Understanding ETL And Effectively Using It

What it is and why it matters

ETL gained popularity in the 1970s when organizations began using multiple data repositories, or databases, to store different types of business information. The need to integrate data that was spread across these databases grew quickly. Over time, the number of data formats, sources and systems has expanded tremendously. ETL has become the standard method for taking data from disparate sources and transforming it before loading it to a target source, or destination.

ETL stands for extract, transform and load. ETL is a type of data integration process referring to three distinct, but interrelated steps used to synthesize raw data from its source to a data warehouse, data lake or relational database. From various data sources (that are not optimized for analytics), ETL loads data first into the staging server and then into the target system, whereas extract, load and transform (ELT) loads data directly into the target system. ELT is an alternate but related approach designed to push processing down to the database for improved performance.

The three distinct steps of ETL

Extract

During extraction, ETL identifies the data and copies it from its sources, so it can transport the data to the target datastore. The data can come from structured and unstructured sources, including documents, emails, business applications, databases, equipment, sensors, third parties, and more.

Transform

Because the extracted data is raw in its original form, it needs to be mapped and transformed to prepare it for the eventual datastore. In the transformation process, ETL validates, authenticates, deduplicates, and/or aggregates the data in ways that make the resulting data reliable and queryable.

Load

ETL moves the transformed data into the target datastore. This step can entail the initial loading of all the source data, or it can be the loading of incremental changes in the source data. You can load the data in real time or in scheduled batches.

Benefits of ETL

- Transforms data from multiple sources and loads it into various targets
- Provides deep historical context for businesses
- Allows organizations to analyze and report on data more efficiently and easily
- Increases productivity as it quickly moves data without requiring the technical skills of having to code it first
- Evolves and adapts to changing technology and integration guidelines

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Critical ETL components

Regardless of the exact ETL process you choose, there are some critical components you'll want to consider:
- Support for change data capture (CDC): Incremental loading allows you to update your analytics warehouse with new data without doing a full reload of the entire data set. We say more about this in the ETL Load section.
- Auditing and logging: You need detailed logging within the ETL pipeline to ensure that data can be audited after it's loaded and that errors can be debugged.
- Handling of multiple source formats: To pull in data from diverse sources such as Salesforce's API, your back-end financials application, and databases such as MySQL and MongoDB, your process needs to be able to handle a variety of data formats.
- Fault tolerance: In any system, problems inevitably occur. ETL systems need to be able to recover gracefully, making sure that data can make it from one end of the pipeline to the other even when the first run encounters problems.
- Notification support: If you want your organization to trust its analyses, you have to build in notification systems to alert you when data isn't accurate. These might include:
- Proactive notification directly to end users when API credentials expire
- Passing along an error from a third-party API with a description that can help developers debug and fix an issue
- If there's an unexpected error in a connector, automatically creating a ticket to have an engineer look into it
- Utilizing systems-level monitoring for things like errors in networking or databases
- Low latency: Some decisions need to be made in real time, so data freshness is critical. While there will be latency constraints imposed by particular source data integrations, data should flow through your ETL process with as little latency as possible.
- Scalability: As your company grows, so will your data volume. All components of an ETL process should scale to support arbitrarily large throughput.
- Accuracy: Data cannot be dropped or changed in a way that corrupts its meaning. Every data point should be auditable at every stage in your process.

ETL tools

Once upon a time, organizations wrote their own ETL code, but there are now many open source and commercial ETL tools and cloud services to choose from. Typical benefits of these products include the following:
- Comprehensive automation and ease of use: Leading ETL tools automate the entire data flow, from data sources to the target data warehouse; many tools recommend rules for extracting, transforming, and loading the data.
- A visual, drag-and-drop interface: This can be used for specifying rules and data flows.
- Support for complex data management: This includes assistance with complex calculations, data integrations, and string manipulations.
- Security and compliance: The best ETL tools encrypt data both in motion and at rest and are certified compliant with industry or government regulations like HIPAA and GDPR.

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How It All Works

1. Structured query language (SQL) is the most common method of accessing and transforming data within a database. Transformations, business rules and adapters
2. After extracting data, ETL uses business rules to transform the data into new formats. The transformed data is then loaded into the target.
3. Data mapping is part of the transformation process. Mapping provides detailed instructions to an application about how to get the data it needs to process. It also describes which source field maps to which destination field. An application or ETL process using that data would have to map these same fields or attributes from the source system into the format required by the destination system.
4. Scripts: ETL is a method of automating the scripts (set of instructions) that run behind the scenes to move and transform data. Before ETL, scripts were written individually in C or COBOL to transfer data between specific systems. This resulted in multiple databases running numerous scripts. Early ETL tools ran on mainframes as a batch process. ETL later migrated to UNIX and PC platforms. Organizations today still use both scripts and programmatic data movement methods.
5. ETL versus ELT: In the beginning, there was ETL. Later, organizations added ELT, a complementary method. ELT extracts data from a source system, loads it into a destination system and then uses the processing power of the source system to conduct the transformations. This speeds data processing because it happens where the data lives.
6. Data quality: Before data is integrated, a staging area is often created where data can be cleansed, data values can be standardized (NC and North Carolina, Mister and Mr., or Matt and Matthew), addresses can be verified and duplicates can be removed. Many solutions are still standalone, but data quality procedures can now be run as one of the transformations in the data integration process.
7. Scheduling and processing: ETL tools and technologies can provide either batch scheduling or real-time capabilities. They can also process data at high volumes in the server, or they can push down processing to the database level. This approach of processing in a database as opposed to a specialized engine avoids data duplication and prevents the need to use extra capacity on the database platform.
8. Batch processing.ETL usually refers to a batch process of moving huge volumes of data between two systems during what's called a 'batch window.' During this set period of time - say between noon and 1 p.m. - no actions can happen to either the source or target system as data is synchronized. Most banks do a nightly batch process to resolve transactions that occur throughout the day.
9. Web services are an internet-based method of providing data or functionality to various applications in near-real time. This method simplifies data integration processes and can deliver more value from data, faster.
10. Master data management (MDM) is the process of pulling data together to create a single view of the data across multiple sources. It includes both ETL and data integration capabilities to blend the data together and create a 'golden record' or 'best record.'
11. Data Virtualization is an agile method of blending data together to create a virtual view of data without moving it. Data virtualization differs from ETL, because even though mapping and joining data still occurs, there is no need for a physical staging table to store the results. That's because the view is often stored in memory and cached to improve performance. Some data virtualization solutions provide dynamic data masking, randomization and hashing functions to protect sensitive data from specific roles or groups.
12. Event stream processing and ETL: When the speed of data increases to millions of events per second, event stream processing can be used to monitor streams of data, process the data streams and help make more timely decisions. An example in the energy space is using predictive analytics on streams of data to detect when a submersible pump is in need of repair to reduce both downtime and the scope and size of damage to the pump.

Conclusion

ETL is the common steps to integrate data and transform it to compatible in targeted destination system. Different ETL tools can be best suited for different needs. Tool types aren't mutually exclusive; Your company's particular requirements should guide your choice. The importance of ETL to an organization's data warehousing efforts can't be overstated. Without ETL tools to pull data together and render it usable, data warehousing would be difficult, if not impossible. ETL maintains the integrity of data to be used for decision making and reporting.

I hope this article has assisted in giving you a fresh perspective on ETL while enabling you to understand it better and more effectively use it going forward.

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