The massive growth of big data has put a strain on data warehouse design. Organizations deal with large amounts of various forms of data, such as social media, customer behaviour, and big data. Data warehouses employ the extract, transform, load (ETL) or extract, load, transform (ELT) data integration methods.
Today in this ETL vs ELT article we will understand them in details including their advantages and disadvantages and will also look at the key difference between them.
The three phases of the typical data pipeline are described by the abbreviation ETL, which stands for “Extract, Transform, and Load.” For smaller data sets requiring complex transformations, the ETL process is ideal.
The process of ETL follows as
- From the source, a specific subset of data is extracted.
- In a staging area, data is converted in some way, such as by data mapping, concatenation, or calculations. To work around the limitations of typical data warehouses, data must be transformed before being loaded.
- The data is placed into the target data warehouse system and is ready to be evaluated using business intelligence or data analytics software.
Some of the advantages of using ETL are
- Manage data warehouse storage: ETL could be able to assist you save money on storage. ETL tools will process and filter your data so that only the information you need is kept. Data storage will be reduced as a result of this.
- Security protocols compliance: You may be adhering to data privacy regulations such as GDPR, SOC2, and HIPAA, as well as company-specific needs. Before storing sensitive data like email or IP addresses in your data warehouse, such requirements sometimes require you to delete, disguise, or encrypt it. By hiding or eliminating data at the transform step of the ETL process, you can simply accomplish this.
Some of the disadvantages of using ETL are
- Low flexibility: You’ll have to set up the transformations ahead of time to account for format changes and edge cases. Otherwise, every edge case will necessitate stopping and modifying the ETL procedure. This can lead to a significant maintenance bill.
- Slow: It’s possible that you’ll have to wait until all of the transformations are finished before loading the data into the warehouse.
- Continuous maintenance: To keep the ETL process up to current with your changing input sources, you may need to do so on a regular basis.
- High initial cost: You may need to define the processes and transformations you’ll require for your project, and the initial cost of setting up your ETL process can be high.
The three steps of the current data pipeline are described by the abbreviation ELT, which stands for “Extract, Load, and Transform.” ELT is a more cost-effective alternative to ETL for bigger, structured and unstructured data collections, as well as when speed is critical.
The process of ELT is
- The source data is used to extract all of the information.
- The target system receives all of the data right away (either a data warehouse, data mart, or data lake). Raw, unstructured, semi-structured, and structured data types are all examples of this.
- The data has been changed in the target system and is now ready to be evaluated using business intelligence or data analytics software.
A cloud-based data lake, data mart, data warehouse, or data lakehouse is typically the target system for ELT. These cloud-based solutions provide near-unlimited storage and processing capacity. This enables users to extract and load any data they require in real-time. At any time, the cloud platforms can transform data for any BI, analytics, or predictive modelling use case.
Advantages of using ELT are
- Fast: There is no waiting involved in the ELT process. The tools will load data into your data warehouse in a matter of seconds, ready for a transformation.
- Flexible: You may simply integrate new and diverse data sources into the ELT process because transformations don’t have to be defined from the start.
- Low initial investment: ELT solutions can simply automate the data onboarding process. Because transformations aren’t required.
- Minimal Maintenance: The procedure is easier and more automated, requiring less maintenance. It’s easier to resolve errors in your transformation pipeline because it’s the last stage in the process. You can only re-run the revised transformation to get the right output
- High scalability: If your data usage grows, you may easily expand your cloud storage by using ELT procedures, which can easily adapt to such situations and manage data ingestion at huge volumes.
Disadvantages of using ELT are
- Data security concerns: When importing huge amounts of raw data into your storage, data security can be a worry. You’ll need to manage user and application access to raw data housed in your data warehouse to reduce security threats.
- Low data security protocol compliance: Because data is kept with minimum processing, you may need to take extra precautions to maintain data security protocol compliance.
|It support storing of structured data from input sources
|It can handle structured, unstructured, and semi-structured data.
|High because transformations must be completed before data can be stored
|Low, because just the most basic processing is done before the data is stored in the data warehouse.
|It is suitable for smaller amount of data
|It can be used for both small and large amount of data
|Low, because data sources and transformations must be defined at the start of the process
|High, as transformation need not be defined when integrating new sources
|Changes in data sources or formats may necessitate constant maintenance.
|ELT tools normally automate the process, there is little maintenance required.
|It’s suitable for both on-premises and cloud storage.
|Designed specifically for cloud data warehouses
|Compaliance with Security Protocols
|Because users can omit any sensitive data before loading it into the target system, ETL is more suited for GDPR, HIPAA, and CCPA compliance.
|Given that all data is put into the target system, ELT has a higher risk of exposing private data and failing to comply with GDPR, HIPAA, and CCPA regulations.
|Low as only transformed data is stored
|Can be high as raw data is stored
|For many small and medium organisations, ETL is prohibitively costly.
|ELT has access to a robust ecosystem of cloud-based platforms that provide significantly cheaper costs and a wide range of plan options for storing and processing data.
|Low, because the ETL tool should be able to scale processes.
|High, because ELT tools are simple to configure for different data sources
|The on-premises ETL method necessitates the purchase of costly hardware. ETL solutions that are cloud-based do not require any hardware
|No new hardware is required because the ELT process is fundamentally cloud-based.
In this ETL vs ELT article, we learned the advantagtes and disadvantages of both and also saw the differences between them. Anyway, ETL or ELT both help you with data integration in various ways. The optimal solution for you will rely on a variety of factors, including the data you own, the type of storage you need, and your company’s long-term demands.