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The rise in self-service analytics is a significant selling point in the business intelligence world. Part of the point of creating self-service analytics is having easy access to the data from your organization.

The question is how do you get your data from external application data sources into a usable format?

The answer is ETLs.

These days, ETLs (Extract, Transform, Load) are a vital aspect of Business Intelligence (BI). With ETLs, data from different sources can be grouped into a single place for analytics programs to act on and realize key business insights. ETL is here and it is highly significant.

What is ETL (Extract, Transform, Load)?

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Data is the foundation of the modern business world. Data on its own is not very useful. On top of that, the data is often stored in some form of application database that isn't easy to use for analytics.

This is why ETL tools are essential. ETLs take data from multiple systems and combine them into a single database (often referred to as a data warehouse) for analytics or storage.

ETLs imply data migrating from one application/database to an analytical database. An ETL takes three steps to get the data from database A to database B. These are:

• Extract (E)
• Transform (T)
• Load (L)

Extract

The extract function involves the process of reading the data within a database. This stage also involves data collection and extraction. Depending on the type of system the extract might operate in several different ways. It could extract the data into some form of flat file or just directly pull it from an API. This is dependent on the risk of interacting with the application system, the timing requirements, and several other technical restrictions.

Transform

The transform function converts the extracted data into a proper format for analysis and storage. This process includes changing the extracted data from its old structure into a more denormalized format. This step is dependent on the end-database. For example, data warehouses have a very specific design pattern that requires reshaping data and implementing slowly changing dimensions.

Load

The load function takes the process of writing the transformed data into the new application/database. This could take several steps as each stage might augment the data differently. The standard setup is to have raw, staging, and production databases. There are other configurations depending on the project's needs.

Major Use Case of ETL

ETLs prepare data and make it accessible and meaningful for analysis. Occasionally, ETLs can be used for several other tasks.

Here are three of the main tasks ETLs can be used for:

• Data Integration
• Data Warehousing
• Data Migration

Data integration

Integrating data is a more regular occurrence in today's business world. Systems that are connected can be aware of updates made in adjacent databases. This might not directly tie into the specific application but it can be very valuable --- it can help provide new features and functionality into applications, as well as new insights, by creating a bridge between products.

Data warehousing

Data warehouses are a key component in creating dashboards and other business intelligence functions. They are a central place for financial, marketing, supply chain and employee data from multiple applications to live. This allows business managers to ask questions across departments and gain key insights.

Data migration

Data migrations are an unavoidable evil. If you're planning to make an upgrade or switch systems, you would have to move data. This can often require lots of changes as databases and systems are 100% alike. In turn, this leads to the need to convert data and alter functionality to fit the new system. Once established, data can easily be migrated. While data migration is not always fun, it is the perfect use case for ETL. You can learn more about data migrations here.

Why Are ETLs Important?

Regardless of the company size, the level of complexity, and the number of data sources, companies will always benefit from better access to their data. ETLs provide access to what's happening in their processes. They also provide the ability to create reports and metrics that can drive strategy.

These reports and metrics are a crucial part of competing with other similar organizations.

So the next question is: which type of ETL tool should your company pick?

ETL Tools

There are many different types of ETL tools an organization can choose from. Picking a standard ETL toolset is an important step as it will impact your team's ability to develop in the future. Picking a very niche ETL framework makes it hard to find developers who can also develop your infrastructure. Here are the two key types of ETL tools.

• Third-party tools
• Library-based frameworks

Third-party tools

Third-party ETL tools are probably the most commonly used ETL tools that large corporations rely on. This is because these tools are often built to scale and have a strong pool of developers that can build on them. These tools include Informatica, SSIS, and Alooma. They're all pretty much drag and drop and make it easy for even non-programmers to pull data from applications.

Programming library tools

There are a lot of new libraries, specifically in Python, that are used as ETLs. You might have heard of Airflow, Luigi, or bonobo. These are all libraries created in python for ETLs. These are great because they allow you lots more flexibility than third-party drag and drop tools. You can easily write your own custom functions and scripts inside the frameworks.

In Conclusion

Today's corporations demand easy and quick access to data. This has lead to an increasing demand for transforming data into self-serviceable systems.

ETLs play a vital part in that system. They ensure analysts and data scientists have access to data from multiple application systems. This makes a huge difference and lets companies gain new insights.