#AI SQL

[dbajamey]

Meet Your New Favorite SQL Copilot — dbForge AI Assistant

Tired of writing SQL from scratch or wasting time optimizing clunky queries?

Now you don’t have to.

The newly developed dbForge AI Assistant, created by Devart, makes even complex SQL coding tasks simple:

Generate, explain, and optimize context-aware queries — instantly Simply attach the required database, and the Assistant will promptly check its metadata. After that, it will be able to generate SQL queries of any type and complexity that will be relevant to your schema.

Convert natural language to SQL dbForge AI Assistant is apt even without the context. You can ask it to generate a query of any kind; just write down a request in your natural language, and the Assistant will respond immediately.

Troubleshoot errors before they hit production You can ask the Assistant to analyze and troubleshoot your SQL code. To do that, enter a query; if there is something wrong with it, the Assistant will see that and immediately provide you with analysis results and actionable suggestions.

Get contextual help across dbForge tools, and much more Enjoy real-time AI chat support, smart coding prompts, and tailored guidance directly within dbForge tools.

Whether you're a developer racing to meet release deadlines, a DBA managing complex environments, a data analyst working without deep SQL knowledge, or a team lead looking to speed up code reviews — dbForge AI Assistant is built to boost your productivity.

Just update to the newest version of your dbForge tool, open the dbForge AI Assistant, and let it do the heavy lifting.

No guesswork. No syntax stress. Just smart SQL, faster.

Get started now with new intelligent dbForge AI Assistant.

Not sure yet? Take it for a spin with a free 14-day trial!

More details, screenshots, and tips here.

[dcoder0604]

Day 0

Hello guys welcome to my blog. this blog is based on coding life

In this blog, I can share updates on daily codes and data-related work, What I do, and what I don't do on a daily basis

So it will be starting on the 1st of November because my exams are near

[xaltius]

Unlocking Intelligence with GenAI-Powered SQL Queries

In today's data-driven world, information is gold. Businesses of all sizes are sitting on vast reserves of valuable insights within their databases, waiting to be unearthed. Yet, for many, this data remains locked behind a formidable barrier: SQL (Structured Query Language). While SQL is the universal language of databases, its syntax, complex joins, and the need for deep schema knowledge often create bottlenecks, making data access an exclusive club for skilled data analysts and engineers.

What if anyone in your organization – a marketing manager, a sales lead, or a finance executive – could simply ask a question in plain English and instantly get the data they need? This is no longer a futuristic fantasy. Thanks to Generative AI (GenAI), specifically large language models (LLMs), we are on the cusp of democratizing data intelligence by transforming natural language into powerful SQL queries.

The Traditional SQL Challenge: A Bottleneck to Insights

For years, the process of extracting specific data insights often looked like this:

A business user has a question ("What were our top-selling products last quarter by region?").

They write a request to a data analyst.

The data analyst translates the request into a complex SQL query, navigating database schemas, table relationships, and specific column names.

The analyst executes the query, retrieves the data, and presents it back to the business user.

This process, while effective, can be slow, resource-intensive, and a significant bottleneck to agile decision-making. Non-technical users often feel disconnected from their own data, hindering their ability to react quickly to market changes or customer needs.

Enter Generative AI: The Universal Data Translator

GenAI acts as an intelligent bridge between human language and database language. These advanced AI models, trained on vast datasets of text and code, have developed an uncanny ability to understand context, infer intent, and generate coherent, structured output – including SQL queries.

How it Works:

Natural Language to SQL (NL2SQL): You simply type your question in conversational English (e.g., "Show me the average customer lifetime value for new customers acquired in the last six months who purchased product X").

Contextual Understanding: The GenAI model, ideally with some understanding of your specific database schema (either through fine-tuning or provided context), interprets your request. It understands the business terms and maps them to the correct tables, columns, and relationships.

SQL Generation: The AI then crafts the precise SQL query needed to fetch that specific information from your database.

Optional: Explanation & Optimization: Some GenAI tools can also explain a generated SQL query in plain English, helping users understand how the data is being retrieved. They can even suggest optimizations for existing queries to improve performance.

