What Is ETL?

Extract, transform, and load (ETL) is an important process in data warehousing when businesses need to pull data from multiple sources and store it in a centralized location. The process logic and infrastructure design will depend on the business requirements, data being stored, and whether the format is structured or unstructured.

What Is ETL?

Data pulled from various sources must be stored in a specific form to allow applications, machine learning, artificial intelligence, and analytics to work with it. The ETL process is a group of business rules that determine the data sources used to pull data, transform it into a specific format, and then load it into a database. Data can be structured or unstructured, or it could be both.

After the ETL process happens, the data is stored in a data warehouse where administrators can further manage it. Administrators responsible for the databases storing ETL data manage logging, auditing, and backups. The log data for ETL events could also go through its own data pipeline before being stored in a data warehouse for administrative analytics.

The ETL Process

ETL has three steps: extract, transform, and load. Database administrators, developers, and cloud architects usually design the ETL process using business rules and application requirements. Design of an ETL process addresses the following three steps:

• Extract: Raw data for extraction could come from one or several sources. Sources could be from an API, a website, another database, IoT logs, files, email, or any other ingestible data format. Because sources could have various formats, the first step in ETL pulls data from a source for the next step.
• Transform: Business rules and the destination storage location define transformation design. Data must be formatted, filtered, and validated before it can be sent to the data warehouse. Duplicate data can skew analytic results, so duplicate line items are removed prior to storing them. Data is formatted so that it can be stored. For example, a phone number might be stored with or without hyphens, so the transformation process either adds or removes hyphens prior to being sent to storage.
• Load: After transformation, data is sent to the data warehouse for storage. Data must be preserved and duplicates avoided, so the load step must take into account incremental changes every time the ETL process executes. ETL often runs several times a day for larger corporations, so only new data is added without affecting current application data already stored in the database.

Benefits of ETL

Once an ETL process is designed, it runs automatically throughout the day. Some ETL processes could be a weekly or monthly occurrence, and most database engines offer a scheduler that runs on the server to run tasks at a set time. A well-designed ETL process does not need many changes and can import data from various sources without manual interaction.

Raw data without any transformation is generally useless for analytics, especially if your business uses similar data from several sources. For example, a business working with traffic analysis could pull data from several different government sources. It’s highly likely that all sources will create duplicate records, but an ETL process will take the data, remove duplicates, and format the data for internal analytic applications. Businesses could pull data from numerous locations and automatically prepare it for internal analytics, which also powers future business decisions and product launches.

ETL speeds up data updates, so it benefits businesses that need to work with current or real-time data. Traditionally, data imports were batched and ETL was slow. Businesses might not see changes to data for several hours, but current ETL technology provides updates to data so that analytics can reflect recent changes to trends.

ETL Tools and Technologies

For large data pipelines, most organizations use custom tools and scripts for ETL. Database engines often come with their own ETL features so that businesses can import data. The way you store data depends on if you need unstructured or structured data. Structured data requires more formatting than unstructured data, so any out-of-the-box tools must integrate with your chosen database platform.

A few tools for ETL:

• Talend: Offers an open source GUI for drag-and-drop data pipeline integration
• Informatica PowerCenter: Gives end users the tools to import data and design their own data pipelines for business projects
• AWS Glue: Enables you to design ETL from unstructured and structured data to store on S3 buckets
• Google Cloud Dataflow: Enables you to create serverless ETL processes to store data on the Google Cloud Platform (GCP)

Best Practices for ETL Implementation

The key to good ETL design is performance and accuracy. Performance often relies on underlying infrastructure, so it’s important to have a data warehouse that can scale and keep up with increasing loads. Structured data often takes more time to transform due to the many table constraints, but solutions such as FlashArray™ are built for large data imports and ensure on-premises pipelines continue to run quickly.

Always design ETL processes for scale and the unknown. It’s highly possible that you’ll eventually import a record that cannot be transformed. Any errors should be logged and records stored for further review. It could mean that there’s a bug in your ETL or the design misses an edge case that can be remediated with changes to ETL code.

Not all ETL processes work with physical servers, so solutions such as Portworx® handle virtualized and containerized databases and analytics. Containerized services must scale as more data is imported and work with common orchestration tools. Portworx integrates with orchestration tools including Kubernetes for dynamic and consistently updated pipelines.

Challenges and Solutions in ETL

Because data sources and business requirements are continually changing, administrators responsible for designing ETL have challenges associated with scale, updates, and quality control. Scaling challenges usually come from storage space limitations, so administrators can remediate this issue with storage that scales with an increase in data storage requirements.

Challenges with changing business requirements often fall under maintenance. A data source might change the way data is stored, or developers might make changes to an application requiring changes to transformation or load structures. Without having any documentation from third-party data sources to alert administrators, changes to data storage or load requirements don’t present themselves until errors occur in the ETL process. Logging and alerts help administrators identify issues early so that they can make changes to ETL coding. Early changes reduce the impact of errors on business productivity and revenue.

The design of an ETL process is one of the most difficult tasks, but it can be easier when administrators speak to stakeholders and ensure that business rules are included. Redesigning and refactoring an ETL design can delay deployment and add unnecessary overhead. Document all business rules so that every case can be included in an ETL design to avoid excessive rewrites.

Keep various ETL processes separated and independent of each other. This solution ensures that the entire ETL process does not fail if one component fails. For example, if an external API crashes, the extraction of data from all other sources still completes until the API is available again. It’s also possible to create multiple ETL schedules if necessary. If you work with multiple cloud platforms, Pure Storage cloud storage supports AWS, Azure, GCP, and other major platforms.

ETL vs. ELT 

It's important to note that ETL can be resource-intensive and may introduce some latency in data availability, especially when dealing with large data sets. If real-time or near real-time data processing is a critical requirement, other data integration methods like change data capture (CDC) or streaming data pipelines may be more suitable.

Additionally, in recent years, ELT (extract, load, transform) has become a popular alternative to ETL, especially in cloud-based data environments where data transformation can be performed within the target data storage system. ELT can be more cost-effective and scalable for some use cases, but the choice between ETL and ELT depends on your specific requirements and the technologies you’re using.

Conclusion

Designing an ETL solution takes time, but don’t forget to build a system that scales with increasing data storage. One of the easiest challenges to solve is data storage capacity, and Pure Storage solutions are built for data warehousing for unstructured and structured data.

Other challenges can be solved with good design standards, documentation, and quality assurance testing. You might find that some tools can help with design, but ETL is often customized for the business. Test a small sample of data in a staging environment, and expect to continually maintain ETL coding as new business requirements are introduced.