- Though introduced in the 1970s, SQL remains extremely relevant even today for a range of data-related operations.
- In the world of big data and analytics, SQL is used to generate queries for storing, retrieving and manipulating data.
- SQL has today become a core part of the BI stack because it makes it easy to handle complex stored procedures and functions, database optimization and data modeling.
- At Merit Group, we’ve built a team of highly experienced SQL developers with expertise in SQL Server, MYSQL, PL SQL for writing queries and data warehouse concepts including implementation of ETL pipelines, data integration and data querying.
Here’s a bit of SQL history: Since the 1970s, Structured Query Language (SQL) has been playing a key role in extracting information from relational database systems. It was first developed at IBM by Donald Chamberlin and Raymond Boyce. In 1986, it became the standard used by American National Standards Institute (ANSI) and in 1987, the International Organization for Standardization (ISO) started embracing SQL.
Although data-related technologies have evolved at a rapid pace, SQL continues to be a popular language that works well even with next-generation technologies such as AI/ML, cloud-native systems, data virtualization and mobile engineering entering the market.
A domain-specific language, SQL can be used for a variety of operations such as creating a database, storing, modifying, extracting data in tables, and so on. With the volume of data being ingested by every organization going up, the need to store it securely and manage it effectively has become critical. Believe it or not, SQL is the most popular language for data work even in 2021. That’s not bad for a language introduced five decades back!
Pulling Up Data
Data-driven decision-making has become a game-changer for enterprises today. Almost every modern organization in the world today has a Business Intelligence (BI) and analytics strategy in place.
SQL helps in combing the data in the RDBMS which has a particular structure, with each record having several linked keys represented visually in a grid. It can be used for altering, retrieving data or manipulating data sets.
“One of the reasons for its continued popularity is its simplicity and its straightforward syntax”.
Developers can use simple SQL commands such as SELECT, ORDER BY, and INSERT to route data in and out of a database table.
Among the many versions and frameworks available for SQL, MySQL is the most common one as it is an open-source solution and enables back-end data management for web applications.
This makes it a popular tool in companies such as Facebook, Instagram, WhatsApp, etc. for back-end data storage and processing.
When an SQL query is generated, it performs the following three operations:
- Parsing – Checking the syntax
- Binding – Checking the query semantics
- Optimization – Generating the query execution plan
The most effective and fast query execution plan is identified in the third step through various permutations and combinations.
Read about other advanced tools recommended by Merit’s data engineering experts for powering and optimising your BI Stack.
Features of SQL
Used widely today for business intelligence, data manipulation and data testing, some of the key features of SQL that make it popular include:
- It’s extremely easy to manage and simple to learn.
- It is flexible as it is platform-agnostic and can work with different database frameworks such as Oracle, IBM, and Microsoft, among others. (For example, Microsoft offers Azure SQL an intelligent and scalable database built for the cloud)
- It can retrieve information proficiently and quickly.
- As it is very structured with established guidelines, overseeing databases without the need for coding is one of the greatest advantages of using this language.
- It can process queries quickly, retrieving, inserting, deleting and manipulating large amounts of data quickly and efficiently.
- Since the syntax is similar to the spoken language, it does not need codes and is very user-friendly.
- It can be run on any device and operating system and can also be embedded with other applications if required.
- It can provide multiple data views, enabling different functions to draw relevant insights from the same data sets.
SQL, despite its many benefits, has its own disadvantages as well. Some of these include:
- Its complex interface can be intimidating
- Some versions can prove to be expensive and therefore inaccessible to programmers
- Hidden business rules may prevent complete control over the database, thereby limiting its effectiveness
Uses of SQL
As the advantages outweigh these disadvantages, SQL is used in a variety of ways including:
- Creating a Database: SQL is a Data Definition Language (DDL) and can be used to create a database, define its structure, and delete it after use.
- Secure Data: SQL is also a Data Control Language (DCL) that protects the database against debasement and misuse through permission controls.
- Modifying a Database: As a Data Manipulation Language (DML), SQL can be used to work with an existing database, adding, modifying, and separating information in the database as needed.
- Front and Back-End Use: SQL can be used both at the Client and the Server end, interfacing the front-end with the back-end and thereby supporting both the customer and the worker architecture. It also supports the three-level design of the Internet architecture with the customer, application worker, and database levels.
- Retrieving Information: One of the defining roles of the SQL, it can help retrieve data as it is a Data Query Language
- Transaction Control: Aligned with ACID, SQL facilitates maintaining the transactions occurring in the databases to ensure database consistency.
SQL finds application in data analysis, machine learning, data management and so on as it integrates with scripting languages such as Python and R with ease.
About Merit Group
At Merit Group, we work with some of the world’s leading B2B intelligence companies like Wilmington, Dow Jones, and Haymarket. Our data and engineering teams work closely with our clients to build data products and business intelligence tools. Our work directly impacts business growth by helping our clients to identify high-growth opportunities.
Our specific services include high-volume data collection, data transformation using AI and ML, web watching, and customized application development.
Our team also brings to the table deep expertise in building real-time data streaming and data processing applications. Our expertise in data engineering is especially useful in this context. Our data engineering team brings to fore specific expertise in a wide range of data tools including SQL, Airflow, Kafka, Python, PostgreSQL, MongoDB, Apache Spark, Snowflake, Redshift, Athena, Looker, and BigQuery.
If you’d like to learn more about our service offerings or speak to a Airflow expert, please contact us here: https://www.meritdata-tech.com/contact-us/
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