What is the future of ETL?

ETL stands for Extract Transform and Load data in order to structure information in a way which is suitable for analytical purposes vs transactional database design. Potentially, combining multiple sources in a single target enabling cross data analysis.

It has been a crucial part of analytics projects over the last 40 years - or even more, with the raise of Data Warehouses back on the 80's.

1. Why ETL or ELT are needed?

The purpose of an ETL or ELT pipeline is to prepare data for analytics. To provide valuable insights, source data from various systems (CRMs, ERPs, social media platforms, external DB, etc.) needs to be moved and consolidated in a destination database.

Heavy calculations or data modifications also take place during this process to streamline and facilitate data extraction.

2. What is ETL?

The key defining feature of an ETL approach is that data is typically processed in-memory rather than in-database.

An ETL pipeline is helpful for:

• Centralizing and standardizing data, making it readily available to analysts and decision-makers 
• Freeing up developers from technical implementation tasks for data movement and maintenance, allowing them to focus on more purposeful work.
• Data migration from legacy systems to a data warehouse
• Deeper analytics after exhausting the insights provided by basic transformation

3. What is ELT?

It relies on having the capacity to initially store large volumes of raw data

 

ELT is a different method of looking at the tool approach to data movement. Instead of transforming the data before it’s written, ELT lets the target system to do the transformation typically making use of parallelization capabilities. The data first copied to the target and then transformed in place.

Despite it is mainly used on cloud architectures, ELT approach is also fully applicable to on-premises developments.

4. When is ELT the right choice?

Depending on a company’s existing architecture, budget, and the degree to which it is already harnessing cloud and big data technologies. When any or all of the following three focus areas are critical, the answer is probably yes.

• When ingestion speed is the number one priority: Because ELT doesn’t have to wait for the data to be worked off-site and then loaded, (data loading and transformation can happen in parallel) the ingestion process is much faster, delivering raw information considerably faster than ETL.

• When re-processing raw data may be required: The advantage of turning data into business intelligence lay in the ability to surface hidden patterns into actionable information. By keeping all historical data on hand, organizations can mine along timelines, sales patterns, seasonal trends, or any emerging metric that becomes important to the organization. Since the data was not transformed before being loaded, you have access to all the raw data. Typically, cloud data lakes have a raw data store, then a refined (or transformed) data store. Often, Data scientists prefer to access the raw data, whereas business users would like the normalized data for business intelligence.

• When you know you will need to scale: When you are using high-end data processing engines or cloud data warehouses, ELT can take advantage of the native processing power for higher scalability. Both ETL and ELT are time-honored methodologies for producing business intelligence from raw data. But, as with almost all things technology, the cloud is changing how businesses tackle ELT challenges.

5. Key differences ETL vs ELT

ETL and ELT differ in two primary ways. One difference is where the data is transformed, and the other difference is how data warehouses retain data.

• ETL transforms data on a separate processing server, while ELT transforms data within the data warehouse itself.
• ETL does not transfer raw data into the data warehouse, while ELT sends raw data directly to the data warehouse.

For ETL, the process of data ingestion is made slower by transforming data on a separate server before the loading process.

ELT, in contrast, delivers faster data ingestion, because data is not sent to a secondary server for restructuring. In fact, with ELT, data can be loaded and transformed simultaneously.

6. What is the future? Our analysis

The raw data retention of ELT creates a rich historical archive for generating business intelligence. As goals and strategies change, BI teams can re-query raw data to develop new transformations using comprehensive datasets. 

ETL, on the other hand, does not generate complete raw data sets that are endlessly queryable. It is ideal for compute-intensive transformations, systems with legacy architectures, or data workflows that require manipulation before entering a target system, such as erasing personal identifying information.

ELT is more flexible, efficient, and scalable, especially for ingesting large amounts of data, processing data sets that contain both structured and unstructured data.

However, there is no one receipt for every company. That is why at Simig Solutions we always start by checking the existing tech infrastructure as well as business goals to define the strategy that fits the best in terms of cost, scalability and compliance.

Feel free to reach out for advice.

Next Chapter:

• Tools enabling ETL | ELT data pipelines

• Alternatives to ETL | ELT. Is this always needed?