ETL vs ELT: Choosing a Data Integration Approach

ETL and ELT are two orders for the same three jobs: extracting data from source systems, transforming it and loading it into a shared place, such as a data warehouse. The difference that matters is where and when the data transformation happens. In ETL, that happens before loading, while in ELT it happens inside the target after the data lands. This guide to ETL vs ELT gives a short table, six questions to answer about your own data and a way to use both. It names no winner and no tool. It sits under the Signisys use case on joining up data across business systems.

ETL vs ELT: Where the Data Transformation Happens

Three short definitions come first:

• Extract: copy data out of a source system.
• Transform: change the data into the shape the reports need.
• Load: write the data into the target, the destination it is meant for.

A data warehouse is one kind of target: a database designed for analysis across business systems.

AWS’s comparison of ETL and ELT describes ETL as transforming data on a secondary processing server before loading it. ELT, as AWS describes it, loads raw data first and transforms it inside the target. IBM’s explanation of ELT versus ETL draws the same line, with the ETL changes made in a staging area before loading.

In AWS’s description, data transformation covers changing formats and types and removing duplicate or inconsistent records. IBM’s list adds validating data and removing, encrypting or protecting sensitive data. Both routes can end with transformed data in the same target. What changes is where the work happens, and at what point a mistake becomes visible.

ETL vs ELT: What Changes When the Order Changes

The four rows below set the two orders of data transformation side by side. Each cell names a step, a place or a person, not a product.

AWS says the transform-first route needs analysts involved from the start, to plan the reports and define the data structures. However, no row here says one order is better. That depends on your own data, which the next section asks about.

ETL vs ELT: Six Questions About Your Own Data

In Signisys’ working description, six questions decide the order for a given flow, and they are ours rather than a standard. Answer them for your own data. Each one notes which way an answer points.

1. Where will the shared data sit? The target might be a lake, a database already in use or a data warehouse. If your team can transform data inside that target, loading first is open to you; if not, transforming first is the route left.
2. What must be removed or masked before it lands? Anything on that list points to transforming first, at least for those fields.
3. How current must the figures be when someone uses them? Write the answer down. It sets the timing of each load, whichever order you choose.
4. Who writes and maintains the data transformation logic, and with what skills? If those people work inside the target, that points to loading first. If a separate team prepares data before it moves, it points to transforming first.
5. How easily must someone trace a wrong number to its source? Raw data kept in the target lets someone compare a figure with what arrived, which points to loading first.
6. Will the raw data serve a purpose nobody has named yet? If so, loading the raw data first keeps that option open.

ETL vs ELT for Sensitive and Personal Data

The question here is what the team must remove, mask or encrypt before data lands, and who in the firm signs that off. AWS says that in ETL, developers have to build custom solutions, like masking personally identifiable information, to protect it. For the load-first route, AWS points to built-in security features inside the data warehouse, such as granular access control. IBM, for its part, says ETL can be preferable when data must be standardised or protected before it reaches the target. Both are vendor descriptions, not rules.

Which law applies to your firm’s personal data is a question for your legal adviser. This article names no law for India or the GCC.

Start with a list. Write down each field that must not reach the data warehouse or other target unprotected, such as national ID numbers or personal phone numbers. Beside each field, note which step removes or masks it, and who signed that off. If each field on the list gets that data transformation before loading, the flow follows the transform-first order, at least for those fields.

One Order per Flow, Not One for the Whole Firm

A firm can transform some flows before loading and others after. Think of each source feeding the data warehouse or other target as its own flow. In Signisys’ working description, a firm picks the order flow by flow with the six questions, not once for the whole firm. The ETL vs ELT choice, then, belongs to each flow rather than to the company as a whole.

Beside each flow, record three things: the order chosen, how fresh its figures need to be and the person who owns its data transformation logic. Later, when a source changes, that record shows which flow to revisit.

Pick a platform once you have written the answers. The comparison then starts from your own requirements. Fixing wrong or duplicate values is separate work. For that, see the use case on improving data quality.

Application Integration vs Data Integration in Two Definitions

The two terms sound alike but answer different needs. In Signisys’ working description, they differ as follows:

• Application integration: linking systems so that an action in one starts or feeds an action in another. For example, an order keyed into the sales system appears in the delivery system.
• Data integration: combining data from several systems so that people can report on and analyse it together.

ETL and ELT belong to the second kind. Even so, one firm can run both kinds side by side. For the first kind, see connecting applications and processes.

Applying the Six Questions to One Report Flow

For example, picture a mid-size firm that builds a monthly receivables report. Sales orders come from one system and payments from another. This firm is an invention to show the method, and its answers are not advice for yours.

1. The shared data will sit in the firm’s existing reporting database.
2. The flow must strip payment card references before landing, which points to transforming first.
3. The finance team needs the figures once a month, and a copy from the day before is fine.
4. One analyst writes the logic.
5. A wrong receivable must be traceable to the order and payment behind it.
6. Nobody plans to reuse the raw data yet.

Put together, the second answer settles it. This flow transforms first, and the step that removes card references runs before loading. By contrast, a second flow at the same firm, website visits for planning, might point the other way. It carries no personal fields, and the planners may want the raw records for questions they have not framed yet.

What Neither Order Can Decide for You

Some questions stay open after you choose the order. This guide does not settle how long loads take on your own volumes, or which platform to use. Cleaning rules, preparing data for forecasting, the steps of a full project, why two reports disagree and scoring a set-up against fixed checks are separate subjects. Nor is this guide an audit or legal advice.

Ask for an outside opinion if a source system’s owner does not respond, or if teams answer questions 1 and 2 differently. The same applies if no one has written down what the combined data is for.

Signisys provides services for connecting and analysing business data through its business intelligence and analytics solution and its data and AI services. Read this paragraph as a description of a Signisys service, not neutral advice.

Frequently Asked Questions