What is extract, transform, load?

ETL stands for “extract, transform, and load.”

 

The process of ETL plays a key role in data integration strategies. ETL allows businesses to gather data from multiple sources and consolidate it into a single, centralized location. ETL also makes it possible for different types of data to work together.

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A typical ETL process collects and refines different types of data, then delivers the data to a data warehouse such as Redshift, Azure, or BigQuery. ETL also makes it possible to migrate data between a variety of sources, destinations, and analysis tools. As a result, the ETL process plays a critical role in producing business intelligence and executing broader data management strategies.

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Three steps make up the ETL process and enable data to be integrated from source to destination. These are data extraction, data transformation, and data loading.

Step 1: Extraction

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Few businesses rely on a single data type or system. Most manage data from a variety of sources and use a number of data analysis tools to produce business intelligence. To make a complex data strategy like this work, the data must be able to travel freely between systems and apps.

Before data can be moved to a new destination, it must first be extracted from its source. In this first step of the ETL process, structured and unstructured data is imported and consolidated into a single repository. Raw data can be extracted from a wide range of sources, including:

 

• Existing databases and legacy systems

• Cloud, hybrid, and on-premises environments

• Sales and marketing applications

• Mobile devices and apps

• CRM systems

• Data storage platforms

• Data warehouses

• Analytics tools

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Although it can be done manually, hand-coded data extraction can be time-intensive and prone to errors. ETL tools automate the extraction process and create a more efficient and reliable workflow.

Step 2: Transformation

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During this phase of the ETL process, rules and regulations can be applied that ensure data quality and accessibility. You can also apply rules to help your company meet reporting requirements. The process of data transformation is comprised of several sub-processes:

 

• Cleansing — inconsistencies and missing values in the data are resolved. 

• Standardization — formatting rule are applied to the data set.

• Deduplication — redundant data is excluded or discarded.

• Verification — unusable data is removed and anomalies are flagged.

• Sorting — data is organized according to type.

• Other tasks — any additional/optional rules can be applied to improve data quality.

 

Transformation is generally considered to be the most important part of the ETL process. Data transformation improves data integrity and helps ensure that data arrives at its new destination fully compatible and ready to use.

Step 3: Loading

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The final step in the ETL process is to load the newly transformed data into a new destination. Data can be loaded all at once (full load) or at scheduled intervals (incremental load).

Full loading — In an ETL full loading scenario, everything that comes from the transformation assembly line goes into new, unique records in the data warehouse. Though there may be times this is useful for research purposes, full loading produces data sets that grow exponentially and can quickly become difficult to maintain.

Incremental loading — A less comprehensive but more manageable approach is incremental loading. Incremental loading compares incoming data with what’s already on hand, and only produces additional records if new and unique information is found. This architecture allows smaller, less expensive data warehouses to maintain and manage business intelligence.

 
ETL and business intelligence

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Data strategies are more complex than they’ve ever been, and companies have access to more data from more sources than ever before. ETL makes it possible to transform vast quantities of data into actionable business intelligence.

Consider the amount of data available to a manufacturer. In addition to the data generated by sensors in the facility and the machines on an assembly line, the company also collects marketing, sales, logistics, and financial data.

 

All of that data must be extracted, transformed, and loaded into a new destination for analysis. In this scenario, ETL helps create business intelligence by:

 

Delivering a single point-of-view

 

Managing multiple data sets demands time and coordination, and can result in inefficiencies and delays. ETL combines databases and various forms of data into a single, unified view. This makes it easier to analyze, visualize, and make sense of large data sets.

Providing historical context

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ETL allows an enterprise to combine legacy data with data collected from new platforms and applications. This produces a long-term view of data, so that older data sets can be viewed alongside more recent information.

 

Improving efficiency and productivity

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ETL Software automates the process of hand-coded data migration. As a result, developers and their teams can spend more time on innovation, and less time managing the painstaking task of writing code to move and format data

Building your ETL strategy

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ETL can be accomplished in one of two ways. In some cases, businesses may task their developers with building their own ETL. However, this process can be time-intensive, prone to delays, and expensive.

Most companies today rely on an ETL tool as part of their data integration process. ETL tools are known for their speed, reliability, and cost-effectiveness, as well as their compatibility with broader data management strategies. ETL tools also incorporate a broad range of data quality and data governance features.

When evaluating an ETL tool, you’ll want to consider the number and variety of connectors you’ll need, as well as its portability and ease of use. You’ll also need to determine if an open-source tool is right for your business, since these typically provide more flexibility and help users avoid vendor lock-in.