ETL vs. ELT: What’s the Difference?

Quick Summary

Selecting the most effective data integration technique between ETL vs. ELT for their project is one of the most significant issues CTOs confront when constructing a data pipeline. By the conclusion of this piece, you can decide which method is ideal for your business after learning about the two main data integration techniques, ETL and ELT. So relax and grab a coffee (or tea) as we enter the fascinating data integration realm.

What is ETL?

The data integration process known as ETL, or extract, transform, and load, brings together data from several data sources into a single, consistent data store that is then put into a data warehouse or other destination system.

ETL, a procedure for integrating and loading data for calculation and analysis, was established as databases gained popularity in the 1970s. Eventually, it became the main way to process data for data warehousing projects.

How Does it Work?

Understanding what occurs at each stage of the process is the simplest approach to comprehend how ETL functions.

Extract

Raw data is transferred or exported from source locations to a staging area during data extraction. Data may be extracted from a range of structured and unstructured data sources by data management teams. They include, but are not restricted to:

• servers using SQL or NoSQL
• ERP and CRM software
• The flat file
• Webmail pages

Transform

The staging area is where the raw data is processed. For its intended analytical use case, the data is changed and consolidated in this place. The following tasks may be involved in this phase:

• The data is filtered, cleaned, duplicated, validated, and authenticated.
• Use the raw data to do computations, translations, or summaries. This may entail modifying text strings, updating row and column headings to ensure uniformity, converting money or other units of measurement, and more.
• Conducting audits to verify data compliance and quality
• Deleting, encrypting, or safeguarding information under the control of authorities such as the government or industry
• Data formatting to create tables or combine tables in accordance with the destination data warehouse’s structure.

Load

The converted data is sent from the staging area into the target data warehouse in this final stage. This often entails initial loading of all data, recurring loading of incremental changes to the data, and, less frequently, full refreshes to completely remove and replace all data in the warehouse.

Most businesses that employ ETL have automated, well-defined, batch-driven processes that are continuous.

ETL often takes place after business hours when usage of the data warehouse and source systems is at a minimum.

What is ELT?

Another method of integrating data is called ELT, or “Extract, Load, Transform,” and it functions similarly to ETL, or “Extract, Transform, Load.” This procedure transfers unprocessed data from a source system to a resource, such a data warehouse.

ELT is a fundamentally new technique to data pre-processing that, while similar to ETL, has just lately become more popular with the shift to cloud platforms.

How Does it Work?

ELT consists of three primary stages; Extract, Load, and Transform. Each of these stages is detailed below.

Extract

Data is exported or transferred from source sites to a staging area during data extraction. The data set may include a wide variety of data types and may originate from almost any structured or unstructured source, including but not restricted to:

• Servers using SQL or NoSQL
• ERP and CRM software
• documents and text files
• Webmail pages

Transform

An strategy known as “schema-on-write” is used in this step to apply the data’s schema using SQL, or to alter the data before analysis. These activities could be part of this phase:

• the data is filtered, cleaned, duplicated, validated, and authenticated.
• use the raw data to do computations, translations, data analysis, or summaries. This might involve anything from consistent row and column header changes to currency or measurement unit conversions, text string altering, adding or averaging numbers, and anything else required to meet the organization’s unique BI or analytical needs.
• deleting, encrypting, burying, or otherwise guarding information that is subject to governmental or commercial rules.
• transforming the data into tables or connected tables in accordance with the warehouse’s deployed schema.

Load

This process involves moving the converted data from the staging location into a data storage space, like a data warehouse or data lake.

For the majority of businesses, the data loading process is automated, well defined, ongoing, and batch-driven.

ELT frequently takes place during business hours, when there is a large volume of traffic on the source systems and the data warehouse and users are eager to use the data for analysis or other purposes.

Key differences: ETL vs ELT

ETL has been enhanced in a number of ways by extract, load, and transform (ELT).

Transform and load location

Both transformation and load take place in various places and using different procedures. On a server used for secondary processing, the ETL process transforms data.

The ELT method, in contrast, uploads raw data right into the target data warehouse. The data may then be transformed whenever you need it. 

Data compatibility

Structured data that can be represented in tables with rows and columns is best suited for ETL. It loads it after converting one piece of structured data into another structured format.

ELT, on the other hand, can handle any kind of data, including unstructured data like pictures or papers that can’t be stored in a tabular manner. The different data formats are loaded into the target data warehouse using ELT. You may then further change it into the format you need.