The Transformative Benefits of GenAI-Powered SQL

The implications of this technology are profound, fundamentally changing how organizations interact with their data:

Democratized Data Access: This is perhaps the biggest win. Business users – from marketing specialists to HR managers – who lack SQL expertise can now directly query data, reducing their dependence on technical teams.

Accelerated Decision-Making: No more waiting for analysts. Instant access to insights means faster, more informed business decisions, allowing companies to be more agile and competitive.

Reduced Bottlenecks: Data analysts and engineers are freed from writing repetitive or simple queries, allowing them to focus on complex modeling, strategic initiatives, data architecture, or advanced analytics projects.

Improved Accuracy: GenAI, when trained correctly, can reduce human error in query writing, especially for complex joins, aggregations, or conditional statements.

Enhanced Data Literacy: By interacting with data in a natural way, users can gradually build a better understanding of their data landscape and the questions it can answer.

Rapid Prototyping and Exploration: Business users can quickly test hypotheses and explore different dimensions of their data without needing extensive technical support.

Real-World Use Cases in Action

Sales & Marketing: "Which marketing campaigns last quarter led to the highest customer conversion rates in the North region, broken down by product category?"

Customer Service: "What is the average resolution time for customer support tickets marked as 'high priority' that originated from mobile users in the past month?"

Operations & Supply Chain: "Show me the current inventory levels for all raw materials in Warehouse 3 that are below our reorder threshold, ordered by supplier lead time."

Finance: "Calculate the gross profit margin for each product line in the last fiscal year, comparing it to the previous year."

Even for Developers: Quickly generate boilerplate queries for new features, or suggest optimizations for existing slow-running queries.

Best Practices for Implementation

While GenAI for SQL is incredibly powerful, successful implementation requires thoughtful planning:

Clear Data Governance and Security: Ensure that GenAI tools adhere strictly to data access controls. Users should only be able to query data they are authorized to see.

Well-Documented Schemas: While GenAI is smart, providing clear, consistent, and well-documented database schemas significantly improves the accuracy of generated queries.

Contextual Training (Fine-tuning): For optimal results, fine-tune the GenAI model on your organization's specific data, terminology, common query patterns, and domain-specific language.

Human Oversight is Crucial: Especially in the early stages and for critical queries, always review AI-generated SQL for correctness, efficiency, and potential security implications before execution.

Start Simple, Iterate: Begin by implementing GenAI for less critical datasets or well-defined use cases, gathering feedback, and iteratively improving the system.

User Training: Train your business users on how to phrase their questions effectively for the AI, understanding its capabilities and limitations.

The Future of Data Analysis is Conversational

GenAI-powered SQL queries are not just a technological advancement; they represent a fundamental shift in the human-data interaction. They are empowering more individuals within organizations to directly engage with their data, fostering a culture of curiosity and data-driven decision-making. As these technologies mature, they will truly unlock the hidden intelligence within databases, making data a conversational partner rather than a complex enigma. The future of data analysis is accessible, intuitive, and remarkably intelligent.

[gvtacademy]

How VBA Automates Reporting and AI Enhances Insights in Data Analysis

In today’s data-driven world, companies are flooded with massive volumes of information. Yet, raw data alone doesn’t deliver value. What matters is how quickly and intelligently we can process and analyze it. This is exactly where the powerful duo of VBA (Visual Basic for Applications) and Artificial Intelligence (AI) comes into play. At GVT Academy, we have integrated both into our curriculum, offering the Best Data Analyst Course with VBA & AI in Noida to meet the industry’s growing demand for skilled professionals.

Why VBA Is Still Relevant in 2025

Despite the emergence of newer tools, VBA remains a powerful language for automating repetitive tasks, especially in Excel – a tool widely used in businesses of all sizes. With VBA, data analysts can:

Create your daily and weekly reports in seconds—just one click and you're done

Create personalized macros to simplify data cleaning and formatting tasks

Reduce manual errors and improve efficiency

Schedule and generate reports without supervision

This kind of automation saves not just hours, but sometimes days of work—giving analysts more time to focus on what really matters: analysis and decision-making.

How AI Transforms the Way We Analyze Data

While VBA handles automation, AI brings intelligence. Machine learning models can identify hidden trends, forecast future patterns, and even detect anomalies in large datasets. In GVT Academy’s Data Analyst course, you learn:

Predictive analytics using Python libraries

Customer behavior analysis with machine learning

Natural Language Processing for unstructured data

AI-driven dashboards for smart visualizations

AI empowers analysts to move beyond dashboards and deliver real insights that drive business strategy.

