What is ELT? How is it Different from ETL? (2022)

What is ELT? How is it Different from ETL? (1)

By

  • Craig S. Mullins,Mullins Consulting

Extract, Load, Transform (ELT) is a data integration process for transferring raw data from a source server to a data system (such as a data warehouse or data lake) on a target server and then preparing the information for downstream uses.

ELT is comprised of a data pipeline with three different operations being performed on data:

The first step is to Extract the data. Extracting data is the process of identifying and reading data from one or more source systems, which may be databases, files, archives, ERP, CRM or any other viable source of useful data.

The second step for ELT, is to Load the extract data. Loading is the process of adding the extracted data to the target database.

The third step is to Transform the data. Data transformation is the process of converting data from its source format to the format required for analysis. Transformation is typically based on rules that define how the data should be converted for usage and analysis in the target data store. Although transforming data can take many different forms, it frequently involves converting coded data into usable data using code and lookup tables.

Examples of transformations include:

  • Replacing codes with values
  • Aggregating numerical sums
  • Applying mathematical functions
  • Converting data types
  • Modifying text strings
  • Combining data from different tables and databases

How ELT works

ELT is a variation of the Extract, Transform, Load (ETL), a data integration process in which transformation takes place on an intermediate server before it is loaded into the target. In contrast, ELT allows raw data to be loaded directly into the target and transformed there.

With an ELT approach, a data extraction tool is used to obtain data from a source or sources, and the extracted data is stored in a staging area or database. Any required business rules and data integrity checks can be run on the data in the staging area before it is loaded into the data warehouse. All data transformations occur in the data warehouse after the data is loaded.

ELT vs. ETL

The differences between ELT and a traditional ETL process are more significant than just switching the L and the T. The biggest determinant is how, when and where the data transformations are performed.

With ETL, the raw data is not available in the data warehouse because it is transformed before it is loaded. With ELT, the raw data is loaded into the data warehouse (or data lake) and transformations occur on the stored data.

(Video) ETL vs ELT

Staging areas are used for both ELT and ETL, but with ETL the staging areas are built into the ETL tool being used. With ELT, the staging area is in a database used for the data warehouse.

What is ELT? How is it Different from ETL? (2)

ELT is most useful for processing the large data sets required for business intelligence (BI) and big data analytics. Nonrelational and unstructured data is more conducive for an ELT approach because the data is copied "as is" from the source. Applying analytics to unstructured data typically uses a "schema on read" approach as opposed to the traditional "schema on write" used by relational databases.

Loading data without first transforming it can be problematic if you are moving data from a nonrelational source to a relational target because the data will have to match a relational schema. This means it will be necessary to identify and massage data to support the data types available in the target database.

Data type conversion may need to be performed as part of the load process if the source and target data stores do not support all the same data types. Such problems can also occur when moving data from one relational database management system (DBMS) to another, such as say Oracle to Db2, because the data types supported differ from DBMS to DBMS.

ETL should be considered as a preferred approach over ELT when there is a need for extensive data cleansing before loading the data to the target system, when there are numerous complex computations required on numeric data and when all the source data comes from relational systems.

The following chart compares different facets of ETL or ELT:

ELT

ETL

Order of Processes

Extract
Load
Transform

Extract
Transform
Load

Flexibility

Because transformation is not dependent on extraction, ELT is more flexible than ETL for adding more extracted data in the future.

(Video) ETL vs ELT | Modern Data Architectures

More upfront planning should be conducted to ensure that all relevant data is being integrated.

Administration

More administration may be required as multiple tools may need to be adopted.

Typically, a single tool is used for all three stages perhaps simplifying administration effort.

Development Time

With a more flexible approach, development time may expand depending upon requirements and approach.

ETL requires upfront design planning, which can result in less overhead and development time because only relevant data is processed.

End Users

Data scientists and advanced analysts

Users reading reports and SQL coders

Complexity of Transformation

Transformations are coded in by programmers (e.g., using Java) and must be maintained like any other program.

Transformations are coded in the ETL tool by data integration professional experienced with the tool.