Speed

Faster than ETL is ELT. Before loading data into the destination, ETL performs an additional step that is challenging to scale and slows the system down as data size grows.

ELT, on the other hand, feeds data directly into the system of target while simultaneously transforming it. In order to provide real-time or almost real-time data transformation for analytics, it makes use of the parallelization and processing capacity that cloud data warehouses have to offer. 

Costs

Analytics must be involved in the ETL process from the beginning. Analysts must set up data structures and formatting for the reports they intend to create in advance. Costs rise as a result of an increase in setup time. It could be more expensive to add servers for transformations.

Since all transformations take place within the target data warehouse, ELT uses fewer systems than ETL. Less systems means less maintenance, which results in a simpler data stack and less setup money.

Security

You must adhere to data privacy laws while working with personal information. Companies are required to prevent unauthorised access to personally identifiable information (PII).

Developers must provide unique solutions for ETL, such as masking PII to watch over and secure data.

ELT systems, on the other hand, offer a number of security measures right inside the data warehouse, such as multifactor authentication and granular access control.

You may devote more time to analytics and less time to ensuring that your data is compliant with regulations.

Pros and Con of ETL

Pros of ETL

Lightning Fast Analytics: One of the main advantages of ETL is that it enables companies to do speedy analyses on structured data. Data is converted before being loaded into a storage system, making it simple to analyse and comprehend. Businesses that need fast data insights for decision-making or trend detection will find this to be of great use.

Increase Security: ETL also ensures adherence to security regulations like the EU’s General Data Protection Regulation (GDPR). In contrast to ELT, which loads raw data without transformation, ETL transforms the data before loading it, removing sensitive information and ensuring rule compliance. As a result, businesses gain an extra layer of safety.

Reduce Storage Costs: ETL simply saves pre-structured data, which considerably minimises the amount of storage space needed. Because they won’t need to spend as much on storage options, companies that pay for storage can save money.

Rich Ecosystem: ETL has been around for more than 20 years, therefore it is supported by a sizable infrastructure and set of tools. As a consequence, with the aid of the many resources available, it is simpler to adopt ETL in your organisation.

Cons of ETL

Resource-Intensive: The high initial cost of ETL is one of its key drawbacks. Especially if a company intends to have on-site data storage, the cost of ETL might reach the hundreds of thousands of dollars range. Even if the storage is cloud-based, the initial cost is still considerable since a transformation algorithm needs to be built. For companies operating on tight financial resources, this might be a serious setback.

Not very Flexible: Another drawback of ETL is that it is less flexible than ELT. Data must be exported and transformed before being loaded, which necessitates a complex transformation method, making it challenging to switch or add new data sources. This might be a serious problem for firms that constantly alter their data sources or need to add new ones.

Slow down data processing: ETL may also make working with big volumes of data more challenging. The system’s transformation stage can create a bottleneck, slowing down data processing overall. Businesses who deal with massive volumes of data every day and need real-time insights may find this to be a serious problem.

Require a lot maintenance: ETL procedures are more maintenance-intensive with onsite solutions using real servers. The need for routine maintenance raises expenses and reduces developer productivity. However, cloud-based ETL systems require less regular maintenance because of automated operations.

Pros and Cons of ELT

Pros of ELT

Faster Data Loading: ELT’s ability to load massive volumes of raw data significantly quicker than ETL systems is one of its key advantages. Because it enables them to swiftly analyse and comprehend their data, this is especially helpful for firms that work with large data.

Wide range of analytics options: ELT is also excellent for in-depth analytics. Businesses can apply both simple and complex modifications to get insights into certain topics or historical data because a lot of raw data is kept. Since ETL only saves pre-structured data, this is not possible.

Easy Maintenance: Most ELT systems do not need organisations to invest in on-site storage, therefore maintenance is also not an issue. Many businesses that provide ELT services also handle maintenance, allowing your personnel to concentrate on more crucial responsibilities.

Cons of ELT

Tough Implementation: The main drawback of ELT is that it can be expensive and challenging to set up. Finding workers or contractors with a high degree of knowledge might be difficult because ELT is a relatively new technology. Additionally, setting up an ELT pipeline requires large resource investments from enterprises.

Expensive to run: Businesses may find it costly to operate an ELT system since most ELT solutions charge by the volume of data being converted for each query. For small organisations who lack the means to conduct analytics, this can be a significant problem.

When should you use ETL?

