ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two approaches to data integration that differ in the sequence of steps. In ETL, data is transformed before being loaded into the destination system, whereas ELT involves loading raw data first and then performing transformations within the target environment. ELT is gaining traction, especially with the rise of cloud-based platforms and the need to handle large volumes of data efficiently.
Key takeaways
- Order matters – ETL transforms data before loading, while ELT loads raw data first then transforms in the destination system
- ELT excels with modern cloud warehouses – Platforms like Snowflake and BigQuery are optimized for in-warehouse transformations, making ELT faster and more scalable
- ETL wins for strict compliance – Healthcare (HIPAA), finance (SOX), and other regulated industries often need data cleaned before storage
- ELT enables flexibility – Raw data storage allows future analysis and reprocessing without re-ingesting from source systems
- Cost depends on use case – ETL may be cheaper for small data subsets, while ELT typically offers lower total ownership costs for high-volume scenarios
- Team skills drive choice – ETL suits teams with strong Python/scripting skills, while ELT works best for SQL-focused teams
- ELT supports AI/ML better – Raw, granular data access enables feature engineering and experimentation for machine learning models
ETL vs ELT: what’s the big difference?
The letter switch is a big clue, of course. The main difference between ETL and ELT in the order of operations: essentially, where any data transformation that needs to happen, happens.
ETL is extract, transform, and load:
- Extract data from source systems
- Transform it on a separate processing server
- Load it into a destination, like a data warehouse
ETL is good for when data needs to be cleaned before loading or storing it, or when you need to match local schema, but it’s slower for large datasets, it can lock you into a set way of organizing your data, and it can also drop datapoints that you might want later (you just don’t know it yet).
ELT is extract, load, and transform:
- Extract data from source systems
- Load the data directly into a data warehouse
- Then transform it there using its compute power
ELT is faster and more scalable with modern cloud warehouses like Snowflake or BigQuery.
On the downside, ELT drops raw data into your data warehouse. That may not be ideal for compliance or sensitive info, or may not work well with verticalized uses that require very specific schema, normalization, or transformations.
In short, with ETL you transform the data before loading it, and with ELT you transform the data after loading … if you do at all.
ETL vs ELT: where does ETL make more sense?
Yep, ETL is the grandaddy of data transfer tech. But that doesn’t mean ELT is obviously better everywhere, for everything. There are still some places where it might make more sense to go with old school over new school.
Here are a few:
- Strict data governance or compliance requirements
When you have super-sensitive data that absolutely must be cleaned, masked, or anonymized before being stored, ETL makes sense. That could be in industries like healthcare, where you’re dealing with HIPAA rules, or finance, where you might have SOX guidelines, or marketing, where you have to think about GDPR in the EU, or government. - Legacy systems or on-prem data warehouses
Some traditional data warehouses like Oracle or Teradata are perhaps less optimized for large-scale in-warehouse transformations. In those cases, using ETL offloads compute to external servers before loading. - Complex transformations not suited to SQL
If you’re doing rocket science with your data and need complex joins, scripting, or business logic that might require or just be easier in Python or Spark, ETL lets you do that work and pre-process your data before it reaches the warehouse. - Minimizing storage costs
Maybe you have truly huge amounts of data, and you don’t want to store large volumes of raw data in the warehouse due to cost. In that scenario, ETL transforms and filters your data so you’re only storing what you absolutely know you need. - Real-time or near real-time processing
Some ETL tools support streaming transformations before loading, which can be useful for use cases like fraud detection in banking or stream conversions for IoT data processing. - Data standardization across sources
When your data comes from multiple inconsistent sources (e.g., different ERPs or CRMs), you may want to standardize it first before loading. This can also be the case in marketing and advertising, where Extract parent company Singular uses ETL to standardize and normalize data from thousands of different ad networks into a consistent source of truth for growth marketers.
ELT vs ETL: where does ELT make more sense?
ELT is younger, hotter, and sexier than ETL. (I want to say just like me, but … that doesn’t make sense and isn’t true.) That doesn’t mean ELT is good for everything in every case, but it is great at doing a lot of different things.
Sometimes new school is just better than old school.
Here’s where ELT shines:
- Modern cloud data warehouses
If you’re new school, you’re probably using modern cloud data warehouses. And when you’re using tools like Snowflake, BigQuery, Redshift, or Databricks, ELT leverages their scalable compute power efficiently. They’re optimized for in-warehouse transformations. - Big data or high volume ingestion
Transformation takes time. In contrast, ELT loads raw data quickly, without waiting for transformations, which makes it great for data lakes, event logs, IoT pipelines, or anywhere you’re dealing with high-volume real-time data. - Flexible ad hoc analysis
Look, you just don’t always know exactly how you’re going to use your data. You don’t know if an analyst or exec will have an idea in 3 months that requires data going back 15 months. So being flexible and allowing analysts and data scientists to transform data on demand however they want after it’s loaded makes sense. - AI & machine learning
ELT gives ML teams access to raw, granular data for feature engineering or experimentation, providing more options. Then they can build custom transformation pipelines in the warehouse to suit exactly their own needs. - Versioning & reprocessing
Storing raw data right off the bat allows you to re-transform it later with updated logic or schema changes … and there’s no need to re-ingest the data all over again. - Incremental loads and schema evolution
Stuff changes. So does data. ELT makes it easier to load data as it currently is even when source (or destination) schemas change often. You can always adapt your transformations post-load. - Cost-efficient development
With ELT, you can achieve a faster time to value because you can start querying data right after loading. Essentially, you’re reducing operational complexity compared to managing external ETL infrastructure.
ETL vs ELT: what’s cheaper?
