ETL

SQL And ETL: The Dynamic Data Duo

If your business is engaging in data and analytics, you may have used SQL (or Structured Query Language) or even developed an ETL process (or Extract, Transform, Load). While it’s often perceived in terms of SQL vs. ETL, looking at both together as SQL + ETL is more applicable to modern business.

When companies engage in fast data manipulation, for example, SQL is often the primary language that allows database servers to communicate, edit, and store data.

When you load customer data into a data warehouse, for example, ETL can help you engage in batch processing to derive the most value for your data warehouse.

Before we go any further, let’s define our terms.

SQL stands for Structured Query Language that is used to access information from a variety of relational databases. By writing SQL commands, you can perform tasks such as updates, data retrieval, deletion, and more.

SQL is used to communicate and manage with the following relational data management systems (RDBMS):

  • MySQL
  • Postgres
  • Microsoft SQL Server
  • Microsoft Access
  • Redshift
  • BigQuery

A very basic example SQL query would be something like this:

SELECT email, created_date
FROM customers
WHERE country = ‘Argentina’
ORDER BY created_date LIMIT 100

 

In this case, the SQL query asks the database the email addresses and date the customer record was created for the first 100 customers in Argentina, and pulls that information from the “customers” table.

ETL can be described as the process of moving data from homogeneous or heterogeneous sources to its target destination (most often a data warehouse). ETL, for example, encompasses three steps that make it easy to extract raw data from multiple sources, process it (or transform it), and load it into a data warehouse for analysis.

Let’s break it down:

Extract: to retrieve raw data from unstructured data pools, and migrate it into a temporary, staging data repository;

Transform: to structure, enrich, and convert raw data to match the target destination;

Load: to load structured data into a data warehouse for analysis or to be leveraged by Business Intelligence (BI) tools.

For decades, data engineers have built or bought ETL pipelines to integrate diverse data types into data warehouses for analysis, seamlessly. The goal here is simple – leveraging ETL makes the process of data analysis much easier (especially when using online analytical processing or OLAP data warehouses).

ETL is also essential when you want to transform online transactional processing databases to work with an OLAP data warehouse quickly.

In recent years, it has become increasingly easy to perform a number of processes involved in ETL, including exception handling, by taking advantage of user-friendly ETL tools that come with robust graphical user interfaces.

The noticeable difference here is that SQL is a query language, while ETL is an approach to extract, process, and load data from multiple sources into a centralized target destination.

Some businesses who have the capital to bear large data warehouse costs opt to use several databases (customer details, sales records, recent transactions, etc.). In such cases, you’ll need a tool to access, adjust, or add to the database as necessary. That’s where SQL comes in.

When working in a data warehouse with SQL, you can:

  • Create new tables, views, and stored procedures within the data warehouse
  • Execute queries to ask and receive answers to structured questions
  • Insert, update or delete records as needed
  • Retrieve data and visualize the contents of the data warehouse
  • Implement strict permissions to access tables, views, and stored procedures

The modern data stack comes with a variety of tools, including ETL tools, and they use SQL to read, write, and query warehouse data. SQL syntax can also be used to frame questions answered using a data warehouse.

However, for the manipulation part of the process, for the most part, data needs to be collected and interpreted before it’s moved to the target destination. That's where ETL comes in.

ETL uses batch or stream processing protocols to pull raw data, modify it according to reporting requirements, and load the transformed data into a centralized data warehouse. However, data scientists can query and analyze the information directly without having to build complex ETL processes.

So we can say that ETL is used to get the data ready and acts as a data integration layer. SQL is used when data needs to be manipulated and tweaked. ETL also helps companies implement data governance best practices and achieve data democracy.

ETL vs ELT

For example, once data is loaded into the warehouse using ETL, you can leverage SQL to transform the data. This is called E-L-T because the Transformation step is the final step instead of an intermediary step. While this can be achieved by writing SQL queries, it’s highly beneficial to use a data warehousing solution like Panoply, which comes with ETL components built-in to perform transformations in SQL.

This method helps boost performance, speed, ensures reusability, and more within a single user interface. This approach helps free up data engineers and data scientists from time-intensive tasks to focus on creating value for your business and customers.

For example, when you’re working with Panoply, you can engage in complex modeling, performance tuning, and complete maintenance tasks quickly.

SQL and ETL together make it easier to maintain the code and take advantage of many reusable components. This approach also provides more flexibility and functionality (like Cached Lookups) and boosts overall performance.

Want to see how ETL and SQL work together with your business data? Get started with your free 14 day trial of Panoply and sync, store, and analyze all your business data in one place.

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