1. Structured Data Transformation: ETL is nicely applicable for eventualities wherein statistics wishes giant transformation earlier than it’s miles loaded into the goal information warehouse. If your facts sources offer uncooked statistics that want to be cleaned, standardized, or aggregated into a particular layout, ETL can be helpful.

2. Data Cleansing and Validation: When outstanding records are a number one trouble, ETL is most wonderful. It allows you to perform information cleansing, validation, and enrichment before loading it into the data warehouse. This is critical for retaining facts, accuracy and consistency.

3. Historical Data Integration: ETL is frequently used for historic facts integration wherein historical facts from numerous sources want to be transformed and loaded into an information warehouse for historical evaluation.

4. Compatibility with Legacy Systems: ETL can be the selection while your agency relies on legacy structures that generate statistics in a layout incompatible with your aim information warehouse. ETL can function as an intermediary step to make statistics compatible.

5. Security and Compliance: ETL can be high-quality at the same time as strict statistics protection and compliance requirements necessitate custom records coping with encryption techniques in advance rather than loading information into the warehouse.

6. Upfront Planning: ETL calls for thorough planning and earlier design of facts changes, making it suitable for situations where careful attention to statistics structures and formatting is needed.

When should you use ELT?

1. Semi-Structured or Unstructured Data: ELT shines even as coping with semi-based or unstructured statistics like JSON, XML, log files, or multimedia content material cloth. It lets you load this fact into the intention information warehouse, after which you take a look at alterations as desired in the warehouse.

2. Real-Time Data Transformation: ELT is quicker for real-time or near-actual-time information transformation and assessment. It takes the gain of cloud data warehouses’ processing energy and parallelization to provide well-timed insights.

3. Cloud-Based Data Warehousing: If your business enterprise uses cloud statistics warehouses like Amazon Redshift, Google BigQuery, or Snowflake, ELT is a herbal healthful. These systems are designed to deal with records modifications successfully within the warehouse.

4. Cost Efficiency: ELT frequently includes lower setup prices and is extra price-effective simultaneously as you need to leverage the prevailing skills of contemporary cloud statistics warehouses for transformation, lowering the need for introduced infrastructure.

5. Flexibility and Agility: ELT offers flexibility to save uncooked statistics in its close-by layout in the warehouse, permitting statistics analysts and scientists to find out and redesign it as desired without being high quality via predefined structures.

6. Focus on Analytics: ELT lets groups shift their attention towards analytics and insights era in place of spending significant assets on facts transformation duties.

ETL Tools

Software called ETL tools is made to support ETL processes, which include extracting data from various sources, cleaning it up for consistency and quality, and storing it all together in data warehouses.

When used appropriately, ETL technologies offer a standardised method to data input, exchange, and storage, which simplifies data management techniques and enhances data quality.

AWS Glue: AWS Glue is a cloud-based data integration solution that helps both technical and non-technical business users. It supports both visual and code-based clients. The serverless platform has a variety of capabilities that may do extra tasks, such the AWS Glue Data Catalogue for locating data throughout the company and the AWS Glue Studio for graphically creating, running, and updating ETL pipelines. Custom SQL queries are now supported by AWS Glue for more direct data connections.

Azure Data Factory: Azure Data Factory is a serverless data integration solution that grows to match compute demands and is based on a pay-as-you-go approach. The service can pull data from more than 90 built-in connections and provides both no-code and code-based interfaces. In order to offer sophisticated data analysis and visualisation, Azure Data Factory also connects with Azure Synapse Analytics. Additionally, the platform supports Git for version control and DevOps teams’ continuous integration/continuous deployment workflows.

ELT Tools

Airbyte: Data teams may rapidly and effectively extract data from a variety of sources and load it into target repositories using the open-source ELT application Airbyte. It offers real-time monitoring, fault logging, and 140+ pre-built interfaces. Updates may also be scheduled. Airbyte supports custom transformations and connects with cutting-edge systems like Kubernetes, Airflow, and debt.

Fivetran:

An ELT tool called Fivetran provides the following comprehensive data integration services:

• data transformations being used
• Having integrated version control
• Documentation
• Testing
• checking for errors and monitoring
• repairing faulty procedures

Tagline can help you choose the right data integration approach

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Conclusion

In conclusion, I would like to say that ELT and ETL processes are different from each other. ETL follows the outline processes like extract, transform, and load according to the structural requirements of the database while ELT follows the outline processes like extract, load, and transform to interact with the data as many times as needed.

FAQ’S