In today's data-driven world, efficiently managing large volumes of data is a pain point for many companies.
While ETL plays a key role and might seem like the end-all solution, it's not always enough to make informed business decisions. And that's where SQL comes in.
In this tutorial, we'll dive deeper into ETL and SQL and how they can work together. We'll also discuss the bottom line of why your company should adopt a hybrid approach for data processing.
What's the Difference Between ETL and SQL?
ETL and SQL are two distinct concepts serving different purposes in data management.
ETL (Extract, Transform, Load) integrates data from structured and unstructured data sources.
SQL (Structured Query Language) is a programming language for managing relational databases.
ETL involves extracting, transforming, and loading data into a target system. ETL pipelines typically involve specialized tools like Portable or Fivetran.
SQL is used to create, modify, and query databases. It's a language that can be used with a variety of tools and platforms.
ETL's purpose is to manage data integration between systems, while the purpose of SQL is to manipulate data within a single system.
What Is ETL?
- ETL (Extract, Transform and Load) is an integration process that moves data from external applications into a data warehouse. It's one of the essential components of the modern data stack.
- The purpose of ETL/ELT is to centralize data in a single location. This makes it easier for business intelligence teams to conduct complex analyses.
- Companies can then leverage their analysis and findings to improve the organization.
- ETL solution cost varies widely. Organizations generally pay anywhere from $99 to $2000+ per month for popular tools.
The ETL process can be broken down into three main steps:
- Extraction: Data is extracted from various external sources. These sources can be flat files, APIs with platforms like LinkedIn, or from business applications like Salesforce. Many ETL applications have built-in connectors to automate this ingestion process.
- Transformation: Extracted data is transformed to match the required format of the target system. The target system is your central location for data, like a warehouse.
- Loading: The data will then be loaded into your central data location. This destination is often a data warehouse but could also be a data lake or another type of data repository.
These are a few examples of how an ETL pipeline can fit into your company's data strategy:
- Data migration: ETL allows organizations to move large amounts of data quickly. This can be particularly useful when transferring data from an on-premises system to the cloud.
- Automating manual workflows: ETL processes can reduce the need for manual data entry. This helps improve efficiency by eliminating human error.
- Real-time monitoring: An ETL pipeline can synchronize data in real-time. This enables companies to track key metrics as they happen.
Popular ETL and Data Warehouse Tools
- Portable: Portable is one of the most accommodating ETL solutions on the market. It's excellent for companies with many long-tail applications that other popular tools don't support.
- Fivetran: Fivetran is a fully managed, cloud-based ETL solution. Tasks like data translation and quality checks are automatic. This zero-maintenance architecture means less work for your data team.
- Airbyte: Airbyte is a free, open-source data integration tool that serves more mainstream data sources. It also can create bespoke connectors.
- Hevo: Hevo is a cloud-based ETL solution with real-time replication from 150+ supported data sources.
- Matillion: Matillion is an ETL solution that includes an on-premise option. Its friendly user interface makes creating data pipelines simpler.
- Amazon Redshift: Redshift is a fully-managed, cloud-based data warehouse service. It's built around industry-standard SQL.
- Snowflake: Snowflake is a data warehouse platform supporting ETL and ELT workflows.
- Informatica: Informatica is another popular data integration tool that's designed around ETL workflows.
Choosing the Right ETL Tool
While pricing is an essential piece of the puzzle, it's not the only factor to consider when choosing an ETL solution for your company.
- Data sources and connectors: Check that the tool you're considering supports your company's data types and sources. Many tools will create custom connectors upon request, but this can be costly — unless you use a tool like Portable for free.
- Data volume: Even if you're a small business, you want a tool that can handle large volumes and scale for big data management as you grow.
- Ease of use: User-friendliness is critical without a dedicated ETL developer, especially if your team isn't very technical.
- Support and maintenance: Regular maintenance and updates are the oil that keeps your data warehouse running smoothly. Also, remember that some tools charge extra for hands-on customer support, while other solutions include it for free.
What Is SQL?
- SQL (Structured Query Language) is a powerful programming language. It's mainly used to manage and communicate with databases. It's the standard language used in most relational database management systems (RDBMS).
- It allows data engineers to convert raw data into modeled data in an automated and, more importantly, scalable manner.
- SQL uses various functions to manipulate data, including metadata and schema.
- SQL statements are written in declarative syntax. This means that users describe what they want to do with the data rather than how to do it.
These are some of the most common SQL commands:
- SELECT: This command allows you to select data in a database.
- WHERE: WHERE is used to apply conditions to the SELECT statement to filter your results.
- ORDER: This command allows you to sort results in either ascending or descending order.
- JOIN: This command joins related data stored in different tables to retrieve combined results.
- ALIAS: ALIAS is used to give a table a temporary nickname that's more easily readable.
- INSERT: This command lets you add new data to an existing table or database.
