Five steps of the ETL process
ETL stands for Extract, Transform, and Load, and is a process for collating and unifying different data sets. With an increasing number of data silos to manage, having a solid ETL strategy in place is necessary for accessing and analyzing this data to gain business insights.
From the name, you may assume that it’s strictly a three-stage process (just the ‘E’, the ‘T’, and the ‘L’, right?). But in reality, ETL often consists of at least five steps. So let’s take a look at these five steps in more detail.
ETL process steps
The five steps of the ETL process can be abbreviated to ‘ECTLA’: Extract, Clean, Transform, Load, and Analyze.
1. Extract: Data sets are captured from unstructured sources and placed into a temporary staging location. Validation processes are often performed during the extraction phase, such as entity presence (ensuring the source and target have matching tables and fields), metadata validation (checking that the definitions of the table and column data types are correct, and executed according to the required specifications), and data completeness (uncovering any missing records or rows in the target table).
2. Clean: Before the data sets can be transformed, they must be cleaned and preprocessed through a series of standardization and normalization steps, which remove any defects and invalid records.
3. Transform: The data sets are processed by applying a series of rules, then converted into the correct format for the intended destination. The three most common transformations in the ETL process are key restructuring (establishing a set of attributes which identify rows, and the rows’ relationships between different tables), data filtering, and derivation (calculating new values from existing data).
4. Load: The finished data set is transferred to the target destination, which is usually a data warehouse. There are two different types of loading in ETL: ‘full load’ (in which the whole data set is transferred to the target in a single process) and ‘incremental load’ (in which the data set is transferred piecemeal in regular intervals). Incremental loads can be further categorized into ‘streaming’, whereby new records are continuously loaded when they are ready, and ‘batch’, whereby records are loaded in groups. In incremental loads, the date of the last load is stored to ensure that only records created after this date are transferred.
5. Analyze: In the last stage, the data is now a strategic asset. It is visualized using leading business intelligence tools to deliver reports and analytics to the business teams.
You can find out more about the ETL process in our ultimate guide.
ETL process example
The best way to illustrate ETL is by examining a real-world use-case. Here’s an example of an ETL process using a data warehouse and batch processing.
Unification of customer records:
Extract: Data sets are copied from a variety of sources, including non-relational databases, APIs, CSV files, and XML files. These are then converted into a single format.
Clean: Any records which have values outside of the expected ranges are filtered out. For example, you may want to include customers who have made a purchase in the last 24 months.
Transform: Business rules are applied, such as joining data sets to merge customer records. Then data integrity is checked to avoid corruption or loss of information.
Load: Transfer data to the target tables in the data warehouse. To avoid storage space mushrooming, causing performance limitations or spiraling costs, the warehouse can overwrite existing records whenever a new batch of customer records is loaded.
Analyze: Data analysts can gain business insights by scrutinizing the company’s entire customer base, such as elucidating how purchase behavior relates to lifetime value.
ETL tools examples
Over the years, a panoply of ETL software has been developed to process data efficiently. ETL is used in both enterprise and SMB settings, each of which use different needs. Some tools require a high degree of DIY, while others provide a more turnkey solution.
The core languages used to build data pipelines are Python and SQL. SQL is a query language used for searching and updating tables in a database, but cannot access different data silos. Python, meanwhile, is a versatile language with many different use-cases. You can learn more about how to choose the most appropriate tool in our comprehensive guide to ETL.
ETL tools list
To give you an idea of the most popular software, here’s an ETL tools list:
Google Data Flow
Azure Data Factory
As the need to connect disparate data sources increases, ETL tools are continuing to evolve. But real-world applications are more complicated than simply following a series of steps. Switchboard’s platform provides a powerful environment which can deal with complex pipelines and unify your company’s data. So, contact us to find out how we can help.