ETL is a process in data migration projects that involves extracting data from its original source, transforming it into a suitable format for the target database and loading it into the final destination. It is vital for ensuring accurate and efficient data migration outcomes since it allows organizations to convert all of their existing data into more easily managed, analyzed and manipulated formats. The ETL process moves data from its source(s) into another system or database, where it can be used for analysis and decision-making purposes.

In this brief guide to ETL, learn more about how it works, the impact it can have on business operations and top ETL tools to consider using in your business.

How does ETL work?

The ETL three-step process is a crucial piece of data migration projects. Here’s how it works, broken down into each of its three main components.

Step one: Extract

The extract step is the first part of ETL. It involves gathering relevant data from various sources, whether homogeneous or heterogeneous. These data sources may use different formats, such as relational databases, XML, JSON, flat files, IMS and VSAM, or any other format obtained from external sources by web spidering or screen scraping.

PREMIUM: Consider implementing a cloud data storage policy.

In many solutions, streaming these data sources directly to the destination database may be possible in some cases when intermediate data storage is unnecessary. Throughout this step, data professionals must evaluate all extracted data for accuracy and consistency with the other datasets.

Step two: Transform

Once data is extracted, the next step of the ETL process is transform. Transformations are a set of rules or functions applied to extracted data to make it ready for loading into an end target. Transformations can also be applied as data cleansing mechanisms, ensuring only clean data is transferred to its final destination.

Transformations can be tricky and complex because they may require different systems to communicate with one another. This means compatibility issues could arise, for example, when considering character sets that may be available on one system but not another.

Multiple transformations may be necessary to meet business and technical needs for a particular data warehouse or server. Some examples of transformation types include the following:

• Encoding free-form values: Mapping “Female” to “F”
• Choosing to load only specific columns: Selecting only “Name” and “Address” from a row
• Normalizing data: Joining first and last names into a single column called “Name”
• Sorting data: Sorting customer IDs by ascending or descending order
• Deriving new calculated values: Computing average products sold per customer
• Pivoting and transposing data: Converting columns into rows

Step three: Load

The last step of ETL is loading transformed information into its end target. Loading could involve an asset as simple as a single file or as complex as a data warehouse. Common destinations include on-premises data warehouses; cloud storage solutions such as Amazon S3, Google Cloud and Azure Data Lake; and cloud data warehouses such as Snowflake, Amazon Redshift, Google BigQuery and Microsoft Azure Synapse Analytics.

PREMIUM: Check out this cloud data warehouse guide and checklist.

This process can vary widely depending on the requirements of each organization and its data migration projects.

Benefits of ETL

ETL offers several benefits to data management professionals. They include:

• Data consistency and quality: ETL ensures the data from various sources remains consistent after transformation. Cleansing, enrichment and validation during transformation also improve quality.
• Scalability and performance: Large data volumes are handled efficiently, while the load on databases is reduced by offloading transformation processed from the target system.
• Security and compliance: With ETL, data can easily be masked, encrypted and anonymized during transformation to comply with privacy laws and regulations.

Drawbacks of ETL

While ETL is a powerful and useful data migration process, it also comes with a few disadvantages, namely:

• Latency and batch processing: ETL processes typically use batch processing. This introduces latency and is not ideal for scenarios that require near-instant data updates.
• Complexity and maintenance overhead: The multiple steps often involve several systems, which introduces complexity. Also, ETL workflows must be updated regularly as data sources evolve or business needs change. This leads to an ongoing maintenance overhead.

How ETL is being used

ETL is a critical process for data integration and analytics. Some common use cases include:

• Data warehousing: ETL pipelines are used to extract data from source systems such as databases, files and APIs, transform the data into a consistent format and then load it into a data warehouse.
• Business intelligence: ETL is used to populate data marts and data warehouses used by BI tools.
• Data migration: ETL is often used during data migrations when an organization needs to transition from one system to another.
• Data integration: ETL makes possible the seamless integration of data from different sources.
• Data cleansing and enrichment: ETL pipelines are also used to clean and standardize data. They also enrich data by incorporating missing information.
• Batch processing: ETL jobs often run at scheduled intervals and process large amounts of data, ensuring that the data warehouse remains updated.
• Data governance and compliance: ETL is a critical tool for the enforcement of data governance policies. Data can be encrypted during the transformation process to comply with data laws.
• Real-time ETL: While traditional ETL is mostly done on schedule intervals (batches) real-time ETL is now used for scenarios that require instant updates, such as stock market updates.
• Cloud data pipelines: ETL tools are used in cloud environments to facilitate the movement of data between cloud platforms and on-premises storage.

ETL vs. ELT

It is important to distinguish ETL from ELT. In ELT (extract, load, transform), raw data extracted from various sources is loaded directly into the target system, such as a data warehouse or lake, and transformation is the final step. The choice between ETL or ELT comes down to the organization’s needs, data volume, complexity, infrastructure, performance considerations and any desired workflows.

SEE: For more information, check out our comparison of ETL and ELT.

Consider ETL tools to help with your data migration

ETL tools are used to migrate data from one system to another, be it a database management system, a data warehouse or even an external storage system. These tools can run in the cloud or on-premises and often come with an interface that creates a visual workflow when carrying out various extraction, transformation and loading processes.

Below are our top five picks for cloud-based, on-premises and hybrid, and open-source ETL tools: