time, promoting teamwork.

Integration: Office apps integrate seamlessly, e.g., embedding Excel charts into Word or scheduling meetings through Outlook and Teams.

Cloud Sync: Microsoft 365’s cloud capabilities ensure that files are accessible from any device with an internet connection.

Security: Microsoft prioritizes data security with advanced encryption and multi-factor authentication to protect user information.

Competitors

While MS Office is dominant, it faces competition from alternatives like Google Workspace (Docs, Sheets, Slides), Apple’s iWork suite, and open-source options such as LibreOffice. However, Microsoft’s strong legacy and feature-rich tools give it a competitive edge, particularly in business environments.

Conclusion

Microsoft Office remains a cornerstone of productivity software, evolving over the decades to meet changing demands in both personal and professional contexts. Its applications are renowned for their usability, versatility, and integration. As businesses and individuals increasingly move towards cloud-based solutions, Microsoft’s pivot to Microsoft 365 ensures that Office will continue to be relevant and indispensable in the future. Whether for students, professionals, or enterprises, MS Office offers tools that empower users to communicate, collaborate, and create with ease.

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Microsoft Excel: A Powerful Tool for Data Management

Introduction

Microsoft Excel is a spreadsheet software developed by Microsoft and included in the Microsoft Office suite. Since its release in 1985, Excel has become an essential tool for businesses, academia, and individuals worldwide for managing, analyzing, and visualizing data. Excel is renowned for its flexibility, allowing users to perform everything from simple calculations to complex data modeling. Its intuitive interface, combined with advanced features like formulas, pivot tables, and automation, makes it suitable for various tasks, from budgeting to statistical analysis.

Key Features of Excel

Spreadsheets and Worksheets Excel files are called workbooks, and each workbook contains multiple worksheets (or tabs). Worksheets consist of a grid of cells arranged in rows and columns, where users input and manipulate data. Cells are identified by a combination of column letters (e.g., A, B) and row numbers (e.g., 1, 2), making it easy to reference data.

Formulas and Functions One of Excel’s most powerful features is its formula engine, which allows users to perform arithmetic operations and apply functions. Excel offers built-in functions to handle tasks ranging from basic calculations (e.g., SUM, AVERAGE) to more advanced operations (e.g., VLOOKUP, IF statements, and INDEX/MATCH). Functions allow users to automate calculations, saving time and reducing the chance of human error.

Charts and Data Visualization Excel supports a variety of chart types, including bar graphs, line charts, pie charts, and scatter plots. These visual elements help users quickly interpret data and communicate insights effectively. With tools such as conditional formatting, users can also highlight specific data points based on conditions, making patterns and trends easier to spot.

Pivot Tables and Data Summarization Pivot tables allow users to organize, summarize, and analyze large datasets efficiently. They enable the dynamic grouping and filtering of data without altering the original dataset, making them ideal for business reporting and data analysis. Pivot tables are often paired with pivot charts for better visualization of summarized data.

Data Import and Integration Excel can import data from various sources, including databases, CSV files, web pages, and APIs. It also integrates with other Microsoft Office applications (such as Word and PowerPoint) and external services like Microsoft Power BI. This flexibility makes it easy to aggregate and analyze data from multiple sources.

Macros and VBA (Visual Basic for Applications) Excel supports automation through macros, recorded sequences of steps that users can replay to perform repetitive tasks. For more advanced automation, Excel uses VBA, a programming language that allows users to create custom functions, automate workflows, and build complex data manipulation routines.

Collaboration and Cloud Features With Microsoft 365, Excel offers cloud-based features, enabling real-time collaboration. Multiple users can edit a workbook simultaneously, with changes automatically saved and synced. Excel files stored in OneDrive or SharePoint are accessible from any device with internet access.

