How ETL is being used in business intelligence

Although we’ve talked about the steps involved in ETL, it’s important to understand how it’s actually carried out in real-world scenarios – one of those being how it helps business teams get the insights they need. Here, we’ll explain a little about how to use ETL in a data-driven organization.

What is ETL, and when should it be used?

‘ETL’ stands for ‘Extract, Transform, and Load’. This is the extraction of raw data from disparate sources, the transformation of said data, then the loading of the completed data set into a target destination.

In business, ETL essentially means collating and unifying data into a useful state, typically so that analysis can then be performed to gain useful insights. Three common usages of ETL are data migration from legacy systems to modern data warehouses, merging data from different siloed systems, and automation of data handling. How ETL is being used can dramatically affect its outcome, potentially creating incredible value for business intelligence.

What does an ETL process require?

ETL processes are complex and involve a number of considerations before execution. Here are the main requirements:

• Data profiling – The data sources must be carefully examined to understand their structure, so that the ETL pipeline can be designed appropriately. The quality and integrity of the sources should also be determined to identify invalid or missing records.
• API compatibility – Connections are needed to transfer data between different locations, as well as handle different file formats.
• Credential management – Your dataops system will need to access different data sources with different login credentials, so these must be easily accessed, secured, and managed.
• Security – Since data from your ETL process will end up in a data warehouse, you’ll need to be careful about how you handle it. Most data sets now include PII or sensitive information, which must be encrypted with permissions restricted as appropriate. Failing to properly secure your data can result in data breaches, regulatory fines, and loss of trust.
• Governance – You’ll need to check that your ETL pipeline complies with your company’s data governance policies. These regulate the quality and security of the data used.

But wait – there’s more! Before embarking on the five steps of the ETL process, there are some other features to consider:

• Automation and scheduling – You absolutely can and should automate as much of your ETL process as possible. After all, the purpose of ETL is to avoid manual inputting of data. ETL can involve hundreds of integration tasks each day, so the more of these you can automate, the more time and manual work you’ll save. Scheduling is important to orchestrate all of these jobs.
• Monitoring and alerting – Processing tasks fail for many different reasons, such as data corruption. You should set up your ETL pipeline to monitor these events and alert you if they occur, so that you can rectify the problem more quickly.

Is ETL testing easy?

Nothing about data unification is “easy”, and ETL testing is no exception. There are a number of considerations when checking the extract, transform, and load process. You must design a battery of test cases to validate the output of the ETL process, as well as benchmarks to measure the performance of the pipeline. You also need to assess the data sources compared with the completed data set, such as ensuring the number of records match, and document any bugs or errors.

What is ETL architecture?

ETL architecture is a blueprint of your data pipeline, progressing like a flowchart from the first stage to the last. This includes, for example, how data is to be transferred from the sources to the target destination, the rules applied at transformation, and the properties of the target. It should also include the programming languages used, such as SQL and Python, as well as file formats, such as CSV, JSON, and spreadsheets.

One aspect of creating your architecture is ETL vs. ELT. ELT stands for extract, load, and transform. Rather than using an interim storage location to transform data, ELT executes transformations in the target destination itself. ELT is a more modern concept that has grown in popularity due to cloud-based data warehouses, which can transform data on demand.