In a data-driven world, an efficient process for moving and transforming data for analysis is critical to business growth and innovation. Loading a data warehouse can be an extremely time-consuming process. The process of extracting, loading, and transforming (ELT) data streamlines the tasks of modern data warehousing and managing big data so that businesses can focus on mining their data for actionable insights.

Extract/load/transform (ELT) is the process of extracting data from one or multiple sources and loading it into a target data warehouse. Instead of transforming the data before it’s written, ELT takes advantage of the target system to do the data transformation. This approach requires fewer remote sources than other techniques because it needs only raw and unprepared data.

ELT is an alternative to the traditional extract/transform/load (ETL) process. It pushes the transformation component of the process to the target database for better performance. This capability is very useful for processing the massive data sets needed for business intelligence (BI) and big data analytics.

Because it takes advantage of the processing capability already built into a data storage infrastructure, ELT reduces the time data spends in transit and boosts efficiency. Take a closer look at the differences between ETL vs ELT processes

Though the ELT process has been in practice for some time, it is gaining popularity with the more widespread use of Hadoop and cloud-native data lakes.

How ELT Works

It is becoming increasingly common for data to be extracted from its source locations, then loaded into a target data warehouse to be transformed into actionable business intelligence. This process consists of three steps:

  1. Extract — This step works similarly in both ETL and ELT data management approaches. Raw streams of data from virtual infrastructure, software, and applications are ingested either in their entirety or according to predefined rules.
  2. Load — Here is where ELT branches off from its ETL cousin. Rather than deliver this mass of raw data and load it to an interim processing server for transformation, ELT delivers it directly to the target storage location. This shortens the cycle between extraction and delivery.
  3. Transform — The database or data warehouse sorts and normalizes the data, keeping part or all of it on hand and accessible for customized reporting. The overhead for storing this much data is higher, but it offers more opportunities to mine it for relevant business intelligence in near real-time.

Benefits of ELT

The explosion in the types and volume of data that businesses must process can put a strain on traditional data warehouses. Using an ETL process to manage millions of records in these new formats can be time-consuming and costly. ELT offers a number of advantages, including:

  • Simplifying management — ELT separates the loading and transformation tasks, minimizing the interdependencies between these processes, lowering risk, and streamlining project management.
  • Future-proofed data sets — ELT implementations can be used directly for data warehousing systems, but oftentimes ELT is used in the data lake approach in which data is collected from a range of sources. This, combined with the separation of the transformation process, makes it easier to make future changes to the warehouse structure.
  • Leveraging the latest technologies — ELT solutions harness the power of new technologies in order to push improvements, security, and compliance across the enterprise. ELT also leverages the native capabilities of modern cloud data warehouses and big data processing frameworks.
  • Lowering costs — Like most cloud services, cloud-based ELT can result in lower total cost of ownership, because an upfront investment in hardware is often unnecessary.  
  • Flexibility — The ELT process is adaptable and flexible, so it’s suitable for a variety of businesses, applications, and goals.
  • Scalability — The scalability of a cloud infrastructure and hosted services like integration platform-as-a-service (iPaaS) and software-as-a-service (SaaS) give organizations the ability to expand resources on the fly. They add the compute time and storage space necessary for even massive data transformation tasks.

Although it is still evolving, ELT offers the promise of unlimited access to data, less development time, and significant cost savings. In these and other ways, the cloud is redefining data integration.

ETL vs ELT: Understanding the Difference

The primary differences between ETL and ELT are how much data is retained in data warehouses and where data is transformed.

With ETL, the transformation of data is done before it is loaded into a data warehouse. This enables analysts and business users to get the data they need faster, without building complex transformations or persistent tables in their business intelligence tools.

Using the ELT approach, data is loaded into the warehouse or data lake as is, with no transformation before loading. This makes jobs easier to configure because it only requires an origin and a destination.

The ETL and ELT approaches to data integration differ in several key ways.

  • Load time — It takes significantly longer to get data from source systems to the target system with ETL.
  • Transformation time — ELT performs data transformation on-demand, using the target system's computing power, reducing wait times for transformation.
  • ComplexityETL tools typically have an easy-to-use GUI that simplifies the process. ELT requires in-depth knowledge of BI tools, masses of raw data, and a database that can transform it effectively.
  • Data warehouse support — ETL is a better fit for legacy on-premise data warehouses and structured data. ELT is designed for the scalability of the cloud.
  • Maintenance — ETL requires significant maintenance for updating data in the data warehouse. With ELT, data is always available in near real-time.

Both ETL and ELT processes have their place in today’s competitive landscape, and understanding a business’ unique needs and strategies is key to determining which process will deliver the best results.

ELT and Data Lakes: The Future of Data Integration?

Modern, cloud-based infrastructure technologies offer large amounts of data storage and scalable computing power at lower costs, making it possible to keep petabytes of data in large and expandable data lakes, and process it quickly on-demand. The proliferation of data lakes has made it possible for more organizations to move from ETL to ELT.

Data lakes offer major benefits for organizations migrating big data and big data processes from on-premises to the cloud. They enable data to be kept in a more flexible format for future use, along with identifiers and metadata tags for faster retrieval. They also offer fast load times.

ELT appears to be the future of data integration, offering many advantages over ETL, which is an older, slower process. Data volume has grown exponentially for organizations, and ETL tools cannot efficiently handle the integration of all this data into a repository for analysis. ELT delivers better agility and less maintenance, making it a cost-effective way for businesses of all sizes to take advantage of cloud-based data storage such as data lakes.

Getting Started with ELT

Data plays a critical role in every business operation; for it to be valuable, it must be moved and prepared for use. ELT is an essential piece of the data integration process, providing a different approach to data movement than the traditional ETL process.

Talend provides some of the world’s largest companies with the data integration tools they need to transform their businesses. Open Studio for Big Data is a free, globally supported platform that empowers companies to take charge of even the biggest ELT jobs.

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