ETL vs. ELT: Pick the Right approach based on your team, not just trends
Georges Awono
Data & Cloud Architect ? Transforming Business Goals to Technical Strategies ? Focus: Scalability, Resiliency, FinOps, Security, Compliance
The ETL vs. ELT debate has been around for years. Some say ETL is outdated, others claim ELT is the future. But here’s what really matters: how well your team can execute.
Forcing a paradigm shift just because it’s trendy can frustrate your team, slow productivity, and even lead to attrition. Instead of chasing buzzwords, choose the approach that aligns with your team’s strengths.
ETL (Extract, Transform, Load) – Best for Python-Heavy Teams
In ETL, data is extracted from the source, transformed before it reaches the data warehouse, and then loaded into Redshift (or another warehouse). This approach is perfect for teams comfortable with Python and distributed processing frameworks like Spark.
On AWS, ETL pipelines typically extract data into Amazon S3, transform it using AWS Glue (Spark) or AWS EMR (big data processing), and then load the cleaned data into Amazon Redshift for analytics. By processing transformations outside of the data warehouse, ETL allows for complex processing, pre-aggregations, and efficient structured data ingestion.
Advantages of ETL
ELT (Extract, Load, Transform) – Best for SQL-Heavy Teams
In ELT, raw data is loaded directly into Amazon Redshift first, and transformations happen inside the warehouse using SQL. This approach is ideal for teams that work extensively with SQL and prefer a warehouse-centric data strategy.
On AWS, ELT workflows extract data into Amazon S3, load it directly into Amazon Redshift using AWS Glue or Redshift COPY commands, and then transform it using SQL within Redshift itself. By leveraging Amazon Redshift’s massive parallel processing (MPP) capabilities, ELT allows for scalable transformations, real-time data modeling, and flexible schema evolution.
Advantages of ELT
The Real Answer? Follow Your Team’s Strengths
If your team is strong in Python and big data processing, ETL provides greater flexibility with Glue and EMR. If your team prefers SQL-first workflows, ELT lets you fully leverage Amazon Redshift’s modern analytics capabilities.
A data strategy should empower teams, not create roadblocks. The best approach is the one your team can execute at scale.
Are you able to identify your team's strength?
#Cloud #Data #Strategy #ETL #ELT
All Pros agree - ETL is the Best
2 周Georges Awono A couple of points: * Most good ETL platforms do not require programming skills to define transformations. In actuality, the ELT concept is where programming skills are required because SQL is not enough to define the transformations and you have to use Python (DBT). * You can implement flexible ETL solutions by using the metadata-drive data pipelines processing. * ELT will always require database to do the transformations. For that reason, ETL is the better technology to handle real-time or near real-time requirements.