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 Note
Both 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.
Note: This Post Was First Published On https://ridgeant.com/blogs/etl-vs-elt-comparison/
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