#sqlquery

[simple-logic]

Which SQL function is used to count the number of rows in a SQL query?

a) COUNT() 📊

b) NUMBER() 🔢

c) SUM() ➕

🔍 SQL Quiz Time! 🧠

Drop your answer in the comment section below!👇

[govindhtech]

Introducing Google’s new Looker template FOCUS v1.0 GA

FOCUS v1.0

At Google  Cloud, Google’s team firmly feel that in order to effectively manage and optimise  cloud expenses, you should have the tools necessary to examine Google Cloud costs in conjunction with those of competing  cloud providers. It shouldn’t be necessary to take time to translate cloud providers’ billing jargon. And they think using open standards is the best way to do that.

Google is a key contributor to the v0.5 and v1.0 Preview and GA open billing specifications, a founding member of the FinOps Foundation, and a founding member of the FOCUS project’s Steering Committee. Google cloud is happy to present a new Looker template view today that makes use of the latest FOCUS v1.0 GA to make managing cloud costs easier across clouds.

Explain FOCUS

FOCUS is a technological specification that unifies cost and usage billing data among  cloud vendors and serves as a unifying standard for  cloud billing data. Through the consolidation of cloud and consumption data into a single, shared data structure, FOCUS seeks to provide uniformity and standardisation throughout cloud billing data. Understanding how billing costs, credits, usage, and metrics map from one cloud provider to another was difficult prior to FOCUS because there was no industry-standard method for standardising key cloud cost and usage measures across multiple cloud service providers (CSPs).

All of the main cloud suppliers are supporting the FOCUS initiative, which is creating an open standard for cloud billing data. Version 1.0 introduced consistent vocabulary, taxonomy, and metrics for billing datasets generated by CSPs.

Introducing FOCUS v1.0 GA’s new Looker template

Using Google’s new Looker template, which creates a table based on the FOCUS query results, you can see your open billing data in Looker. You won’t need to generate these tables by hand because the included LookML code produces and maintains them automatically. With several advantages, this template provides an idea of how your cost patterns across services, SKUS, zones, territories, and resource types can be visualised.

Pre-made template: Eliminate the need to wait for personalised dashboards. You may get pre-built visualisations that show cost trends and breakdowns by services, charges, and locations right away by using the templates.

Simple filtering: This template can be used by anyone without the requirement to be a data analyst. Looker’s user-friendly interface enables you to delve deep into facts with a few clicks and filter to particular time periods or services.

Adaptability: Although the template is a fantastic place to start, Looker’s versatility allows you to customise the views to meet your own requirements. You may quickly add custom metrics, alter the visualisations, and integrate the dashboards into your current workflows if necessary.

An updated FOCUS v1.0 GA BigQuery view

Google provide three export options for  Cloud Billing data to BigQuery that are related to cost and usage: Standard Billing Export, Detailed Billing Export (which includes price fields and resource-level data to join with a Price Export table), and Price Export. Google released a new BigQuery view in January that converts data into the FOCUS v1.0 Preview format.

A BigQuery view is a virtual table that displays the output of a SQL query. Google is now releasing an update for that BigQuery view in preparation for the FOCUS v1.0 GA. Please refer to the current guidance, which is updated to reflect any new changes, if you are currently using the Preview and would like to update your BigQuery view to the FOCUS GA.

Because the queryable virtual table in BigQuery views only holds information from the tables and fields listed in the base query that defines the view, these views are fantastic. Since BigQuery views are virtual tables, if you already use Billing Export to BigQuery, there are no extra data storage fees.

This BigQuery view allows you to:

View and query Google Cloud billing data that has been adjusted to comply with FOCUS v1.0.

Utilise Looker Studio or other visualisation tools by using the BigQuery view as a data source.

Examine your Google  Cloud expenses in addition to information from other suppliers using the standard FOCUS format.

How it functions

Your current  Cloud Billing data is layered with a virtual table that is the FOCUS BigQuery view. You must have Price Exports and Detailed Billing Exports enabled in order to utilise this feature. Your Cloud Billing data is mapped into the FOCUS schema by the FOCUS BigQuery view using a basic SQL query, and it is presented in the designated format. This makes it simpler to compare the costs of other  cloud providers by enabling you to query and analyse your data as if it were local to FOCUS.

Looker and Looker Corenot Looker Studio support the Looker template. Make sure you have Detailed Billing Export and Pricing Export Enabled in order to use the template straight out of the box. Permissions are also required in order to start a new Looker Project and Connection.

