SQL On-Demand: An easier way to Query Data

TLDR

• About SQL On-Demand
• Creating the Data Source
• Gaining Access to the Azure Storage
• Querying Files in Azure Storage through Azure Synapse Workspace
• YouTube Video for Visual Description of Steps Below

About SQL On-Demand

In my last post, I mentioned the Top 10 Features in Azure Synapse Analytics. Synapse SQL Database has 2 options: SQL Pool & SQL On-Demand. In this post, I am going to dive in deeper in to the 2nd Option; SQL On-Demand. SQL On-Demand is an interesting feature that will make Query your Data a lot more easier.

So What is SQL On-Demand? According to the Microsoft Docs. It is a

Synapse SQL on-demand (preview) is a serverless query service that enables you to run SQL queries on files placed in Azure Storage.

So what type of files will you find in your storage? These are files like CSV, **Parquet **Files, and JSON. This is quite interesting because typically you’ll have to import these file into your Database, not to mention the making sure the data format is compatible with the target database environment.

How can you Query Files without pulling them in?

This is done by creating an External Data Source. Data Source includes:

• Master Key
• Database Scoped Credential

#azure #azure-sql-database #big-data #sql #azure-synapse-analytics

What is GEEK

Buddha Community

Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

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Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

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SQL for Data Science

Currently, the demand for the skill of SQL is on the rise. Most of the jobs describe their skill requirements, and while doing that, they mention the knowledge in SQL specifically. As the name suggests, ‘Data Science’ is data-driven. Thus, SQL will be an integral part of any data science job. It is also because of the advantage that it offers among the other alternatives. This article tries to elaborate on why SQL and querying are essential for data science and related roles.

If you want to learn SQL for data science, then you can start your journey here!

Structured Query Language, acronymized to SQL, is a computer programming language aimed and designed to manipulate data warehoused in RDBMSs, i.e., Relational Database Management Systems. Different functions such as insertion, deletion, updating, modification of data can be done using SQL. Since most of the structured data is stored in RDBMSs, working with data science will necessarily involve RDBMS and, hence, SQL.

With the advent of big data, data warehousing using relational database management systems has gained more importance, and it is strictly necessary to use them. Moreover, traditionally along with the programming languages Python and R, SQL is used. For instance, a data scientist can write an SQL query to extract data from a database, on which further analyses can be made using Python or R.

If you want to become a data scientist, then you can start your journey here!

Why SQL for Data Science?

Data Science is simply the analysis and study of data to extract meaningful insights. SQL comes into the picture in two of the most critical steps of a data science cycle — Data Extraction, the pre-processing step, as mentioned in the introduction, and Machine Learning. Most of the database platforms are designed using SQL, as it has become a standard for database systems. Also, it is easy to communicate with databases with complex instructions and manipulate data.

Modern systems such as Hadoop, Spark use SQL to maintain relational database systems and to process structured data. Identification of suitable data sources and pre-processing are the key steps in any data analysis work. Since the data is stored in relational databases, querying to extract the data without copying the entire database is necessary as it saves time and is efficient. Hence, a data scientist needs to have comprehensive knowledge in querying language, SQL.

Importance of SQL

SQL is a comprehensive language with several functions, statements, and operators that pave the way to seamless data extraction. SQL has multiple reasons to assert its importance and relevance in data science. First of all, even though SQL has a wide range of tools available, learning them is not an arduous task, as the commands and queries in SQL are comparable to simple English. For example, consider the SQL query ‘select name, nationality from employee’, which can be comprehended by any person of its function with its simplicity of language. Thus, a data science novice can quickly learn SQL, unlike the other programming languages that require more conceptual understanding.

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Introduction to Recursive CTE

This article will introduce the concept of SQL recursive. Recursive CTE is a really cool. We will see that it can often simplify our code, and avoid a cascade of SQL queries!

Why use a recursive CTE ?

The recursive queries are used to query hierarchical data. It avoids a cascade of SQL queries, you can only do one query to retrieve the hierarchical data.

What is recursive CTE ?

First, what is a CTE? A CTE (Common Table Expression) is a temporary named result set that you can reference within a SELECT, INSERT, UPDATE, or DELETE statement. For example, you can use CTE when, in a query, you will use the same subquery more than once.

A recursive CTE is one having a subquery that refers to its own name!

Recursive CTE is defined in the SQL standard.

How to make a recursive CTE?

A recursive CTE has this structure:

• The WITH clause must begin with “WITH RECURSIVE”
• The recursive CTE subquery has two parts, separated by “UNION [ALL]” or “UNION DISTINCT”:
• The first part produces the initial row(s) for the CTE. This SELECT does not refer to the CTE name.
• The second part recurses by referring to the CTE name in its FROM clause.

Practice / Example

In this example, we use hierarchical data. Each row can have zero or one parent. And it parent can also have a parent etc.

Create table test (id integer, parent_id integer);

insert into test (id, parent_id) values (1, null);

insert into test (id, parent_id) values (11, 1);
insert into test (id, parent_id) values (111, 11);

insert into test (id, parent_id) values (112, 11);

insert into test (id, parent_id) values (12, 1);

insert into test (id, parent_id) values (121, 12);

For example, the row with id 111 has as ancestors: 11 and 1.

Before knowing the recursive CTE, I was doing several queries to get all the ancestors of a row.

For example, to retrieve all the ancestors of the row with id 111.

While (has parent)

	Select id, parent_id from test where id = X

With recursive CTE, we can retrieve all ancestors of a row with only one SQL query :)

WITH RECURSIVE cte_test AS (
	SELECT id, parent_id FROM test WHERE id = 111
	UNION 
	SELECT test.id, test.parent_id FROM test JOIN cte_test ON cte_test.id = test.parent_id

) SELECT * FROM cte_test

Explanations:

• “WITH RECURSIVE”:

It indicates we will make recursive

• “SELECT id, parent_id FROM test WHERE id = 111”:

It is the initial query.

• “UNION … JOIN cte_test” :

It is the recursive expression! We make a jointure with the current CTE!

Replay this example here

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