The Magic Happens When VBA Meets AI

Imagine this: You build an automated Excel report using VBA that pulls sales data every morning. That same data is fed into an AI model which predicts future revenue or flags unusual spending patterns. This is not fiction — it’s the real-world, hands-on training offered in the Best Data Analyst Course with VBA & AI in Noida, exclusively at GVT Academy.

Our hybrid approach teaches students to automate routine tasks using VBA, and then apply AI to gain deeper, more accurate insights from the data.

What You'll Learn at GVT Academy

At GVT Academy, our program is designed to make you industry-ready. Here's what you gain:

Mastery over Excel and VBA for automation

In-depth training in Power BI and SQL

Python programming for AI and machine learning

Real-life projects and case studies to apply what you learn

Interview preparation and resume building

Our trainers are industry professionals who bring real-world expertise into the classroom, ensuring you're learning what employers actually look for.

Why Choose GVT Academy in Noida?

GVT Academy has helped countless students build successful careers, backed by dedicated placement support — making it a top choice for the Best Data Analyst Course with VBA & AI in Noida. Our focus is not just on tools—but on problem-solving and critical thinking, which are crucial in today’s competitive job market.

Whether you're a fresher or a working professional looking to upskill, this course is a gateway to high-paying jobs in data analytics, business intelligence, and AI-driven decision-making roles.

Final Thoughts

VBA streamlines your current tasks, while AI empowers you to forecast what’s coming next. Together, they make a powerful combination that companies are actively seeking in their data teams. Don’t miss the opportunity to become a skilled data analyst with expertise in both.

Enroll now at GVT Academy and take the smartest step toward your data analytics career.

1. Google My Business: http://g.co/kgs/v3LrzxE

2. Website: https://gvtacademy.com

3. LinkedIn: www.linkedin.com/in/gvt-academy-48b916164

4. Facebook: https://www.facebook.com/gvtacademy

5. Instagram: https://www.instagram.com/gvtacademy/

6. X: https://x.com/GVTAcademy

7. Pinterest: https://in.pinterest.com/gvtacademy

8. Medium: https://medium.com/@gvtacademy

[giridhar78]

While AI can generate SQL queries, it can’t replace the power of human intelligence. At Global Teq, we help you master SQL by teaching how to apply real-world business logic, context, and error-checking skills that AI can't match. Our SQL course is designed for beginners and professionals alike, with hands-on practice, expert guidance, and job-oriented learning. Learn SQL, master it, and stay in demand in today’s data-driven world. Enroll now and future-proof your tech career!

📞 Contact: +1 (516) 974-6662 📧 Email: globalteq.info@gmail.com 🌐 Website: www.global-teq.com

[eduacations-blog]

Data Science Lifecycle: From Data to Decisions

In today's fast-moving digital world, data isn't just numbers—it’s currency. And the people who know how to work with it? They're shaping the future.

Whether you're managing a business, running marketing campaigns, or building apps, understanding the data science lifecycle in 2025 is no longer optional. It’s the framework that turns raw data into real results.

Let’s break down the five core steps of data science and explore how they work together to power smart, data-driven strategies.

Step 1: Business Understanding – Defining the Real Problem

Everything begins with a question. In data science, the first step is to understand the business problem you’re trying to solve.

Are customers dropping off after signup?  Is a product underperforming in a specific market?

Before any data is touched, clear objectives must be defined. This step helps teams focus on what matters most and aligns analytics efforts with real business outcomes.

Why it matters in 2025: Companies that prioritize business data insights from the start save time and money—and build more relevant solutions.

Step 2: Data Mining – Gathering What You Need

With a clear objective in mind, the next step is data mining—the process of collecting the right data from the right sources.

That could mean pulling records from internal databases, scraping data from websites, or connecting to external APIs. In modern data analytics, the variety and volume of data sources has exploded, making this step both powerful and challenging.

The key is knowing what to collect—and what to leave out.

Step 3: Data Cleaning – Preparing Your Data for Analysis

Ask any experienced data scientist and they��ll tell you: clean data is gold.

