The ELT process: break down silos and leverage your data

Three letters, three phases, many benefits: the ELT (Extract, Load, Transform) process is a crucial step of any Analytics project. By matching the reality of an organization’s rapidly changing data needs, ELT boosts data capabilities and Business Intelligence in particular. It is, though, an iterative process and must be improved through multiple trials and errors. Let’s dive into it!

What is ELT?

Definition

ELT is an acronym for: Extract, Load, Transform.

It refers to the integration process that enables raw data to be transferred from one or multiple source servers to a target database, data warehouse or data lake — before being prepared for downstream uses.

Here are the 3 segments of the ELT process: 

• extract: taking data from one or several source systems (such as databases, flat files, web services, RSS feeds);
• load: storing the extracted data into the target database, data warehouse or data lake.
• transform: converting the raw, unformatted data to the format your users need in order to carry out their analysis.

From ETL to ELT

ELT (Extract, Load, Transform) is a successor to ETL. ETL involves that your raw data doesn’t remain available in your data warehouse, because it has already been transformed before being loaded – which means you lose a whole piece of your data history.

The emergence of data warehouses with lower storage costs eased the transition to the new ELT paradigm.

Data Storytelling: why the ELT process matters

The ELT process’ main goal is to consolidate your data by bringing it together into a unified data warehouse – which is often called a “Single Source Of Truth” (SSOT) – and make the analysis and reporting processes much easier.

During the transformation operation, a bit of coding will work wonders to improve data quality, and thus make it more useful and trustworthy for your business teams.

How to choose an ELT solution

Connectors

You have to determine which data sources and data warehouses are compatible with each ELT solution, and ask yourself where you need to extract the data from (Google Analytics, Hubspot, Semrush…). You also need to decide where you want to load it (Postgres, BigQuery, Snowflake…). 

Airbyte particularly stands out since it offers open-source connectors: you can choose a pre-built one and edit it, or even create your own!

Volume

Another important issue to consider is the tool’s scalability. Think about the data volume that you have to manage today, and also the one you will need to deal with in the future: will this solution be able to handle everything?

Real-time processing

Ask yourself if you only need an ELT solution for BI purposes: if this is the case, daily synchronisations will be enough for you. But if you need automated processes and real-time data sharing, you will have to turn to a data streaming solution like Kafka.

Open source or vendor

Choose between an open-source solution (such as Airbyte) and a vendor solution (like Fivetran).

For small businesses, it can be easier to buy a solution rather than building and maintaining one. However, make sure this solution doesn’t use vendor lock-in: find out if your ELT pipeline is dependent on a single vendor. 

You can decide to use separate solutions for your EL and T components, to ensure that the chosen technology is generic. Besides, some vendor solutions are built on open-source conventions, and will help you migrate at your request.

Additional options

When implementing an ELT solution, you can start small, and then use an iterative approach by adapting it over time. 

• When (and if) needed, opt for a solution that offers data orchestration and automation options such as Airflow.
• Don’t forget to regularly assess the tool’s user-friendliness and UX performance!

Reverse ETL

The ELT process advances companies' data capabilities and Business Intelligence. But this should not end there: data should also be available in various Business tools and in the CRM (for instance).

This is where reverse ETL comes in: with this method, formatted and enriched data is sent back to Business tools in order to make it actionable.

This operation prevents the data warehouse from becoming a silo where the data is only available to technical business users who have a good command of SQL.

Empowering non-technical business users implies providing them with actionable data – in other words, information that gives them enough clarity to make business decisions: this practice is known as data operationalization.

Building a strong Data culture in your organization entails making data available in the right form and at the right time, in order to drive business outcomes efficiently. Thanks to the ELT process, your data will be ready to run advanced Analytics. It will then be time for you to connect your unified data warehouse to a powerful Analytics solution, such as Husprey’s notebook!