(Video) ETL vs ELT... Why there is market shift toward ELT..?

Hardware Requirements

Typically, ELT tools do not require additional hardware, instead using existing compute power for transformations.

It is common for ETL tools to require specific hardware with their own engines to perform transformations.

Skills

ELT relies mostly on native DBMS functionality, so existing skills can be used in most cases.

ETL requires additional training and skills to learn the tool set that drives the extraction, transformation and loading.

Maturity

ELT is a relatively new practice, and as such there is less expertise and fewer best practices available.

ETL is a mature practice that has existed since the 1990s. There are many skilled technicians, best practices exist, and there are many useful ETL tools on the market.

Data Stores

Mostly Hadoop, perhaps NoSQL database. Rarely relational database.

Almost exclusively relational database.

Use Cases

(Video) ETL vs ELT | Data Warehouse Tutorial For Beginners | Data Warehouse Concepts (6/30)

Best for unstructured data and nonrelational data. Ideal for data lakes. Can work for homogeneous relational data, too. Well-suited for very large amounts of data.

Best for relational and structured data. Better for small to medium amounts of data.

Benefits of ELT

One of the main attractions of ELT is the reduction in load times relative to the ETL model. Taking advantage of the processing capability built into a data warehousing infrastructure reduces the time that data spends in transit and is usually more cost-effective. ELT can be more efficient by utilizing the computer power of modern data storage systems.

When you use ELT, you move the entire data set as it exists in the source systems to the target. This means that you have the raw data at your disposal in the data warehouse, in contrast to the ETL approach where the raw data is transformed before it is loaded to the data warehouse. This flexibility can improve data analysis, enabling more analytics to be performed directly within the data warehouse without having to reach out to the source systems for the untransformed data.

Using the ELT can make sense when adopting a big data initiative for analytics. Big data often relies on a large amount of data, as well as wide variety of data that is more suitable for ELT.

Uses of ELT

ELT is often used in the following cases:

  • when the data is structured, but the source and target database are the same type (i.e., Oracle source and target);
  • when the data is unstructured and massive, such as processing and correlating data from log files and sensors'
  • when the data is relatively simple, but there are large amounts of it;
  • when there is a plan to use machine learning tools to process the data instead of traditional SQL queries; and
  • schema on read.

ELT tools and software

Although ELT can be performed using separate tools for extracting, loading and transforming the data, tools exist that integrate all ELT processes. When seeking an ELT tool, users should look for the ability to read data from multiple sources, specifically the sources that their organization uses and intends to use. Most tools support a wide variety of source and target data stores and database systems.

Users can look for tools that can perform both ETL and ELT, as it's likely to have the need for both data integration techniques.

Although there are many ELT/ETL tool providers, a few of the market leaders include:

  • IBM
  • Informatica
  • Microsoft
  • Oracle
  • SAS
  • Talend
  • Teradata

A data store can be useful for managing a target data mart, data warehouse and/or data lake. For an ELT approach, NoSQL database management systems and Hadoop are viable candidates, as are purpose-built data warehouse appliances. In some cases, a traditional relational DBMS may be appropriate.

This was last updated in January 2020

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FAQs

What is the difference between ELT and ETL explain? ›

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.

What is ELT explain? ›

ELT, which stands for “Extract, Load, Transform,” is another type of data integration process, similar to its counterpart ETL, “Extract, Transform, Load”. This process moves raw data from a source system to a destination resource, such as a data warehouse.

What's the difference between ETL and ELT and what's the pros and cons? ›

Learn the differences between ELT and ETL tools, the processing differences between each, and how to choose between them for your data pipeline needs.
...
What are the main differences between ETL and ELT?
ETLELT
FLEXIBILITYLOWHIGH
SCALABILITYLOWHIGH
MAINTENANCECONTINUOUSLOW MAINTENANCE
COMPLIANCEEASYCOMPLEX
9 more rows
3 Jan 2022

What are the difference between ETL and ELT method and which method most fit for handle big data and unstructured data please explain? ›

On a high-level, ETL transforms your data before loading, while ELT transforms data only after loading to your warehouse.