Applications of Microsoft Excel

Business and Finance Excel is widely used in financial planning and analysis. Businesses rely on it for budgeting, forecasting, cash flow management, and financial reporting. Functions like NPV (Net Present Value) and IRR (Internal Rate of Return) help in investment analysis and decision-making.

Data Analysis and Statistics Excel is a popular tool for data analysis, providing features like data sorting, filtering, and descriptive statistics. Analysts use Excel to clean data, analyze trends, and visualize insights. While it is not as specialized as software like Python or R, Excel’s simplicity makes it accessible to non-programmers.

Project Management Project managers use Excel to create schedules, timelines, and Gantt charts. The ability to link tasks and track progress makes Excel a lightweight alternative to dedicated project management tools. Excel’s conditional formatting can highlight critical paths or upcoming deadlines.

Education and Personal Use Educators use Excel to track student performance, attendance, and grades. Students, in turn, use it for assignments and data projects. At a personal level, individuals use Excel for household budgeting, expense tracking, and planning.

Advantages of Excel

User-Friendly Interface: Excel’s intuitive design makes it accessible to users with varying levels of expertise.

Flexibility: It can handle both small tasks (e.g., grocery lists) and large datasets (e.g., financial reports).

Customization: Users can create customized templates, automate processes with macros, and personalize workflows.

Integration: Excel works seamlessly with other Microsoft tools and third-party applications.

Cross-Platform Availability: Excel is available on Windows, macOS, and mobile platforms, allowing access on the go.

Limitations of Excel

Limited Data Capacity: Excel can handle large datasets, but it may struggle with extremely large files or complex computations, which require specialized software.

Errors and Data Integrity: Incorrect formulas or manual input errors can lead to inaccurate results.

Limited Collaboration: While Excel offers collaboration features, it can’t match the collaborative capabilities of cloud-native tools like Google Sheets.

Learning Curve: Advanced features like pivot tables, VBA, and complex formulas require practice to master.

Conclusion

Microsoft Excel is a versatile and powerful tool that has earned its place as a cornerstone of modern productivity. Its ability to manage, analyze, and visualize data makes it indispensable across industries, from finance and business to education and personal planning. While it has limitations in handling very large datasets or specialized tasks, Excel’s continuous evolution ensures it remains relevant. Whether for simple calculations or complex financial modeling, Excel empowers users to work smarter, offering endless possibilities for data management and decision-making.

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Boost AI Production With Data Agents And BigQuery Platform

Data accessibility can hinder AI adoption since so much data is unstructured and unmanaged. Data should be accessible, actionable, and revolutionary for businesses. A data cloud based on open standards, that connects data to AI in real-time, and conversational data agents that stretch the limits of conventional AI are available today to help you do this.

An open real-time data ecosystem

Google Cloud announced intentions to combine BigQuery into a single data and AI use case platform earlier this year, including all data formats, numerous engines, governance, ML, and business intelligence. It also announces a managed Apache Iceberg experience for open-format customers. It adds document, audio, image, and video data processing to simplify multimodal data preparation.

Volkswagen bases AI models on car owner’s manuals, customer FAQs, help center articles, and official Volkswagen YouTube videos using BigQuery.

New managed services for Flink and Kafka enable customers to ingest, set up, tune, scale, monitor, and upgrade real-time applications. Data engineers can construct and execute data pipelines manually, via API, or on a schedule using BigQuery workflow previews.

Customers may now activate insights in real time using BigQuery continuous queries, another major addition. In the past, “real-time” meant examining minutes or hours old data. However, data ingestion and analysis are changing rapidly. Data, consumer engagement, decision-making, and AI-driven automation have substantially lowered the acceptable latency for decision-making. The demand for insights to activation must be smooth and take seconds, not minutes or hours. It has added real-time data sharing to the Analytics Hub data marketplace in preview.

Google Cloud launches BigQuery pipe syntax to enable customers manage, analyze, and gain value from log data. Data teams can simplify data conversions with SQL intended for semi-structured log data.