This step, also known as data preparation, involves fixing missing values, removing duplicates, and correcting inconsistencies. It’s all about ensuring the data is high-quality and ready for analysis.

If your data is messy, your results will be too—no matter how fancy the model.

Clean data importance has skyrocketed in 2025, especially with the rise of automation and real-time insights.

Step 4: Data Exploration – Let the Patterns Speak

Once your data is clean, it’s time to explore.

This stage involves using visualizations and statistical summaries to understand what’s going on inside your dataset. You might discover trends, detect outliers, or uncover correlations that weren’t obvious before.

Data exploration is like turning on the lights—you suddenly see the story the data is telling.

It’s a crucial part of modern analytics and sets the foundation for better decision-making.

Step 5: Feature Engineering – Building Smarter Data

Not all data is immediately useful. That’s where feature engineering comes in.

This is the process of transforming raw data into more meaningful inputs for machine learning models. You might combine variables, categorize data, or create entirely new features that better represent the problem.

Think of it like refining raw materials into a polished product—this is where data becomes intelligent.

In 2025, feature engineering is a critical skill, especially for improving model accuracy and performance.

Why This Lifecycle Matters More Than Ever

The steps of data science aren’t just a checklist—they’re a roadmap. They ensure that companies, regardless of industry, can turn data into action.

With businesses relying more than ever on data-driven strategy, knowing how this lifecycle works empowers teams to stay competitive, innovative, and efficient.

Final Thoughts

The data science lifecycle in 2025 is more relevant than ever. It blends technology with critical thinking, automation with human insight. Whether you’re a beginner learning the ropes or a business leader exploring analytics, understanding these five steps is the key to unlocking smarter decisions.

So, the next time you hear the term “data science,” just remember—it’s not magic. It’s a process. And now you know exactly how it works.

So, now that you’ve got a clear idea of how the data science lifecycle works and why it matters, the real question is—are you ready to actually do it?

Because here’s the truth: reading articles is great, but real transformation happens when you start building. And that’s exactly what we help you do at Ntech Global Solutions.

We don’t believe in just teaching definitions or tools. We focus on helping you think like a data scientist, solve real problems, and build the kind of portfolio that makes recruiters take notice. From the first step of understanding business needs to cleaning data, analyzing it, and building predictive models—we walk you through it all, side by side.

Our approach is practical, personal, and built for today’s fast-changing industry. You’ll get hands-on with real-world datasets, work on live projects, and gain the confidence to step into the field with clarity—not confusion.

Whether you’re a college student curious about tech, a working professional ready to upgrade, or someone looking to switch into a high-growth career path, we’ve got your back. And we don’t stop at training—we also offer career support and mentorship that actually makes a difference.

Because your future in data science isn’t just waiting—it’s being built. One decision at a time. Let’s make it count.

[nschool]

Unlock Your Career Potential with a Data Science Certificate Program

What Can I Do with a Certificate in Data Science?

Data science is a broad field that includes activities like data analysis, statistical analysis, machine learning, and fundamental computer science. It might be a lucrative and exciting career path if you are up to speed on the latest technology and are competent with numbers and data. Depending on the type of work you want, you can take a variety of paths. Some will use your strengths more than others, so it is always a good idea to assess your options and select your course. Let’s look at what you may acquire with a graduate certificate in data science.

Data Scientist Salary

Potential compensation is one of the most critical factors for many people when considering a career. According to the Bureau of Labor Statistics (BLS), computer and information research scientists may expect a median annual pay of $111,840, albeit that amount requires a Ph.D. degree. The BLS predicts 19 percent growth in this industry over the next ten years, which is much faster than the general average.

Future data scientists can make impressive incomes if they are willing to acquire a Ph.D. degree. Data scientists that work for software publishers and R&D organizations often earn the most, with top earners making between $123,180 and $125,860 per year. On average, the lowest-paid data scientists work for schools and institutions, but their pay of $72,030 is still much higher than the national average of $37,040.

Role of statistics in research

At first appearance, a statistician’s job may appear comparable to that of a data analyst or data scientist. After all, this job necessitates regular engagement with data. On the other hand, statistical analysts are primarily concerned with mathematics, whereas data scientists and data analysts focus on extracting meaningful information from data. To excel in their field, statisticians must be experienced and confident mathematicians.