This Looker template makes use of temporary tables, in contrast to BigQuery Views. You won’t need to create these tables by yourself because the LookML code that is provided will create and maintain them automatically.

Google has made it simple to use the FOCUS feature in Looker and BigQuery by providing a comprehensive guide. Register here to see the Looker template and sample SQL query, as well as to follow the instructions step-by-step.

Read more on govindhtech.com

[analyticsshiksha]

SQL is a programming language used for managing and manipulating relational databases, crucial for storing and retrieving data efficiently.

Stay tuned for the next post!

know more: https://www.analyticsshiksha.com

[techpointfundamentals]

Dynamic Data Filtering (Dynamic Where Condition in SQL):

Question: How can you filter the records based on different dynamic criteria in SQL?

Question: How can you write a stored procedure that accepts different input criteria and returns data filtered based on the input parameters in SQL?

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[hopkins20]

SQL

Quizzes

[futuretechskills]

Mastering SQL for ETL Testing, Business Analysts, and Advanced Querying

[udaipurwebdesigner1]

SQL Assignments with Solutions - SQL Assignments for Beginners

SQL Assignments for Beginners - We have over ten years of experience and have completed over a thousand SQL assignments with solutions.

https://udaipurwebdesigner.com/sql-assignments-with-solutions/

[aiwikiweb]

How Sql AI Empowers Data Analysts to Retrieve Insights Faster

Data analysts often need to write complex SQL queries to retrieve insights from databases, but manually crafting these queries can be time-consuming and prone to errors. Sql AI provides a powerful solution by enabling analysts to generate SQL queries using natural language, simplifying data access.

Problem Statement: Crafting SQL queries requires in-depth knowledge of SQL syntax, which can be challenging for analysts who are not experts in database management. Writing queries manually also increases the risk of errors, leading to delays in retrieving data.

Application: Sql AI allows data analysts to type in natural language requests, such as "show total sales by product for the last quarter," and converts them into optimized SQL queries. This feature makes data retrieval faster and more intuitive, allowing analysts to focus on interpreting the results rather than spending time writing code.

Outcome: By using Sql AI, data analysts can retrieve data insights much more efficiently, reducing the time spent on query creation and minimizing errors. This leads to quicker decision-making and better overall productivity.

Industry Examples:

E-Commerce: Data analysts in e-commerce companies use Sql AI to quickly generate queries that analyze sales trends and customer behavior.

Healthcare: Analysts in healthcare organizations use Sql AI to generate reports on patient data, improving healthcare outcomes through better data analysis.

Finance: Financial analysts use the platform to access transaction data and generate reports on account activity, aiding in fraud detection and financial planning.

Additional Scenarios: Sql AI can also be used by marketers for campaign analysis, HR departments for employee data insights, and small businesses to generate inventory reports.

Discover how Sql AI can help you retrieve insights faster and improve your data analysis workflow. Get started today at https://aiwikiweb.com/product/sql-ai/

[jignecttechnologies]

SQL Queries Execution Order - JigNect Technologies

Enhance your SQL knowledge by understanding the order in which queries are executed. Discover how SQL processes statements for better query writing.

[edcater1]

A Beginner's Guide to Database Management

SQL, or Structured Query Language, is a powerful tool used in database management and manipulation. It provides a standardized method for accessing and managing databases, enabling users to store, retrieve, update, and delete data efficiently. SQL operates through a variety of commands, such as SELECT, INSERT, UPDATE, DELETE, and JOIN, allowing users to perform complex operations on relational databases. Its simplicity and flexibility make it an essential skill for beginners in data management and analysis. With SQL, users can interact with databases seamlessly, extracting valuable insights and ensuring data integrity across various applications and platforms. For the more information visit our website -

[davidkehr08]

SQL Assignment Help

If you find yourself grappling with SQL assignments, seek solace in SQL Assignment Help services. These specialized services are tailored to assist students and professionals in mastering Structured Query Language concepts and overcoming the challenges posed by complex assignments. With SQL Assignment Help, you gain access to expert guidance and solutions, ensuring a comprehensive understanding of database management and query optimization. Navigate through intricate SQL queries, database design, and data manipulation with confidence, as these services provide timely and accurate assistance. Empower your SQL skills, meet academic requirements, and boost your confidence in handling database-related challenges with the dedicated support offered by SQL Assignment Help.

