ETL and ELT: Which to use and why?
What Is ETL?
ETL is an abbreviation that means "Extract, Transform, Load." It involves the transfer of data between two locations. "Extract" refers to a term that typically means to draw raw data out of a source system. These sources may be a database, spreadsheet, API, or web application. The term "transform" states that it cleans the data and restructures it before sending it to its destination. "Load" is used to refer to loading the cleaned data into a storage mechanism such as a data warehouse.
Consider the ETL as laundry prior to stuffing it in a suitcase. Clean first and put away.
A retailing company gathers sales information from five branches of stores. The data is stored in a different format and is stored in each branch. The ETL process obtains all this data, corrects the formatting disparities, and eliminates any duplicates, then loads a single clean, consolidated table into the central database of the company. Reporting by analysts can now be done without messy data.
ETL has been utilized for decades. Its popularity came at a time when storage was costly, and companies could not afford to store raw, unprocessed data. It was also time-saving to transform data prior to loading it and maintain clean warehouses.
What Is ELT?
ELT is used as an abbreviation to mean "Extract, Load, Transform." The processes are nearly similar to ETL, except that these last two processes are reversed. Data are extracted from the source and loaded into the destination system in their raw form and transformed within that destination system.
ELT can be imagined as putting all your clothes in the suitcase and sorting them out when you get to the hotel.
One tech startup transmits millions of logs of user activities daily in the company's mobile application. The group puts all those crude logs in a cloud data warehouse such as BigQuery. After the data is in, data analysts then write SQL queries to clean and convert the data into useful reports. The raw data remains accessible to anybody in need of it in the future.
ELT was made popular by the fact that cloud storage became very cheap, and cloud data warehouses became very powerful. Snowflake, Google BigQuery, and Amazon Redshift are examples of platforms that have the ability to perform large transformations through their own internal computing capabilities.
The major differences between ETL and ELT.
1. The location of the transformation.
In ETL, conversion occurs out of the destination system. The heavy lifting is done by a separate tool or server prior to the arrival of the data. Transformations, in ELT, occur within the destination system once the data has been loaded.
This is important due to its impact on speed, cost, and flexibility.
2. Speed of Loading
ETL may be slow to load since the data has to be cleaned prior to being loaded into the warehouse. ELT is much faster in loading data as raw data are directly transferred into the storage with no pre-processing.
3. Data Storage
Only clean, transformed data is stored in ETL. Transformation usually results in the discarding of the raw version. Raw data is always stored in ELT, and thus, the original information does not get lost. Later, teams can re-transform the same data with other rules.
4. Flexibility
ETL pipelines are stiff. In case of a change in business rules, the pipeline will have to be constructed again. ELT is more lenient. As the raw data remains intact, the analysts can add new transformation queries without having to touch the pipeline itself.
5. Cost
ETL needs an additional transformation server or a tool, which increases the costs. ELT is based on the computing power of the cloud warehouse itself, which can be less expensive, depending on the size of operations.
6. Skill Requirements
ETL frequently demands engineers familiar with particular software, such as Informatica or Talend. ELT frequently involves individuals familiar with SQL, a more common skill.
| Feature | ETL | ELT |
|---|---|---|
| Order | Extract → Transform → Load | Extract → Load → Transform |
| Where Transform? | Outside warehouse | Inside warehouse |
| Raw Data Stored? | No | Yes |
| Speed to Load | Slower | Faster |
| Flexibility | Lower | Higher |
| Best For | On-premise systems | Cloud-based systems |
| Common Tools | Informatica, Talend, SSIS | dbt, BigQuery, Snowflake |
ETL in real-world applications.
1. Banking and Finance
Banks process transactions made on ATMs, mobile applications, and branch counters. All these data are in various formats. All the records are standardized by an ETL pipeline, and any unfinished ones are removed and then loaded into the reporting database. Banks cannot just afford to store bad information, as it directly impacts financial reports and regulatory compliance.
2. Healthcare
Patient data is gathered by the various systems at hospitals. There is a system that holds lab results, a system that holds prescriptions, and a system that holds appointment history. ETL consolidates all such data in a single clean patient record. The step of transformation is important, as any incorrect data format in a medical record can lead to grave consequences.
3. Legacy System Migration
ETL is usually utilized when a firm is changing its database system to a new one. The existing data is taken out, cleaned to suit the new system, and finally inserted into the new database.
