ETL (extract, transform and load) gathers business intelligence, but it isn’t fast enough for specific use cases. Enterprises need the latest data 24/7, and small latency and processing issues can have a huge impact on query performance.

ELT (extract, load and transform) is faster, aggregating only the desired information on demand to prepare it for analysis. Does it mean the end of ETL?

Find out with this ETL vs. ELT breakdown.

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What This Article Covers:

• What Is ETL?
• What Is ELT?
• Use Cases
• Differences
• Best Practices
• How To Choose the Best Software
• Wrapping Up

What Is ETL?

ETL is a set of processes for extracting information from source systems, transforming and cleansing it, and loading it into the target repository. In the words of Bill Inmon, the father of data warehousing, ETL is the process of converting application data to enterprise data.

ETL tools are middleware that does this, loading the information into warehouses and keeping it in sync with source systems. But that’s not all. ETL programs support backend processes in data warehouse solutions, while the front interface serves consumer applications, including BI and reporting tools.

Data Integration vs. ETL

Data integration is a set of activities that involve combining information from various sources to present a unified view. People equate it with ETL, though the two are different. Data integration includes ETL workflows, and many data integration tools have this capability, hence the confusion.

The aim of integration is consolidated views, while ETL enables data transformation and availability in a warehouse. Big data integration was another big change in pipeline technology as vendors began offering fast and flexible processing of disparate data types.

Stages

Extraction involves replicating information from the source to target systems through attribute mapping, and in an ETL tool, it runs in batches in an offline mode.

Defining mapping rules is a challenging task. It’s like walking on a tightrope — you want access to the sources but mustn’t interfere with routine database processes. Databases are often legacy systems, and DBAs (database administrators) can’t allow configuration changes to facilitate ETL for you.

Transformation is the next stage and involves changes from the simple to the complex — renaming columns, joining tables, fixing incorrect data and removing what you don’t need. Your ETL tool organizes the information into normalized tables, which is the format your warehouse accepts.

Refer to our cleaning tools list for more information.

In the loading phase, your ETL tool moves the information to the warehouse, which accommodates it in dimensional tables, mapping it to schemas which are visual representations of database design. For existing records, it updates the information incrementally; else, a new record is created.

Primary Benefits

• With ready-to-go data, you can perform faster reporting and analysis.
• ETL includes data quality management, providing total transparency about data lineage. It becomes easy to apply compliance and access protocols to the data before loading it to the warehouse.
• ETL saves warehouse storage by dumping raw data in the staging area and then organizing it properly before uploading to the warehouse.
• Reverse ETL – sending data back to business applications – supports revenue-generating strategies for segmenting customers, scoring leads and improving operational reporting. You can track critical metrics like customer lifetime value (CLV) and ARR (annual recurring revenue).

Reverse ETL solves the last mile problem in the modern data stack, as Atlan co-founder Prukalpa Sankar says. It’s a front-end process supporting operational analytics — sales, marketing, customer support and product development. Hevo, Snowflake, Rudderstack and Airbyte offer ingestion and reverse ETL in the same tool.

Limitations

• ETL can be inflexible when tuning running integration pipelines and executing ad hoc workflows, as you must map all the parameters every time. It can slow you down.
• Transformation workflows aren’t transparent — only users performing them have visibility.
• Performance can lag as batch refreshes run in offline mode.
• Scalability can be an issue when dealing with large volumes, especially when you’re on a clock and need instant insights. Data can get backed up, overwhelming batch update workflows.

Big data analytics and the need for instant insights put many demands on ETL. Is it up to the challenge?

But first, let’s define ELT.

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What is ELT?

ELT is the pipeline technique of ingesting and loading raw information into the warehouse and transforming only the desired datasets. There’s no staging area — the ELT code uses the warehouse engine for transformations. Normalizing data, allocating schemas and building dimensional tables — all the good stuff happens within the warehouse.

ELT checks all the right boxes for buyers in being cloud-based and supporting data lakes and warehouses.

Data lakes offer flexibility when your business deals with all information types. But there’s more to them than big data — they support predictive analytics and real-time applications using artificial intelligence and machine learning (ML).

Many ELT tools provide automated data ingestion and replication via change data capture (CDC). Setting up a warehouse is made simpler with data warehouse automation solutions (DWA) that allow provisioning of the data structure, business logic, setup and documentation. Fivetran and Stitch Data are popular ELT tools.

Like puzzle pieces, various tools make ELT and ETL possible, helping you avoid long-winded workarounds and manual tasks. These include NoSQL database management systems and big data platforms like Apache Hadoop and Spark.

Stages

Whether transformation happens in the staging layer or within the warehouse determines the tools you’ll use. The data structure, queries and optimization engines will be different for ELT.

There’s another issue — that of security. When sensitive data lands in the warehouse, governance and security become paramount. There are no guarantees the warehouse solution will inherit the security protocols of source systems. It puts the onus on you to ensure data integrity.

Your ELT system uses the warehouse engine to populate tables per the business logic using schemas, dimensions and measures. ELT speeds up the data pipeline by performing multiple tasks in parallel.

Primary Benefits

• The technology is lightweight and economical, being cloud-based.
• Adding new sources and data types, like streaming data, is straightforward and quick as ELT bypasses the staging area, reducing processing overheads.
• Decoupling ingestion from transformation reduces the burden on source systems.
• ELT processes scale with large volumes, thanks to the warehouse engine.
• ELT workflows are transparent and visible to users during the pipeline.

Limitations

ELT isn’t a silver bullet, though. Having large volumes of raw data in the warehouse poses a few challenges.

• Performance issues can crop up as complex queries require trawling through large volumes and transforming data on-the-fly.
• Keeping the warehouse synced with production databases isn’t easy when raw and formatted data coexist within the same ecosystem.