Statisticians may work in various industries since most organizations require some statistical analysis. Statisticians frequently specialize in fields such as agriculture or education. A statistician, on the other hand, can only be attained with a graduate diploma in data science due to the strong math talents necessary.

Machine Learning Engineer

Several firms’ principal product is data. Even a small group of engineers or data scientists might need help with data processing. Many workers must sift through vast data to provide a data service. Many companies are looking to artificial intelligence to assist them in managing extensive data. Machine learning, a kind of artificial intelligence, is a vital tool for handling vast amounts of data. 

Machine learning, on the other hand, is designed by machine learning engineers to analyze data automatically and change it into something useful. However, the recommendation algorithm accumulates more data points when you watch more videos. As more data is collected, the algorithm “learns,” and its suggestions become more accurate. Furthermore, because the algorithm runs itself after construction, it speeds up the data collection.

Data Analyst

A data scientist and a data analyst are similar, and the terms can be used interchangeably depending on the company. You may be requested to access data from a database, master Excel spreadsheets, or build data visualizations for your company’s personnel. Although some coding or programming knowledge is advantageous, data analysts rarely use these skills to the extent that data scientists do.

Analysts evaluate a company’s data and draw meaningful conclusions from it. Analysts generate reports based on their findings to help the organization develop and improve over time. For example, a store analyst may use purchase data to identify the most common client demographics. The company might then utilize the data to create targeted marketing campaigns to reach those segments. Writing reports that explain data in a way that people outside the data field can understand is part of the intricacy of this career.

Data scientists

Data scientists and data analysts frequently share responsibilities. The direct contrast between the two is that a data scientist has a more substantial background in computer science. A data scientist may also take on more commonly associated duties with data analysts, particularly in smaller organizations with fewer employees. To be a competent data scientist, you must be skilled in math and statistics. To analyze data more successfully, you’ll also need to be able to write code. Most data scientists examine data trends before making forecasts. They typically develop algorithms that model data well.

Data Engineer

A data engineer and a data scientist are the same people. On the other hand, data engineers frequently have solid technological backgrounds, and data scientists usually have mathematical experience. Data scientists may develop software and understand how it works, but data engineers in the data science sector must be able to build, manage, and troubleshoot complex software. 

A data engineer is essential as a company grows since it will create the basic data architecture necessary to move forward. Analytics may also discover areas that need to be addressed and those that are doing effectively. This profession requires solid software engineering skills rather than understanding how to interpret statistics correctly.

Important Data Scientist Skills

Data scientist abilities are further divided into two types.

Their mastery of sophisticated mathematical methods, statistics, and technologically oriented abilities is significantly tied to their technical expertise.

Excellent interpersonal skills, communication, and collaboration abilities are examples of non-technical attributes.

Technical Data Science Skills

While data scientists only need a lifetime of information stored in their heads to start a successful career in this field, a few basic technical skills that may be developed are required. These are detailed below Technical Data Science Skills

An Understanding of Basic Statistics

An Understanding of Basic Tools Used

A Good Understanding of Calculus and Algebra

Data Visualization Skills

Correcting Dirty Data

An Understanding of Basic Statistics

Regardless of whether an organization eventually hires a data science specialist, this person must know some of the most prevalent programming tools and the language used to use these programs. Understanding statistical programming languages such as R or Python��and database querying languages such as SQL is required. Data scientists must understand maximum likelihood estimators, statistical tests, distributions, and other concepts. It is also vital that these experts understand how to identify which method will work best in a given situation. Depending on the company, data-driven tactics for interpreting and calculating statistics may be prioritized more or less.

A Good Understanding of Calculus and Algebra

It may appear unusual that a data science specialist would need to know how to perform calculus and algebra when many apps and software available today can manage all of that and more. Valid, not all businesses place the same importance on this knowledge. However, modern organizations whose products are characterized by data and incremental advances will benefit employees who possess these skills and do not rely just on software to accomplish their goals.

