ETL Vs ELT – Comparing Two Popular Data Integration Processes

Data is omnipresent and the competence to capture, store and analyze it is vital to a successful business. Integrating this bulk of data leads to evolving patterns and trends that can help identify business prospects in the future. Data is available in disparate formats – sheets, emails, documents, images, videos, databases, websites, etc. It needs a competent data integration process to collect all this data and process it effectively. Two popular data integration strategies – ETL Vs ELT are constantly compared for the same. Both sound similar and reach the same goal but have different routes of mechanism.ETL stands for Extract, Transform, Load, and ELT stands for Extract, Load, Transform. Both describe the approach to clean, enrich and transform data from a range of data sources, prior to using it for data analytics and BI. The terms stand for:

Extract means the process of obtaining data from different sources
Transform means converting the structure of a data set to meet requirements
Load means putting the data set into the targeted system

This write-up compares the two processes that sound alike but have significant differences between them. Before we compare them both, let us understand them separately.

What is ETL? An Overview

In computing, extract, transform, load (ETL) is a three-phase process where data is extracted, transformed (cleaned, sanitized, scrubbed), and loaded into an output data container.In the ETL process, data is extracted from source systems with the identification of keys. In the data integration process, the source systems could be multiple – databases, files, images, etc. It is then transformed on a secondary server and then loaded onto the target database. It is leveraged when data needs to adhere to the data management of a database.Here, data transformation is done in a staging area out of the warehouse, and it is ensured that all the data is transformed prior to loading. It yields clean data that is ideal for small data sets that need less updating. It works well with cloud data warehouses via cloud-driven SaaS platforms.

Salient Benefits of ETL

Complete automation of data flow
Security and adherence to standards for sensitive data
Visual drag and drop interface
Faster data analysis with structured data
Can be implemented either on-premises or cloud-based
Access to skilled resources with long-term expertise

Limitations of ETL

Loading speed is a little low
Inflexible workflow
Not suitable for large volumes of data

What is ELT? An Overview

Extract, Load, Transform (ELT) is a data integration process for transferring raw data from a source server to a data system on a target server and then preparing the information for downstream uses.In the ELT process, data is obtained from source systems and then directly loaded to the target database, for transformation to happen later. There is no data staging and it makes use of data warehousing for basic data transformation. There is real-time process update and hence offers good results.With the help of the ELT data pipeline, the cleaning and transformation of data happen inside the data warehouse itself. There could be multiple transformations after the loading happens. The transformation may take a little longer but avoids the migration slowdown. It dissociates the two stages of transformation and load to ensure smooth execution.

Salient Benefits of ELT

Real-time and flexible analysis of data
Ingestion of data in any format
Less cost and maintenance
The higher efficacy of resources
Readily available data in the warehouse
Faster loading and implementation time

Limitations of ELT

Lesser community spread
May not adhere to compliance standards
Time for analysis may go slow

The Similarities between ETL and ELT

Data integration approaches
Consolidate data from databases to data lakes/warehouse
The data pipeline includes cleaning and filtering
Offers an accurate and consistent source of data

Comparing the Two – ETL Vs ELT

	ETL	ELT
Overview	ETL involves extraction of data from the source system, transformation on a server, and loading onto a server.	ELT involves the extraction of data from a source system, loading it onto a designated server, and then transformation on a server.
Maturity	Well-known for over two decades with built protocols	Fresher form of data integration with less experience
Maintenance	Little more maintenance because of the secondary on-premises server	Less maintenance because of lesser systems involved and automated transformation
Data Transformation	ETL transforms data on a different server. It does not transfer raw data to the warehouse.	ELT transforms data within the data warehouse itself. It transforms data within the warehouse itself
Data Ingestion	Slower data ingestion with transformation on a separate server prior to load	Faster data ingestion with simultaneous data loading and transformation
Data Lake Compatibility	ETL is not compatible with data lakes	It has structured, semi-structured, and unstructured data
Type of Data Involved	It involves mostly structured data	Data analyst, data scientist, data engineer, BI analyst, etc.
Involved Costs	Fit for smaller data sets with complex needs	Costs can be less because of a simple data stack
Volume of Data	Fit for smaller data sets with complex needs	Fit for larger data sets that need faster performance
Hardware	The traditional, on-premises ETL needs costly hardware	Being newer, it needs less costly hardware
Compliance to Standards	Fit for complying with GDPR, HIPAA standards, etc.	Less suitable to standards since data is exposed while loading
Storage Type	Can be utilized for on-premises or cloud-based storage	Can be utilized for cloud-based storage
Latency Levels	High latency levels as transformation must be done prior to data storage	Low latency levels as minimum processing are performed prior to data storage
Flexibility	Lower flexibility as source and transformation must be defined at the start	Higher flexibility as transformation need not be mentioned at the start
Aggregations	Aggregation is difficult as the data set reduces in size	Aggregation is easier with a cloud-based system in place
Loading Time	Longer loading time because of the various stages involved	Faster loading since the data gets loaded only once
Retaining Raw Data	No complex raw data generation and hence not easy to query data	Makes an enriched historical collection for BI analytics for better querying

On a Wrapping NoteBoth data warehousing concepts are high in demand and have their own advantages. ETL is more flexible but needs extra resources for transforming data. ELT is also effective but needs the target database for managing raw data. Finally, it is up to the organization to decide which one to choose. There are many factors that it depends on, before finalizing the choice – available data, data needs, target database capabilities, storage type, long-term requirements of the business, project deadlines, budget, etc. Analyzing all these factors will offer a correct judgment in choosing the right data warehousing approach.We help clients collect, clean, and consolidate all kinds of data into a single repository. Our data architects build and manage ETL data pipelines using different data integration tools. Our data integrity services manage multiple varieties and volumes of data from different sources.Reach out to us for any kind of ETL or ELT jobs that you are looking for, in your organization.