Cost is always a concern. So when you’re making an ETL vs ELT decision, what’s cheaper? Well, it’s kind of a trick question. Because, as usual, it depends.
ETL cheaper?
On the 1 hand, compute in ETL happens outside your data warehouse, whether in Spark, Python, or an ETL tool. So you might end up paying for external infrastructure, like managed ETL platforms (think Informatica, Talend, or Fivetran’s ETL mode), or cloud VMs or clusters.
On the other hand, with ETL you might have lower warehouse costs since you only store transformed data … not all the raw data.
And on yet another hand (or I guess the original hand), you’re likely to have higher development and/or maintenance costs thanks to ETL’s relatively more complex pipelines and data orchestration requirements.
ELT cheaper?
Continuing with the hands thing, on the 1 hand in ELT compute and storage happen inside your cloud data warehouse. That means you might incur higher warehouse costs, especially if you’re transforming large raw datasets frequently, or you’re not optimizing queries.
On the other hand, with ELT you save on external infrastructure, and some ELT tools (like dbt) are free or open-source. (Which has its own cost considerations since you may have to manage that now by yourself.)
Ultimately, however, ELT is generally easier to maintain and scale, which can lead to a lower total cost of ownership over time.
So which one is cheaper?
Here’s the dreaded answer: it depends on what you’re doing.
ETL is cheaper when:
- You store and process only a subset of all your data
- Your warehouse has limited resources or is expensive per compute
ELT is cheaper when:
- You already pay for a scalable cloud data warehouse
- You want fewer moving parts and faster time to insight
Your team’s skillset matters
When you’re making an ETL vs ELT decision, you also have to consider your team’s skillset.
What do you do well, and what do you struggle with?
ETL may be better if your team has strong data engineering and scripting skills. Do you have some Python geeks on staff? But ELT could be better if your team is comfortable with SQL-first workflows and in-warehouse logic.
Not surprisingly, those skillsets line up with where data transformations happen.
Your data needs matter too
ELT handles high-volume, multi-source ingestion more easily than ETL: a key consideration in an ETL vs ELT decision. That said, ELT can introduce delays if transformations are required before the data is usable, and they have to get queued, or depend on varying warehouse loads.
Conversely, ETL can be better if you want to pre-aggregate or filter data to reduce volume before loading, and ETL can support near real-time pipelines with some tools.
Your data matters, and how you use it, when you use it, and when you absolutely need to have access to it all matter.
Why Extract is the the ELT tool for you
If you decide that ELT is the way to go, Extract is the ELT tool for you.
It’s ultra-modern, built on the fastest, most scalable architecture. That means we can run on far less hardware than legacy ELT vendors, making Extract radically scalable and affordable.
We are literally up to 17X more efficient than the biggest names in the ELT space, providing enterprise-grade speed and reliability for the biggest loads on the planet. That efficiency also allows us to offer ELT for half the cost in most cases and even up to 75% cheaper, with no hidden fees.
In addition, Extract offers total transparency and control: you see everything that’s happening with your data via our connectors in real-time, giving you unprecedented ability to see permissions issues or other challenges for every query, API call, and data move.
And while Extract is a new offering, it comes from a decade-long legacy of data aggregation, transformation, and loading at Singular, our parent company. That means not only do we have experience working with the biggest global brands like Microsoft, Rovio, Uber, Shopify, Nike, Electronic Arts, Doordash, Warby Parker, and hundreds more, we also are used to delivering on mission-critical data applications that require world-class uptime and guaranteed reliability.
ETL vs ELT Comparison Table
| Factor | ETL (Extract, Transform, Load) | ELT (Extract, Load, Transform) |
|---|---|---|
| Process Order | Transform before loading | Load then transform |
| Best For | Legacy systems, strict compliance | Modern cloud warehouses |
| Data Storage | Only transformed data | Raw + transformed data |
| Speed | Slower for large datasets | Faster data ingestion |
| Flexibility | Limited by pre-defined transformations | High – transform data as needed |
| Compute Location | External servers/tools | Inside data warehouse |
| Compliance | Better for sensitive data | Requires careful handling |
| Team Skills | Python/scripting focused | SQL-focused workflows |
| Cost Structure | External infrastructure + warehouse | Primarily warehouse costs |
| Scalability | Limited by transformation bottlenecks | Highly scalable with cloud |
| AI/ML Support | Limited raw data access | Full granular data access |
| Schema Changes | Requires pipeline updates | Easy adaptation post-load |
| Real-time Processing | Supports streaming transformations | Better for high-volume ingestion |
| Ideal Use Cases | Healthcare, finance, legacy systems | Analytics, AI/ML, cloud-first orgs |
FAQs
When should I choose ETL over ELT?
Choose ETL when you have strict data governance requirements (like HIPAA or SOX compliance), legacy on-premise warehouses, or need complex transformations that aren’t suited to SQL. ETL is also better when you want to minimize storage costs by filtering data before loading, or when working with traditional systems that aren’t optimized for in-warehouse processing.
What makes ELT better for modern data teams?
ELT leverages the compute power of modern cloud data warehouses, making it faster for high-volume data ingestion. It provides flexibility for ad-hoc analysis since raw data is preserved, supports AI/ML initiatives with granular data access, and typically has lower total cost of ownership due to simpler infrastructure requirements and easier maintenance.
How do I decide between ETL and ELT for my organization?
Consider your data warehouse type (cloud vs on-premise), compliance requirements, data volumes, team skillsets, and use cases. If you’re using modern cloud platforms like Snowflake or BigQuery with high data volumes and need flexibility for analytics, ELT is typically the better choice. If you have strict compliance needs or legacy systems, ETL might be more appropriate.