What is the difference between data integration and ETL? ›

The main difference between data integration and ETL is that the data integration is the process of combining data in different sources to provide a unified view to the users while ETL is the process of extracting, transforming and loading data in a data warehouse environment.

Where is ETL and ELT used? ›

ETL is best suited for dealing with smaller data sets that require complex transformations. ELT is best when dealing with massive amounts of structured and unstructured data. ETL works with cloud-based and onsite data warehouses. It requires a relational or structured data format.

What is ELT example? ›

For example, an ELT tool may extract data from various source systems and store them in a data lake, made up of Amazon S3 or Azure Blob Storage. An ETL process can extract the data from the lake after that, transform it and load into a data warehouse for reporting.

Which is better ELT or ETL? ›

Advantages of ELT over ETL

More flexibility, as ETL is traditionally intended for relational, structured data. Cloud-based data warehouses enable ELT for structured and unstructured data. Greater accessibility, as ETL is generally supported, maintained, and governed by organizations' IT departments.

What is ETL and ELT in data warehousing? ›

ELT (extract, load, transform) and ETL (extract, transform, load) are both data integration processes that move raw data from a source system to a target database, such as a data lake or data warehouse.

What is the benefit of ELT? ›

The main advantage of using an ELT approach is that you can move all raw data from a multitude of sources into a single, unified repository (a single source of truth) and have unlimited access to all of your data at any time. You can work more flexibly and it makes it easy to store new, unstructured data.

Where is ELT used? ›

ELT is most useful for processing the large data sets required for business intelligence (BI) and big data analytics. Nonrelational and unstructured data is more conducive for an ELT approach because the data is copied "as is" from the source.

What is the difference between the ETL and ELT components of Talend Open Studio? ›

The key difference between ETL and ELT tools is ETL transforms data prior to loading data into target systems, while the latter transforms data within those systems.

What are the different methods of ELT? ›

English Teaching Methods
  • The Direct Method. ...
  • The Grammar Translation Method. ...
  • The Audio Lingual Method. ...
  • The Structural Approach. ...
  • Suggestopedia. ...
  • Total Physical Response. ...
  • Communicative Language Teaching (CLT) ...
  • The Silent Way.

Why ELT has no information loss? ›

Advantages of ELT

It allows you to save any type of information, even if you haven't transformed and structured it. It gives you immediate access to information whenever you want it. Other advantages of this Data Integration process include: High Speed: It allows all data to get into the data warehouse immediately.

What is the difference between application integration and data integration? ›

Typically, data integration deals with large sets of data at rest; it happens when the process that created the data has been completed. Application integration, on the other hand, is for integrating real-time data between two or more applications. They also differ in how they are managed organizationally.

What is the difference between ETL and data warehousing? ›

While the data warehouse acts as the storage place for all your data and BI tools serve as the mechanism that consumes the data to give you insights, ETL is the intermediary that pushes all of the data from your tech stack and customer tools into the data warehouse for analysis.

What is the difference between ETL and data migration? ›

ETL represents Extract, Transform and Load, which is a cycle used to gather data from different sources, change the data relying upon business rules/needs and burden the information into an objective data set. Data migration is the way toward moving information starting with one framework then onto the next.

What is the best ELT? ›

The Talend cloud data integration tool is known as one of the best ELT tools. It is a modern big data and cloud integration software to connect, extract, and transform any data across the cloud and on-premises.

What is ELT in English PDF? ›

The field of English Language Teaching (ELT) that is categorized under applied linguistics is based on two major pillars: linguistics and psychology.

What is ETL process? ›

ETL, which stands for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.

What is ETL ELT data pipelines? ›

An ETL pipeline is the set of processes used to move data from a source or multiple sources into a database such as a data warehouse. ETL stands for “extract, transform, load,” the three interdependent processes of data integration used to pull data from one database and move it to another.

Why ETL is used in data warehouse? ›

ETL tools break down data silos and make it easy for your data scientists to access and analyze data, and turn it into business intelligence. In short, ETL tools are the first essential step in the data warehousing process that eventually lets you make more informed decisions in less time.