Data Visualization Skills & Correcting Dirty Data

This skill subset is crucial for newer firms beginning to make decisions based on this type of data and future projections. While robots solve this issue in many cases, the ability to detect and correct erroneous data may be a crucial skill that differentiates one in data science. Smaller firms significantly appreciate this skill since incorrect data can substantially impact their bottom line. These skills include locating and restoring missing data, correcting formatting problems, and changing timestamps.

Non-Technical Data Science Skills

It may be puzzling that data scientists would require non-technical skills. However, several essential skills must be had that fall under this category of Non-Technical Data Science Skills.

Excellent Communication Skills

A Keen Sense of Curiosity

Career Mapping and Goal Setting Skills

Excellent Communication Skills

Data science practitioners must be able to correctly communicate their work’s outcomes to technically sophisticated folks and those who are not. To do so, they must have exceptional interpersonal and communication abilities.

A Keen Sense of Curiosity

Data science specialists must maintain a level of interest to recognize current trends in their business and use them to make future projections based on the data they collect and analyze. This natural curiosity will drive them to pursue their education at the top of their game.

Career Mapping and Goal Setting Skills

A data scientist’s talents will transfer from one sub-specialty to another. Professionals in this business may specialize in different fields than their careers. As a result, they need to understand what additional skills they could need in the future if they choose to work in another area of data science.

Conclusion:

Data Science is about finding hidden data insights regarding patterns, behavior, interpretation, and assumptions to make informed business decisions. Data Scientists / Science professionals are the people who carry out these responsibilities. According to Harvard, data science is the world’s most in-demand and sought-after occupation. Nsccool Academy offers classroom self-paced learning certification courses and the most comprehensive Data Science certification training in Coimbatore.

[hubertdudek]

Databricks: what’s new in May 2025? Updates & Features Explained! #databricks Databricks, What’s New in Databricks? May 2025 Updates & Features Explained! In May 2025, Databricks added several key features. 📌 Key Highlights for This Month: - *0:16* 16.4 LTS - *0:28* Autoloader auto cleaner - *2:28* Lakeflow UI connectors - *3:01* Workflow run with different settings - *4:27* ETL/DLT editor - *5:30* PRIVATE materialised views and streaming tables - *6:48* Delta share materialised views and streaming tables - *7:27* Clean rooms up to 10 collaborators - *7:57* Predictive optimisation for all - *8:45* Just-in-time user provisioning - *10:04* Cluster logs - *11:13* Run the code inside the assistant - *13:22* Query snippets - *14:34* New charts - *15:43* Run apps locally - *16:51* Custom data sources - *18:01* Syntax highlighter - *19:25* String aggregation ============================= 📚 *Notebooks from the video:* 🔗 [GitHub Repository](https://ift.tt/aJpTNju) 🔔𝐃𝐨𝐧'𝐭 𝐟𝐨𝐫𝐠𝐞𝐭 𝐭𝐨 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐮𝐩𝐝𝐚𝐭𝐞𝐬. https://www.youtube.com/@databricks_hubert_dudek/?sub_confirmation=1 🔗 Support Me Here! ☕Buy me a coffee: https://ift.tt/nlEDgNR ✨ Explore Databricks AI insights and workflows—read more: https://ift.tt/hUeGRFE ============================= 🎬Suggested videos for you: ▶️ [What’s new in January 2025](https://www.youtube.com/watch?v=JJiwSplZmfk) ▶️ [What’s new in February 2025](https://www.youtube.com/watch?v=tuKI0sBNbmg) ▶️ [What’s new in March 2025](https://youtu.be/hJD7KoNq-uE) ▶️ [What’s new in April 2025](https://youtu.be/FDgtNVeLTc8) ============================= 📚 **New Articles for Further Reading:** - 📝 *Clean Landing Zone — autoloader cleanSource:* 🔗 [Read the full article](https://ift.tt/gS2h1s3) - 📝 *Nested groups in databricks:* 🔗 [Read the full article](https://ift.tt/TileUHn) - 📝 *Cost Benchmark: 2 billion records from bronze to silver on serverless:* 🔗 [Read the full article](https://ift.tt/WUnICfR) - 📝 *Logs to Volumes and to Dataframe:* 🔗 [Read the full article](https://ift.tt/Reya0pJ) ============================= 🔎 Related Phrases: #databricks #bigdata #dataengineering #machinelearning #sql #cloudcomputing #dataanalytics #ai #azure #googlecloud #aws #etl #python #data #database #datawarehouse via databricks by Hubert Dudek https://www.youtube.com/channel/UCR99H9eib5MOHEhapg4kkaQ May 19, 2025 at 03:07AM

[simple-logic]

#PollTime What stores structured data?

