What is ELT?
ELT is Extract, Load, Transform. A modern approach where raw data is loaded first, then transformed in the warehouse.
Definition
ELT flips the traditional ETL order. Instead of transforming data before loading it, ELT extracts raw data, loads it directly into a cloud warehouse, then transforms it there. This approach gained popularity because cloud warehouses like Snowflake and BigQuery have enough compute power to handle transformations at scale.
Why It Matters
ELT is faster to implement because you don't need to define transformations upfront. Load the raw data, then figure out how to transform it later. This is more flexible than ETL, but requires a warehouse with enough horsepower to handle the transformation workload.
Example
Fivetran extracts raw data from Salesforce and loads it directly into Snowflake. Then dbt runs transformation queries inside Snowflake to create clean, analytics-ready tables.
Best Practices for ELT
Start with Clear Requirements
Before adopting any elt tooling, document what specific problems you need to solve. Teams that skip this step end up with tools that don't match their actual workflow. Write down your current pain points, the volume of data you handle, and the outcomes you expect.
Evaluate Against Your Existing Stack
The best elt solution is one that connects to what you already use. Check integration support with your CRM, data warehouse, and other tools before committing. A standalone tool that doesn't sync with your existing systems creates more work than it saves.
Measure Before and After
Set baseline metrics before you implement any changes to your elt process. Track data quality, time spent on manual tasks, and downstream conversion rates. Without a baseline, you can't prove ROI or identify regressions.
Build Internal Documentation
Document how elt fits into your data operations. Include which fields are affected, which systems are involved, and who owns the process. When team members leave or tools change, this documentation prevents knowledge loss.
Common Mistakes with ELT
Treating It as a One-Time Project
ELT requires ongoing attention. Data decays, requirements shift, and tools update their capabilities. Teams that set up a elt process and never revisit it end up with stale or broken workflows within 6 to 12 months.
Ignoring Data Quality Upstream
No amount of elt tooling fixes bad data at the source. If your input data is full of duplicates, formatting errors, or outdated records, the output will carry those same problems forward. Clean your source data first.
Over-Investing in Tools Before Process
Buying an expensive platform before you have a defined process for elt wastes money. Start with a clear workflow, test it manually or with basic tools, and then invest in automation once you know exactly what you need.
Not Auditing Results Regularly
Automated elt processes can drift over time. Schedule quarterly audits to check accuracy rates, coverage gaps, and whether the output still matches your team's needs. Catching issues early prevents compounding errors.
How ELT Connects to Your Stack
ELT rarely operates in isolation. It sits within a broader data and sales technology stack, and understanding where it fits helps you choose the right tools and build effective workflows.
CRM Systems
Your CRM is the central repository where elt data gets stored and used. Whether you run Salesforce, HubSpot, or another platform, the elt tools you choose should write data directly into CRM records without manual import steps.
Data Warehouses
For teams with analytics infrastructure, elt data often needs to flow into a data warehouse like Snowflake or BigQuery. This lets analysts build reports that combine elt signals with revenue data, usage metrics, and other business intelligence.
Sales Engagement Platforms
Outreach tools like Salesloft and Outreach rely on accurate data to personalize sequences. ELT feeds these platforms with the information sales reps need to write relevant messages and target the right prospects at the right time.
Marketing Automation
Marketing platforms use elt data for segmentation, lead scoring, and campaign targeting. The more complete and accurate your data, the better your marketing automation performs across email, ads, and content personalization.