ELT and the Lakehouse: How They Fit Together

·October 16, 2025

·8 min read

ELT and the Lakehouse: How They Fit Together — Image Source: pexels

You can use ELT and the Lakehouse together to change how your group works with data. If you load raw data first, you get more choices and things move faster. This way helps you do analytics quickly, work with many kinds of data, and spend less money.

Benefit	Description
Faster Insights	Get raw data fast to study it
Scalability	Handle lots of data without trouble
Cost-Effectiveness	Save money with flexible cloud storage

Key Takeaways

ELT lets you load raw data fast. This gives you more ways to look at the data. You can also get answers quicker.
The Lakehouse model mixes good things from data lakes and warehouses. It helps you work with all kinds of data well.
Using ELT with a Lakehouse costs less money. It also helps you grow your system easily. This makes it simple to handle lots of data.

ELT and the Lakehouse Overview

ELT Basics

ELT is used to move and get data ready for study. ELT means Extract, Load, Transform. First, you take data from places like databases or files. Then, you put this raw data into one main storage spot. After that, you change the data so it fits what you need. This way, you keep the original data safe for later. ELT works great with cloud systems. You can store lots of data and use strong tools to change it when you want.

Tip: ELT lets you keep your data open for many uses. You can pick how to use it after you load it.

Lakehouse Model

The Lakehouse model mixes the best parts of data lakes and data warehouses. You can keep all kinds of data—structured, semi-structured, and unstructured—in one place. This model lets you run fast searches and helps with analytics, AI, and machine learning. You also get strong rules for keeping data safe, like access controls and versioning.

Feature	Data Lakehouse	Data Lake	Data Warehouse
Data Format	All types	Unstructured	Structured
Query Performance	Fast	Slow	Fast
Metadata Layer	Unified	Limited	Strong
User Accessibility	Broad	Limited	Analysts

Why ELT Fits Lakehouse

ELT and the Lakehouse work well together. The Lakehouse lets you load raw data first and change it later. You do not have to change your data before loading it. This saves time and gives you more choices. The Lakehouse works with both ELT and ETL, so you can pick what works best. You get real-time analytics and can use batch or streaming data. This setup costs less and makes your data easier to handle.

Integration and Benefits

Core Components

When you use ELT and the Lakehouse together, you need some key parts. These parts help you move, check, and manage your data. The table below shows the main things you will use:

Component	Description
Orchestration tools	These tools decide when jobs run and how they connect.
Monitoring solutions	These watch your data pipelines and warn you about problems.
Cloud capabilities	Cloud features let you grow or shrink and share ELT with everyone.

You can use tools like Integrate.io, AWS Glue, Fivetran, and Informatica for ELT jobs. These tools help you link to many data sources and do tasks automatically. Monitoring solutions check if your data is good and working well, so you always know if it is healthy. Cloud platforms help you handle more data as your needs change.

Tip: Always set up alerts and checks for your data. This helps you find problems before they hurt your work.

ELT Workflow in Lakehouse

The ELT workflow in a Lakehouse has clear steps. First, you bring in data. Next, you organize it. Last, you make it ready for people to use. Here is how it usually works:

Data Ingestion: You take and load data into your Lakehouse. The data stays raw at first. The ingestion layer can handle both batch and streaming data, so you can use many sources.
Schema Enforcement: As you load data, the Lakehouse checks if it fits the right format. This step keeps your data neat and clean.
Data Processing: You change the raw data to fit your business needs.
Unified Governance Model: The Lakehouse tracks where your data comes from and how it changes. This helps you trust your data and keeps it safe.
Data Serving: Clean and improved data is now ready for your team. You can use it for reports, dashboards, or machine learning.

You can use Apache Iceberg to store your data in open formats. Iceberg lets you pick engines like Trino, Spark, or Dremio to process your data. This means you can grow your data tasks and not get stuck with one vendor. Tools like DLT and SlingData also help you move and manage data well.

Note: Many companies use checks and tests during these steps. These checks catch mistakes early and keep your data correct.

Scalability and Efficiency

ELT and the Lakehouse work together to give you speed and scale. You can load lots of data fast and change it when you need to. This way is much faster than old ETL, where you must change data before loading it. Here is a table that compares ETL and ELT:

Category	ETL	ELT
Speed	Data is changed before loading, so it takes longer.	Data loads first, then changes, so it is faster.
Data Volume	Best for small, complex data sets.	Best for big data sets when you need speed.
Data Lake Support	Does not work with data lakes.	Works with data lakes.