A) SQL 🗄️ B) NoSQL 📂 C) API 🔗 D) AI 🤖

Comments your answer below👇

💻 Explore insights on the latest in #technology on our Blog Page 👉 https://simplelogic-it.com/blogs/

🚀 Ready for your next career move? Check out our #careers page for exciting opportunities 👉 https://simplelogic-it.com/careers/

[chiefastro]

Idea Frontier #4: Enterprise Agentics, DaaS, Self-Improving LLMs

TL;DR — Edition #4 zeroes-in on three tectonic shifts for AI founders: Enterprise Agentics – agent frameworks such as Google’s new ADK, CrewAI and AutoGen are finally hardened for production, and AWS just shipped a reference pattern for an enterprise-grade text-to-SQL agent; add DB-Explore + Dynamic-Tool-Selection and you get a realistic playbook for querying 100-table warehouses with…

[fraoula1]

𝐅𝐫𝐨𝐦 𝐒𝐐𝐋 𝐭𝐨 𝐀𝐈: 𝐇𝐨𝐰 𝐃𝐚𝐭𝐚 𝐓𝐞𝐚𝐦𝐬 𝐀𝐫𝐞 𝐄𝐯𝐨𝐥𝐯𝐢𝐧𝐠

The shift from SQL to AI is reshaping how data teams operate. In just 60 seconds, discover how roles, skills, and tools have evolved—and why staying ahead of the curve is crucial in today’s data-driven world. Let’s talk about what’s next.

Watch https://lnkd.in/gKtpygq5

[emergysllc]

[ #eBook 🤖 ] | SQL AI Agents - Marking a shift from static reporting to an AI-first intelligent ecosystem. Let's decode: https://www.emergys.com/ebooks/sql-ai-agents-bi-for-the-future/ Read our comprehensive guide for a deeper analysis on the future of business with SQL AI Agents! 🔄 #SQLAI#aigents#Emergys

[xaltius]

Unlock the Power of Data: SQL - Your Essential First Step in Data Science

So, you're eager to dive into the fascinating world of data science? You've heard about Python, R, and complex machine learning algorithms. But before you get swept away by the advanced stuff, let's talk about a foundational skill that's often underestimated but absolutely crucial: SQL (Structured Query Language).

Think of SQL as the universal language for talking to databases – the digital warehouses where most of the world's data resides. Whether you're aiming to analyze customer behavior, predict market trends, or build intelligent applications, chances are you'll need to extract, manipulate, and understand data stored in databases. And that's where SQL shines.

Why SQL is Your Best Friend as a Beginner Data Scientist:

You might be wondering, "With all the fancy tools out there, why bother with SQL?" Here's why it's the perfect starting point for your data science journey:

Ubiquitous and Essential: SQL is the standard language for interacting with relational databases, which are still the backbone of many organizations' data infrastructure. You'll encounter SQL in almost every data science role.

Mastering Data Wrangling: Before you can build models or create visualizations, you need to clean, filter, and transform your data. SQL provides powerful tools for these crucial data wrangling tasks. You can select specific columns, filter rows based on conditions, handle missing values, and join data from multiple tables – all with simple, declarative queries.

Understanding Data Structure: Writing SQL queries forces you to understand how data is organized within databases. This fundamental understanding is invaluable when you move on to more complex analysis and modeling.

Building a Strong Foundation: Learning SQL provides a solid logical and analytical foundation that will make it easier to grasp more advanced data science concepts and tools later on.

Efficiency and Performance: For many data extraction and transformation tasks, SQL can be significantly faster and more efficient than manipulating large datasets in memory with programming languages.

Bridging the Gap: SQL often acts as a bridge between data engineers who manage the databases and data scientists who analyze the data. Being proficient in SQL facilitates better communication and collaboration.

Interview Essential: In almost every data science interview, you'll be tested on your SQL abilities. Mastering it early on gives you a significant advantage.