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Use Cases

Real-life examples give a better perspective of how the technology works.

ETL: CloudFactory

The company, a global leader in ML-driven data enrichment, wanted to improve delivery timelines and product quality. It flagged performance improvement and capacity planning as essential cogs in the wheel that needed a little help.

The challenge was consolidating performance data by analyzing the information in its vast source ecosystem. Automating ETL by adding Xplenty and its readymade connectors to their tech stack helped them get a handle on their large data volumes.

Analysts and decision-makers can plan staffing and drive best practices by identifying top performers across teams.

Xplenty is a data integration software on the integrate.io platform.

ELT: Jaguar Land Rover (JLR)

The company struggled with complex and time-consuming data access from source systems, including legacy applications. Traditional ETL tools and manual extraction couldn’t cope with the increasing demand for real-time information. JLR adopted DataOps, combining it with Qlik Replicate to fill its data lake using change data capture.

Additionally, it provided wide coverage on all key systems within JLR’s estate. The company can now respond quickly to the regulatory demands of global markets by exposing SAP data through external websites in near real-time.

The tax team can consume and analyze the information required for VAT submissions within minutes instead of hours or days.

Differences

In an ETL vs. ELT comparison, the two technologies are head-to-head. ELT offers speed with on-demand transformations, while ETL poses stiff competition with ready-to-go data.

As for the waiting period between extraction and analysis, it exists in both techniques. In ELT, it occurs later, but it’s still there.

• ETL is suitable for smaller datasets, while ELT is better for larger volumes. Batch refreshes in ETL might overwhelm the transform-and-update pipeline. If the ETL run exceeds the duration between two refreshes, you risk getting stuck with older data.

Creating the consumption layer for ETL. Source

• ETL is high-maintenance and effort-intensive. This belief stems from the transformation phase happening earlier than loading. By contrast, fully automated ELT systems sail you through the extraction and loading phases through automation.
• ETL systems provide better data quality and security. They apply transformation and standardization before loading, so data is prepped and secure when it lands in the repository. With ELT, sensitive data can reside in the warehouse without any masking or encryption — it’s why you need to compensate with stricter security rules.
• ETL is cost-effective — many ETL vendors charge a flat fee per connector, not for processing workloads. ELT software vendors offer their products at seemingly low prices, charging for extraction and loading while shifting the processing costs to the data warehouse.

Despite these differences, ETL retains its position of importance in data management. But ELT isn’t far behind, having proved its value in many industries. Both ETL and ELT can support AI-ML pipelines.

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Best Practices

To effectively implement ETL/ELT, a holistic approach accounting for technical expertise, business needs and system limitations goes a long way. With careful planning and execution, you can optimize your ETL processes to realize significant benefits.

• Technical experts should proactively identify and resolve potential bottlenecks in code.
• Deciding when to fully load data or incrementally update it in the warehouse is business-critical.
• Partitioning large tables can significantly enhance the performance of relational databases.
• Caching data in memory is a recommended practice to improve response times, but it depends on your warehouse’s hardware capacity.
• Defining business rules for data sourcing and transformation maintains a lean repository and optimizes ETL performance.
• Parallel processing enhances performance, but CPU limitations and sequential workflows can hamper implementation. Adding Hadoop to your tech stack might work as its distributed file system (HDFS) is adept at replicating data across cluster nodes.

Ready to get started with your software search? Our free ETL requirements template can help organize your data pipeline needs in one place.

How To Choose the Best Software

Choosing an ETL platform depends on your organizational needs, IT structure and budget. Many database providers offer ETL with the primary product free of cost, which might be worth checking out.

ETL is great for organizations that need to unify and sync data formatted differently across sources. Batch updates are indispensable when migrating large volumes across systems.

Execution graph of transform jobs in a Google Cloud Platform data pipeline. Source

ELT can handle structured and unstructured data types, especially when you need time-sensitive insights. Do you, and how frequently?

Keeping an open mind regarding add-ons helps. Hadoop doesn’t directly read relational data, so tools like Sqoop are necessary for data transfers.

Migrating from ETL to ELT can involve significant scaling, including configuration changes and modifications in the tech stack, if need be.

Confused about how to start your software search?

Our Lean Selection Methodology can guide you through the process.

Alternative Technologies

Parallel to ELT, many ETL vendors promise real-time ETL inspired by ETLT (extract-transform-load-transform) solutions.

The workflows include lightweight transforms on ingested data after loading it directly into the warehouse — no intermediate steps or storage involved. It can serve your live reporting needs while processing the remaining data in the staging area.

But, it’s near-real-time at best, as the time between the actual transaction and its propagation to the warehouse can vary from five to fifteen minutes. If you can live with this delay, it might be worth checking out.

There’s the question of overheads, though — any technology that proposes in-motion transforms can burden source systems. A trial run or proof-of-concept can give you a better idea of if the technology fits your needs.

Informatica mentions pushdown optimization as the technology behind ELT. It involves packing the standardization code, mapping logic into SQL queries and pushing them down into the source or target system on demand. Defining business rules on the fly speeds up the entire transformation process.

In his LinkedIn article The Death of ETL, Inmon stresses that transformation is the linchpin of enterprise reporting and analytics and warns us about falling for ETL or ELT products without a robust transformation layer.

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Wrapping Up

The ETL vs. ELT debate isn’t going away anytime soon, and neither is the industrywide quest for a perfect ETL solution that provides live and low-cost insights. The competition between ETL and ELT spawned many software programs serving part or all of the data pipeline, and enterprises are spoilt for choice.

And it’s fueled a healthy discussion and tons of research, which holds out hope for more resilient ETL solutions.

Which ETL software have you used? What challenges did you face? Are you considering shifting to ELT? Let us know in the comments below.