ELT use cases in the real world.
1. E-commerce Analytics
Even an online shop such as a Jumia or Amazon-type store gathers billions of clicks, searches, and purchases on a daily basis. All of this raw data is directly stored in a cloud warehouse. The next step is writing SQL queries by data teams to identify trends, such as the most commonly viewed products prior to a purchase. The raw data remains open to analysis in the future.
2. Social Media Platforms
ELT is highly utilized in platforms that follow the user behavior, likes, shares, and watch time. The amount of data is too huge to convert prior to loading. The only feasible way is to load raw data quickly and to transform data at a later stage within the warehouse.
3. Start-ups and emerging firms.
Early-stage companies are not always sure of the questions that they will pose to their data in the future. ELT allows storage of raw data and enables users to explore. On the occurrence of a new business query, analysts write new transformation queries without the reconstruction of any pipeline.
Tools Used in ETL
One of the most robust and reliable ETL tools is Informatica PowerCenter. It is popular in big businesses in the banking and insurance sectors.
The Microsoft SSIS (SQL Server Integration Services)is an inbuilt part of the Microsoft ecosystem. SQL Server databases are already in use by companies, so they are likely to select SSIS to perform ETL workflows.
Talend is an ETL tool that has free and paid versions and is an open-source tool. It is linked to hundreds of data sources and allows a visual interface to be used to construct transformation logic.
Apache NiFi is a system created to transfer data between systems. It manages data routing, transformation, and scaling of data.
Another free alternative that is being used by many mid-sized businesses to create ETL pipelines without incurring high licensing fees is Pentaho.
Tools Used in ELT
The most used tool of transformation steps during ELT is the dbt (Data Build Tool). SQL models are written in dbt by data engineers, and dbt executes such transformations directly on the data warehouse, along with automatically tracing documentation and data lineage.
Google BigQuery is a cloud data warehouse capable of housing raw data and performing large-scale SQL transformations at scale. A lot of companies rely on BigQuery as the heart of their ELT.
Another example of a cloud-based warehouse is Snowflake, where storage and computing capabilities are independently handled. The design renders it highly economical for ELT processes.
Amazon Redshift is a cloud data warehouse by AWS. It can be easily integrated with other AWS services and is a powerful option for teams already on the Amazon cloud.
Fivetran and Airbyte/ are software that manage the ELT extract and load stages. They are linked to hundreds of data sources and automatically synchronize raw data to the warehouse. DBT then performs the transformation step.
Which to use and which to leave.
These questions need to be asked prior to selecting an approach.
- Is the amount of data extremely large?
High volumes prefer ELT since unprocessed information can be loaded in the shortest amount of time and processed in the future with the help of the computing ability of the warehouse.
- Is storage cheap?
It is cheap today to store data in the cloud. ELT is much more appropriate in clouds. Storage: On-premise storage is more costly, and the ETL's strategy of only storing clean data remains valid for older systems.
- Is there a high rate of business rule change?
Regular amendments of the rules are in favor of ELT. The raw data remains the same, and additional transformations can be written without modification of the pipeline.
- Do you have any concerns over data privacy?
ETL has the capability to cover up or anonymize sensitive data even prior to its coming into the warehouse. This is critical in the fields of health care and finance, where uncontrolled keeping of raw personal data should not be allowed.
- What are the skills of the team?
SQL-based teams have the ability to work more swiftly with ELT and dbt. Experienced teams that are familiar with certain ETL tools might find it easier to adhere to the ETL model.
A real-life application of a combination of the two.
There are numerous actual businesses that adopt a hybrid model. ETL may be used by a company to strip out any personally identifiable information in customer records prior to loading them. It is then sensitively cleaned up, and the rest of the data is stored in the warehouse as raw data with ELT making all further transformations. This offers the best of both worlds: the protection of data privacy, which ETL provides, and flexibility, which ELT provides.
Final Thoughts
ETL and ELT are solutions to the same issue in dissimilar ways. ETL standardizes data prior to storage and is most effective in structured settings that have rigid data guidelines. ELT is a data storage that is transformed subsequently and is therefore the best fit for cloud platforms that deal with large and fast-moving data. The decision to make is based on the tools available in the team, the amount of data, the storage system, and the frequency of business requirements. Each method is valid, and each is extensively utilized in the data engineering industry today.
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