[govindhtech]

Google Cloud Pub Sub Provides Scalable Managed Messaging

Google Cloud Pub Sub

Many businesses employ a multi-cloud architecture to support their operations, for various reasons, such as avoiding provider lock-in, boosting redundancy, or using unique offerings from various cloud providers.

BigQuery, which provides a fully managed, AI-ready multi-cloud data analytics platform, is one of Google Cloud’s most well-liked and distinctive offerings. With BigQuery Omni’s unified administration interface, you can query data in AWS or Azure using BigQuery and view the results in the Google Cloud panel. Then, Pub/Sub introduces a new feature that enables one-click streaming ingestion into Pub/Sub from external sources: import topics. This is useful if you want to integrate and transport data between clouds in real-time. Amazon Kinesis Data Streams is the first external source that is supported. Let’s examine how you can use these new import topics in conjunction with Pub/Sub’s BigQuery subscriptions to quickly and easily access your streaming AWS data in BigQuery.

Google Pub Sub

Learning about important subjects

Pub/Sub is an asynchronous messaging service that is scalable and allows you to separate services that generate messages from those that consume them. With its client libraries, Pub/Sub allows data to be streamed from any source to any sink and is well-integrated into the Google Cloud environment. BigQuery and Cloud Storage can receive data automatically through export subscriptions supported by Pub/Sub. Pub/Sub may send messages to any arbitrary publicly visible destination, such as on Google Compute Engine, Google Kubernetes Engine (GKE), or even on-premises. It is also naturally linked with Cloud Functions and Cloud Run. Image credit to Google Cloud

Amazon Kinesis Data Streams

While export subscriptions enable the uploading of data to BigQuery and Cloud Storage, import topics offer a fully managed and efficient method of reading data from Amazon Kinesis Data Streams and ingesting it directly into Pub/Sub. This greatly reduces the complexity of setting up data pipelines between clouds. Additionally, import topics offer out-of-the-box monitoring for performance and health visibility into the data ingestion processes. Furthermore, automated scalability is provided by import topics, which removes the requirement for human setting to manage variations in data volume.

Import topics facilitate the simple movement of streaming data from Amazon Kinesis Data Streams into Pub/Sub and enable multi-cloud analytics with BigQuery. On an arbitrary timeline, the Amazon Kinesis producers can be gradually moved to Pub/Sub publishers once an import topic has established a connection between the two systems.Image credit to Google Cloud

Please be aware that at this time, only users of Enhanced Fan-Out Amazon Kinesis are supported.

Using BigQuery to analyse the data from your Amazon Kinesis Data Streams

Now, picture yourself running a company that uses Amazon Kinesis Data Streams to store a highly fluctuating volume of streaming data. Using BigQuery to analyse this data is important for analysis and decision-making. Initially, make an import topic by following these detailed procedures. Both the Google Cloud console and several official Pub/Sub frameworks allow you to create an import topic. After selecting “Create Topic” on the console’s Pub/Sub page

Important

Pub/Sub starts reading from the Amazon Kinesis data stream as soon as you click “Create,” publishing messages to the import topic. When starting an import topic, there are various precautions you should take if you already have data in your Kinesis data stream to avoid losing it. Pub/Sub may drop messages published to a topic if it is not associated with any subscriptions and if message retention is not enabled. It is insufficient to create a default subscription at the moment of topic creation; these are still two distinct processes, and the subject survives without the subscription for a short while.

There are two ways to stop data loss:

To make a topic an import topic, create it and then update it:

Make a topic that isn’t importable.

Make sure you subscribe to the subject.

To make the subject an import topic, update its setup to allow ingestion from Amazon Kinesis Data Streams.

Check for your subscription and enable message retention:

Make an import topic and turn on message retention.

Make sure you subscribe to the subject.

Look for the subscription to a timestamp that comes before the topic is created.

Keep in mind that export subscriptions begin writing data the moment they are established. As a result, going too far in the past may produce duplicates. For this reason, the first option is the suggested method when using export subscriptions.

Create a BigQuery subscription by going to the Pub/Sub console’s subscriptions page and selecting “Create Subscription” in order to send the data to BigQuery:

By continuously observing the Amazon Kinesis data stream, Pub/Sub autoscales. In order to keep an updated view of the stream’s shards, it periodically sends queries to the Amazon Kinesis ListShards API. Pub/Sub automatically adjusts its ingestion configuration to guarantee that all data is gathered and published to your Pub/Sub topic if changes occur inside the Amazon Kinesis data stream (resharding).