What You'll Learn with SQL (The Beginner's Toolkit):

As a beginner, you'll focus on the core SQL commands that will empower you to work with data effectively:

SELECT: Retrieve specific columns from a table.

FROM: Specify the table you want to query.

WHERE: Filter rows based on specific conditions.

ORDER BY: Sort the results based on one or more columns.

LIMIT: Restrict the number of rows returned.

JOIN: Combine data from multiple related tables (INNER JOIN, LEFT JOIN, RIGHT JOIN).

GROUP BY: Group rows with the same values in specified columns.

Aggregate Functions: Calculate summary statistics (COUNT, SUM, AVG, MIN, MAX).

Basic Data Manipulation: Learn to insert, update, and delete data (though as a data scientist, you'll primarily focus on querying).

Taking Your First Steps with Xaltius Academy's Data Science and AI Program:

Ready to unlock the power of SQL and build a strong foundation for your data science journey? Xaltius Academy's Data Science and AI program recognizes the critical importance of SQL and integrates it as a fundamental component of its curriculum.

Here's how our program helps you master SQL:

Dedicated Modules: We provide focused modules that systematically introduce you to SQL concepts and commands, starting from the very basics.

Hands-on Practice: You'll get ample opportunities to write and execute SQL queries on real-world datasets through practical exercises and projects.

Real-World Relevance: Our curriculum emphasizes how SQL is used in conjunction with other data science tools and techniques to solve actual business problems.

Expert Guidance: Learn from experienced instructors who can provide clear explanations and answer your questions.

Integrated Skill Development: You'll learn how SQL complements other essential data science skills like Python programming and data visualization.

Conclusion:

Don't let the initial buzz around advanced algorithms overshadow the fundamental importance of SQL. It's the bedrock of data manipulation and a crucial skill for any aspiring data scientist. By mastering SQL, you'll gain the ability to access, understand, and prepare data – the very fuel that drives insightful analysis and powerful AI models. Start your data science journey on solid ground with SQL, and let Xaltius Academy's Data Science and AI program guide you every step of the way. Your data-driven future starts here!

[pleasantgentlementale]

𝐘𝐨𝐮𝐫 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐁𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭! Want to break into the field of data science? Follow these key steps to build a strong foundation and land your dream job! ✅ Learn Python, SQL & Statistics ✅ Master Data Visualization (Tableau/Power BI) ✅ Understand Machine Learning Concepts ✅ Work on Real-world Projects ✅ Build a Portfolio & Apply for Jobs Start your journey today!

[codeandcanvas]

Let’s talk about resets

I had to do one larger one, so I’ll give you the full list. This is basically an undo.

On Tumblr:

I had to undo the rename of my tumblr, and went back to Code & Canvas.

Used XKit Rewritten to replace the tag “mario breskic” with “code and canvas” for my posts.

On social.mariobreskic.de:

Renamed the “mario breskic” tag to “code and canvas” in WordPress.

Replaced the links to mario-breskic.tumblr.com with codeandcanvas.tumblr.com using phpMyAdmin

UPDATE wp_posts SET post_content = REPLACE(post_content, 'mario-breskic', 'codeandcanvas') WHERE post_content LIKE '%mario-breskic%';

Replaced the string “Mario Breskic” with “Code & Canvas” but only in posts tagged “code and canvas” using phpMyAdmin

UPDATE wp_posts p JOIN wp_term_relationships tr ON p.ID = tr.object_id JOIN wp_term_taxonomy tt ON tr.term_taxonomy_id = tt.term_taxonomy_id JOIN wp_terms t ON tt.term_id = t.term_id SET p.post_content = REPLACE(p.post_content, 'Mario Breskic', 'Code & Canvas') WHERE p.post_type = 'post' AND t.name = 'code and canvas' AND p.post_content LIKE '%Mario Breskic%';

Struck the previous changes from my changelogs on my websites and added a note that I have done so.

Made link changes on my homepage and on my bento.me, too.

Same with other socials with more link options, like Artstation, Bēhance, Facebook.

I used an AI (Copilot) to write the SQL for me.

All in all, this should get me to at least 80% of the reset. There might be something I’ve overlooked but this is alright for now.

As resets go, this one has been smooth.

I think I’ll grab a splatbook and sit down with a cup of caffeine‑free coffee.