What is ELT knowledge? ›

English Language Teaching, or ELT, refers to the activity and industry of teaching English to non-native speakers.

What is ELT activity? ›

An activity describes any procedures in which learners work towards a goal such as play a game or engaging in a discussion. Finally, a task is something undergone by students using pre-existing or scaffolded language resources.

What are the four types of ELT? ›

There are five basic types of ELTs: automatic fixed (ELT-AF), automatic portable (ELT-AP), survival (ELT-S), automatic deployable (ELT-AD), and distress triggered (ELT-DT).

Which is better ELT or ETL? ›

ETL is better suited for compliance with GDPR, HIPAA, and CCPA standards given that users can omit any sensitive data prior to loading in the target system. ELT carries more risk of exposing private data and not complying with GDPR, HIPAA, and CCPA standards given that all data is loaded into the target system.

What is ETL and ELT in data warehousing? ›

ELT (extract, load, transform) and ETL (extract, transform, load) are both data integration processes that move raw data from a source system to a target database, such as a data lake or data warehouse.

What is ETL explain with example? ›

ETL is a process in Data Warehousing and it stands for Extract, Transform and Load. It is a process in which an ETL tool extracts the data from various data source systems, transforms it in the staging area, and then finally, loads it into the Data Warehouse system.

What is the difference between the ETL and ELT components of Talend Open Studio? ›

The key difference between ETL and ELT tools is ETL transforms data prior to loading data into target systems, while the latter transforms data within those systems.

What is the benefit of ELT? ›

The main advantage of using an ELT approach is that you can move all raw data from a multitude of sources into a single, unified repository (a single source of truth) and have unlimited access to all of your data at any time. You can work more flexibly and it makes it easy to store new, unstructured data.

What is the best ELT? ›

The Talend cloud data integration tool is known as one of the best ELT tools. It is a modern big data and cloud integration software to connect, extract, and transform any data across the cloud and on-premises.

Where is ELT used? ›

ELT is most useful for processing the large data sets required for business intelligence (BI) and big data analytics. Nonrelational and unstructured data is more conducive for an ELT approach because the data is copied "as is" from the source.

What is the difference between ETL and data warehousing? ›

While the data warehouse acts as the storage place for all your data and BI tools serve as the mechanism that consumes the data to give you insights, ETL is the intermediary that pushes all of the data from your tech stack and customer tools into the data warehouse for analysis.

What is ETL ELT data pipelines? ›

An ETL pipeline is the set of processes used to move data from a source or multiple sources into a database such as a data warehouse. ETL stands for “extract, transform, load,” the three interdependent processes of data integration used to pull data from one database and move it to another.

How do you explain ETL project in interview? ›

Have the candidate describe a recent ETL project she performed and discuss the steps involved in the project. Find out how she managed each step of the project. Ask her to elaborate on the kinds of transformations she had to make with the data, and inquire about the specific ETL tools she used to perform the project.

What are the five main steps in the ETL process? ›

What is the ETL Process? The 5 steps of the ETL process are: extract, clean, transform, load, and analyze. Of the 5, extract, transform, and load are the most important process steps. Clean: Cleans data extracted from an unstructured data pool, ensuring the quality of the data prior to transformation.

What are the different components of an ETL tool? ›

The Foundation of ETL Architecture
  • Profiling.
  • Extraction.
  • Cleansing.
  • Transformation.
  • Loading.
  • Monitoring.

What is the difference between built in and repository in Talend? ›

build in: all information is stored directly in your job. repository: all information is stored in the repository. In the job only a link is stored. So if you change the properties in the repository (for example a server name) all jobs will use the new value.

Videos

1. ETL vs ELT Explained Clearly!!
(Satyajit Pattnaik)
2. Difference Between ETL and ELT Processes
(TechLake)
3. ETL vs ELT Evolutions
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4. ELT vs ETL - A Pizza Comparison
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5. EL vs ETL vs ELT in Google Cloud Bigquery
(SkillCurb)
6. ETL vs ELT with Dan Silberman
(The Engineering Side of Data)

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