Connect all data to AI

BigQuery clients may produce and search embeddings at scale for semantic nearest-neighbor search, entity resolution, semantic search, similarity detection, RAG, and recommendations. Vertex AI integration makes integrating text, photos, video, multimodal data, and structured data easy. BigQuery integration with LangChain simplifies data pre-processing, embedding creation and storage, and vector search, now generally available.

It previews ScaNN searches for large queries to improve vector search. Google Search and YouTube use this technology. The ScaNN index supports over one billion vectors and provides top-notch query performance, enabling high-scale workloads for every enterprise.

It is also simplifying Python API data processing with BigQuery DataFrames. Synthetic data can replace ML model training and system testing. It teams with Gretel AI to generate synthetic data in BigQuery to expedite AI experiments. This data will closely resemble your actual data but won’t contain critical information.

Finer governance and data integration

Tens of thousands of companies fuel their data clouds with BigQuery and AI. However, in the data-driven AI era, enterprises must manage more data kinds and more tasks.

BigQuery’s serverless design helps Box process hundreds of thousands of events per second and manage petabyte-scale storage for billions of files and millions of users. Finer access control in BigQuery helps them locate, classify, and secure sensitive data fields.

Data management and governance become important with greater data-access and AI use cases. It unveils BigQuery’s unified catalog, which automatically harvests, ingests, and indexes information from data sources, AI models, and BI assets to help you discover your data and AI assets. BigQuery catalog semantic search in preview lets you find and query all those data assets, regardless of kind or location. Users may now ask natural language questions and BigQuery understands their purpose to retrieve the most relevant results and make it easier to locate what they need.

It enables more third-party data sources for your use cases and workflows. Equifax recently expanded its cooperation with Google Cloud to securely offer anonymized, differentiated loan, credit, and commercial marketing data using BigQuery.

Equifax believes more data leads to smarter decisions. By providing distinctive data on Google Cloud, it enables its clients to make predictive and informed decisions faster and more agilely by meeting them on their preferred channel.

Its new BigQuery metastore makes data available to many execution engines. Multiple engines can execute on a single copy of data across structured and unstructured object tables next month in preview, offering a unified view for policy, performance, and workload orchestration.

Looker lets you use BigQuery’s new governance capabilities for BI. You can leverage catalog metadata from Looker instances to collect Looker dashboards, exploration, and dimensions without setting up, maintaining, or operating your own connector.

Finally, BigQuery has catastrophe recovery for business continuity. This provides failover and redundant compute resources with a SLA for business-critical workloads. Besides your data, it enables BigQuery analytics workload failover.

Gemini conversational data agents

Global organizations demand LLM-powered data agents to conduct internal and customer-facing tasks, drive data access, deliver unique insights, and motivate action. It is developing new conversational APIs to enable developers to create data agents for self-service data access and monetize their data to differentiate their offerings.

Conversational analytics

It used these APIs to create Looker’s Gemini conversational analytics experience. Combine with Looker’s enterprise-scale semantic layer business logic models. You can root AI with a single source of truth and uniform metrics across the enterprise. You may then use natural language to explore your data like Google Search.

LookML semantic data models let you build regulated metrics and semantic relationships between data models for your data agents. LookML models don’t only describe your data; you can query them to obtain it.

Data agents run on a dynamic data knowledge graph. BigQuery powers the dynamic knowledge graph, which connects data, actions, and relationships using usage patterns, metadata, historical trends, and more.

Last but not least, Gemini in BigQuery is now broadly accessible, assisting data teams with data migration, preparation, code assist, and insights. Your business and analyst teams can now talk with your data and get insights in seconds, fostering a data-driven culture. Ready-to-run queries and AI-assisted data preparation in BigQuery Studio allow natural language pipeline building and decrease guesswork.

Connect all your data to AI by migrating it to BigQuery with the data migration application. This product roadmap webcast covers BigQuery platform updates.

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