- UPDATE: The UPDATE command changes specific rows after you've inserted data.
- UPSERT: This command lets you update a record without first checking if it exists.
- DELETE: As you can probably guess, this command allows you to erase records.
Here are a couple of use cases for SQL:
- Querying and data retrieval: SQL commands can be used to filter, sort, and aggregate your company's data.
- Database maintenance: SQL is beneficial for database maintenance, managing user access, and security.
Popular SQL Tools
- MySQL: MySQL is one of the most popular relational database management systems. It's open-source and designed for fast transactions.
- Oracle Database: Oracle is a proprietary RDBMS. It's widely used across large enterprises and government organizations.
- Microsoft SQL Server: Microsoft SQL server is known for its scalability and security. And it should come as no surprise that it's easily integrated with other Microsoft products like Excel and PowerPoint.
- SQLite: SQLite is a lightweight and open-source RDBMS. It includes database administration capabilities that allow you to track the health of your SQL server.
- PostgreSQL: PostgreSQL is a robust open-source tool. It has advanced features such as support for geospatial data and JSON.
- Azure Data Studio: Azure is a modern data analytics tool for SQL Server and Azure databases.
How ETL and SQL Work Together
While ETL and SQL are distinct, they are often used together in data management.
- Using ETL with SQL allows data teams to manage large volumes of data for business insights.
- SQL is used to write queries and commands to extract, transform, and load transformed data. ETL solutions play a role by automating this process, making it more efficient, and providing a nice visual interface.
- ETL testing is another process that's facilitated by complex SQL queries. It uses these queries to access, extract, transform, and load large volumes of data.
SQL Uses in Extract
- SQL is used to define the query that will extract the data from the external source systems.
- Queries can also be used in this phase to filter data, perform calculations, and join tables before the data is extracted by a big data ETL tool.
SQL Uses in Transform
- The data transformation phase allows analysts to convert unstructured data into packaged data sets. In most scenarios, this is the phase where SQL does its heaviest lifting.
- During transformation, SQL aggregate functions like COUNT, SUM, and AVG are used to summarize data by grouping rows.
- Other common commands used during transformation are WHERE, ORDER BY, and JOIN.
SQL Uses in Load
- In the final phase of the ETL process, SQL is used to define the schema of the target system.
- From there, it can be used to create tables and indexes, optimize queries, and even enforce referential integrity.
ETL with SQL Examples and Use Cases
- Online retail businesses can use ETL with SQL to efficiently manage their product and customer data.
- ETL tools collect and load e-commerce analytics from multiple sources, such as CRMs. SQL queries can then be used to integrate that data, standardize customer attributes, and remove duplicates.
- Companies can also use SQL to segment customers based on lifetime value and purchase frequency.
- Companies can use ETL tools to extract data and calculate financial metrics using queries. This can aid in generating financial reports (such as balance sheets and income statements) in Excel.
- You can use SQL queries to filter company transactions based on the type of transaction, account type, or date range. This can give organizations a clearer picture of their outgoing expenses.
- SQL commands are often used to calculate financial metrics, which can then be analyzed using a language like Python.
- ETL with SQL helps financial departments maintain high financial data quality. With the use of queries, detecting errors and inconsistencies is less complicated.
- In supply chain management, SQL can be used to analyze operation bottlenecks and inefficiencies.
- ETL can extract logistics data like average delivery time and route optimization and load it into a database for analysis.
- From there, extracted data can be used to generate new data, like projected arrival dates, using SQL.
- Electronic health records (EHRs) contain detailed clinical data. ETL can be used with SQL queries to extract only relevant patient data sections.
- Using SQL commands, hospitals can also calculate important metrics like patient readmission rate, patient satisfaction, and healthcare costs.
- This information can be used to create predictive models like patient risk scores and patient outcomes.
- ETL tools extract and integrate marketing data from platforms like Klavio, Meltwater, social media, and website analytics. SQL can use this data to help companies determine campaign ROI (return on investment).
- SQL can also be a tool for improving marketing campaigns. For example, you could use a decision tree to recommend products based on your customers' unique browsing/purchase history.
- Human resources teams can transform employee data using SQL queries to calculate key metrics and build predictive models with machine learning algorithms.
- For example, companies can utilize logistic regression to predict which employees are most likely to churn.
- Organizations can also use SQL to join payroll data with HR data to add/remove benefits, adjust salaries, etc.
The Bottom Line on ETL With SQL
ETL and SQL are distinct concepts in data management that can work together brilliantly.
Taking a hybrid approach to data warehousing makes it easier for your team to manage and analyze data to garner valuable insights that push your organization forward.
If you're still looking for the right ETL solution for your company, consider Portable. With over 350 built-in connectors to long-tail applications, it's one of the most accommodating options. The Portable team also offers hands-on support and can build custom connectors for free upon request.
Try Portable** for free today!**