ELT (not ETL) uses the best parts of a cloud data warehouse: you can grow as needed, run many jobs at once, and start or stop jobs quickly.

You can use open-format storage to keep full control of your data. This lets you process data for less money and grow as you need. Automation in ELT saves time and helps your team do more. The Lakehouse model works with both structured and unstructured data, so you can use any data type.

Many companies have seen big gains from using ELT and the Lakehouse. For example:

Deloitte made a healthcare tool with a Lakehouse, which helped their predictions.
Netflix uses a Lakehouse to manage viewer data and give better suggestions.
Warner Bros Discovery predicts what customers want and gives real-time tips with Lakehouse data.
Airbnb saved money and time by moving to a Lakehouse for their data work.

One company moved their ELT jobs from a cloud data warehouse to a Lakehouse. They cut costs from $1.16 million to about $200,000 each year. They also made data delays drop from hours to just minutes.

Old ETL jobs often cannot grow well, so they slow down and use too many resources when there is lots of data.

With ELT and the Lakehouse, you can put your compute in one place, use elastic scaling, and store data in columns for faster modeling. This setup lowers costs, speeds up data loading, and makes your data work better.

ELT vs ETL in Lakehouse

Key Differences

There are some big differences between ELT and ETL in a Lakehouse. ELT can load any kind of data, even things like pictures or documents. ETL works best with data that is already organized. ELT puts data in first and changes it later. ETL changes data before putting it in. ELT is faster and easier to keep working well.

Aspect	ETL	ELT
Order of Operations	Transforms data before loading it	Loads data before transforming it
Data Compatibility	Primarily structured data	Handles all types of data, including unstructured
Speed	Slower due to pre-processing	Faster as it loads data first
Maintenance	More complex due to transformation logic	Simpler as transformations are done later

ELT works for data lakes and Lakehouse. ETL does not work well with messy data. ELT lets you change data when you need to. ETL needs rules set up before you start.

Advantages

ELT has many good points in a Lakehouse. You can bring in data quickly and handle lots of it. ELT uses strong data warehouses, so you do not worry about servers. You can use different kinds of data and get answers fast.

Advantage	Description
Speed	ELT enables faster implementation than ETL, as transformation occurs after loading the data.
Scalability	ELT leverages the processing power of data warehouses, avoiding server scaling issues.
Flexibility	ELT supports various data types and allows for rapid ingestion and flexible modeling.

ELT is good for teams that want to change models often or try new things. You can react fast when your business needs change.

You also get a simple setup and better ways to control your data. Lakehouse tools help you track data and keep it safe.

Implementation Tips

There are smart ways to use ELT in your Lakehouse. Put your data in Bronze, Silver, and Gold layers. Split your data by common questions to make things faster. Use strong pipelines for both batch and streaming data. Automate your jobs and set alerts to check data quality.

Use the Lakehouse Medallion Architecture for better data.
Split data to make things faster and cheaper.
Use Change Data Capture for quick updates.
Automate jobs for better results.
Set up checks and alerts for data quality.

Challenge	Description	Solution
Data Integration	Integrating data from various sources can be complex.	Map to a common schema and define transformation rules.
Data Security	Centralized storage increases risk.	Use access controls, encryption, and monitoring.
Data Quality	Large volumes make accuracy hard.	Automate cleansing and set rules.
Data Governance	Managing access is difficult.	Define roles and policies, use access controls.

These tips help you build a strong ELT process. Your Lakehouse will work well and stay healthy.

You can make your data work better with ELT and the Lakehouse. Many groups use this way now. People can get to more data, up to 90% more.

Key Statistic	Description
85%	Groups use Data Lakehouse architecture now
63%	Big companies will use IDP by 2025

Start by making a good plan. Teach your team what to do. Move your data in small steps.

FAQ

What is the main benefit of using ELT with a Lakehouse?

You can load raw data quickly. You decide how and when to change it. This gives you more control and faster results.

Can you use ELT for both structured and unstructured data?

Yes! You can use ELT to handle all types of data, like tables, images, or documents. The Lakehouse supports this flexibility.

How do you keep your data safe in a Lakehouse?

Set up access controls.
Use encryption.
Monitor your data often.
These steps help you protect your information.