ETL vs. ELT: What you need to know
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Both the data integration methods, ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), pass source data to a data center.
For on-premises, relational and structured data, the ETL methodology is used, while ELT has been used for scalable, cloud-based structured and unstructured data sources.
What is ETL?
ETL is a data integration approach that pulls raw data from sources, transforms it on a secondary processing server, and then puts it into a final database.
When data must be modified to fit a target database’s data format, ETL is used.
The approach first appeared in the 1970s and is still widely used in on-premise databases with limited memory and processing capacity.
Take a look at an example of ETL, Database systems that use Online Analytical Processing (OLAP) only support relational SQL-based data structures.
A methodology like ETL maintains compliance in this type of data repository by sending extracted data to a retrieval server and then converting non-conforming data into SQL-based input.
The extracted data is only deleted from the processing server once it has been successfully transformed and moved to the final data system.
For example, organizations that need to migrate and update data from an existing system include: The ETL procedure is used to convert data from older systems into a format that is compatible with the target database’s new structure.
Use cases of ETL:
Turning raw data into actionable insights
ETL solutions make it feasible to turn large amounts of data into meaningful business knowledge. Data strategies are more sophisticated than they’ve ever been.
Consider the sheer volume of raw data at your disposal.
To be analyzed, all of the data must be extracted, transformed, and loaded into a new location. Data management, business intelligence, data analytics, and machine learning are all made possible through ETL.
Providing a single standpoint
Managing many data sets in a corporate data world requires time and effort, which can lead to inefficiencies and delays. ETL is the process of combining databases and other types of data into a single, cohesive picture. This makes enormous datasets simpler to collect, analyze, display, and make sense of.
Providing historical background
ETL enables the integration of historical corporate data with information gathered from new platforms and apps. This results in a long-term perspective of data, allowing older datasets to be examined alongside more recent ones.
Enhancing quality and performance
ETL software automates the process of hand-coded data transformation and input. As a consequence, developers and their teams can focus on innovation rather than the time-consuming chore of creating code to transport and prepare data.
What is ELT?
ELT does not require data transformations prior to the loading phase, unlike ETL.
Instead of transferring raw data to a processing server for transformation, ELT installs it simply into a target database system.
Data entry, improvement, and conversion all take place within the data center when using ELT. The data center saves raw data permanently, allowing for various transformations.
Snowflake, Amazon Redshift, Google Big Query, and Microsoft Azure are examples of cloud data centers that offer the storage and processing control to support raw information systems and in-app conversion.
For example, Stock exchanges create and use vast amounts of data in real-time, and delays can be harmful. Large-scale material and component providers also require real-time access to current data for business analytics.
Use cases of ELT:
Real-time assessment
Since the destination system can perform data transformation and loading in parallel, ELT has a significant impact on data integration time. In turn, this enables real-time analytics.
Handles enormous volumes of structured and unstructured data.
A shipping business that employs tracking devices need to process vast quantities of different data supplied. All of this data processing takes a substantial amount of resources and investments in those resources. ELT is a method for saving money and improving performance.
Cloud-based ventures and hybrid architectures.
Even though current ETL has extended its doors to cloud warehouses, transformations must be performed by a separate engine before data is loaded into the cloud. ELT eliminates the requirement for building intermediary processing engines, making it a superior option for cloud and hybrid use cases.
Effective for a data science team that requires access to all raw data for machine learning applications.
It is beneficial to leverage the great scalability of modern cloud warehouses and data lakes.
Key differences between ETL Tools and ETL Scripts:
| Parameter | ETL Tools | ELT Scripts |
| Definition | A source system’s data is extracted, converted on a secondary processing server, and loaded into a destination data warehouse. | Data is retrieved from one system, fed into another, and modified in the destination. |
| Code Required | Transformations that are computationally intensive for a small bit of data. | For large amounts of data |
| Time-Load | Data is first loaded into staging, then into the target system. It is time-consuming. | Data is only loaded into the target system once. Quicker. |
| Needs | Because data is changed before entering the destination system, raw data cannot be accessed again. | Raw data is directly imported into the destination system and can be accessed indefinitely. |
| Complexity | Simpler with a drag and drop UI | Needs skilled workers with scripting knowledge and therefore, complex |
| Data Volume | Ideal for small data sets which involve extensive transformations. | Ideal for large datasets that require speed & efficiency. |
| Cost | High costs for small and medium-sized businesses. | Low entry costs using online Software as a Service Platforms. |
| Maintenance | Secondary processing servers increase the maintenance burden. | The maintenance burden is minimized when there are fewer systems. |
| Data Output | Structured (generally). | Structured, semi-structured, unstructured. |
| Hardware | The majority of tools have specific hardware requirements that are costly. | Because SaaS is used, hardware cost isn’t an issue |
| Lookups | Both facts and dimensions must be available in the staging area during the ETL procedure. | Because extract and load are combined into one process, all data will be available. |
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ELT – The sophisticated data expert
Reading data or using the “Extract” function puts the least amount of strain on a highly available operational system. Instead of producing an intermediary flat file, current connectors copy data from one database platform to another with a comparable workload on the OLTP side.
As a result, flat files in the data warehouse structure have been substituted with tables, enabling raw data to be “loaded” first. We now have a version of the necessary operational data that we can clean, standardize, filter, mask, and aggregate. The majority of the stages can be done using SQL queries within the data warehouse environment.
DataSwitch makes migration to the cloud easier than ever!
It’s time to reduce your costs and adopt a time- and cost-efficient solution to your data issues.
The migration from ELT/ETL to ETL/ELT is enhanced by our sophisticated tools and processes, as well as our combined expertise. We understand the purpose of stored procedures and have developed platform-critical functionality. From script conversions to migration planning, data migration, and data intake, we have your back.
DataSwitch leverages process converter to seamlessly migrate the workloads. Now you can easily convert your legacy data scripts and ETL tools using DataSwitch’s “No-Touch Code” easy online interface. We provide tool-to-tool, script-to-tool, a tool-to-script, or script-to-script translations as well. We conduct this process irrespective of the source systems and support both ETL and ELT procedures.
In our next blog, we will talk about Matillion. Matillion ETL is a cloud database ETL/ELT tool designed exclusively for Amazon Redshift, Google BigQuery, Snowflake, and Azure Synapse. It has a sophisticated, browser-based user interface and strong, push-down ETL/ELT capability. It can be up and running in minutes with a quick setup. Stay tuned.