To ensure ongoing data intake from the various shards of the Amazon Kinesis data stream, Pub/Sub uses the Amazon Kinesis Subscribe ToShard API to create a persistent connection for any shard that either doesn’t have a parent shard or whose parent shard has already been swallowed. A child shard cannot be consumed by Pub/Sub until its parent has been fully consumed. Nevertheless, since messages are broadcast without an ordering key, there are no rigorous ordering guarantees. By copying the data blob from each Amazon Kinesis record to the data field of the Pub/Sub message before it is published, each unique record is converted into its matching Pub/Sub message. Pub/Sub aims to optimise each Amazon Kinesis shard’s data read rate.

You can now query the BigQuery table directly to confirm that the data transfer was successful. Once the data from Amazon Kinesis has been entered into the table, a brief SQL query verifies that it is prepared for additional analysis and integration into larger analytics workflows.

Keeping an eye on your cross-cloud import

To guarantee seamless operations, you must monitor your data ingestion process. Three new metrics that google cloud has added to Pub/Sub allow you to assess the performance and health of the import topic. They display the topic’s condition, the message count, and the number of bytes. Ingestion is prevented by an incorrect setup, an absent stream, or an absent consumer unless the status is “ACTIVE.” For an exhaustive list of possible error situations and their corresponding troubleshooting procedures, consult the official documentation. These metrics are easily accessible from the topic detail page, where you can also view the throughput, messages per second, and if your topic is in the “ACTIVE” status.

In summary

For many businesses, functioning in various cloud environments has become routine procedure. You should be able to utilise the greatest features that each cloud has to offer, even if you’re using separate clouds for different aspects of your organisation. This may require transferring data back and forth between them. It’s now simple to transport data from AWS into Google Cloud using Pub/Sub. Visit Pub/Sub in the Google Cloud dashboard to get started, or register for a free trial to get going right now.

Read more on govindhtech.com

[onlineitcourse]

Teradata SQL Online Certification Training | H2k Infosys

Introduction:

Teradata SQL refers to the structured query language (SQL) dialect used specifically with Teradata Database, a popular data warehousing solution. SQL is a domain-specific language used for managing and querying relational databases. Teradata SQL is tailored to work efficiently with the Teradata Database, which is known for its parallel processing capabilities and its ability to handle large-scale data processing.

Key aspects of Teradata SQL:

Parallel Processing: Teradata is designed for parallel processing, meaning it can divide tasks among multiple processors to handle large volumes of data more efficiently.

Why Choose H2k Infosys for this Teradata SQL Training

H2k Infosys provides 100% job oriented Teradata training online and onsite training to individuals and corporate teams.

Our Teradata certification training is instructor-led, face-to-face training with live classes.

We incorporate real-time project work in our Teradata training which helps our students gain practical, hands-on experience.

Our faculty are accomplished data professionals in their rights with many years of industrial and teaching experience.

During our Teradata SQL training H2k infosys, we conduct several mock interviews to help you gain the confidence to face real interviews.

After completion of the course, we assist you in preparing your resume.

We also provide you with recruiter driven job placement assistance Future of Teradata SQL:

Integration with Cloud Services: Teradata is working on enhancing its cloud offerings. This includes the integration with popular cloud platforms like AWS, Azure, and Google Cloud. The future may see more seamless integration and optimization for cloud-based deployments.

Advanced Analytics and Machine Learning: Teradata has been expanding its capabilities in advanced analytics and machine learning. Expect more features and functionalities geared towards data science applications.

Focus on Hybrid and Multi-Cloud Environments: As organizations increasingly adopt hybrid and multi-cloud strategies, Teradata may continue to evolve to support these complex environments.

Optimization for IoT and Streaming Data: With the proliferation of IoT devices and the importance of real-time data processing, Teradata may develop features to handle streaming data and IoT workloads more efficiently.

AI-Driven Automation and Optimization: Automation and AI-driven features may become more prominent in Teradata SQL to help optimize queries, workload management, and performance tuning.

Tags: H2kinfosys, Teradata SQL Online Certification Training | H2k Infosys, Teradata Database, For Basic Level, Teradata SQL Aggregates, data warehousing, data engineering, real-time project work training.

[techpointfundamentals]

Returning data from Dynamic Linked-Server in SQL:

What is Linked-Server in SQL? How can you check the available Linked-Servers in SQL? How can you return the data from the linked-server which is known at run-time?

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[excelworld]