Iva Works — Published On August 6, 2022 and Last Modified On August 8th, 2022
Beginner Data Engineering

This article was published as a part of the Data Science Blogathon.

Introduction

Data is ubiquitous in our modern life. Obtaining, structuring, and analyzing these data into new, relevant information is crucial in today’s world. Since contextual data exposes popular patterns and trends, we have arrived at the stage where businesses take data-driven decisions to grab windows of opportunities.

In an unprecedented generation of data, not only is the amount of data snowballing, but it is obtainable in varying formats and sources. Data is available in structured or unstructured formats from varying systems and resources like images, videos, websites, documents, etc. Statista predicts that the volume of data will expand to 97 zettabytes in 2022 and 181 zettabytes in 2025.

Categorizing these different data into a single source of truth, SSOT requires the use of ETL or ELT. ETL and ELT are necessary because sources of information rarely use uniform or accordant formats, either in structured SQL or unstructured NoSQL databases. Data requires cleaning, transformation, and enrichment before integration into an analyzable form to derive insights.

Whether using ELT or ETL, the process of transmuting and integrating data consists of three steps, extraction, transformation, and loading. The difference between ETL and ELT is the order in which they process the data. An important question then arises, which of them should you use? Is it advisable to convert your data before or after loading it into a data warehouse? Defining and understanding both methods separately helps to answer these questions.

For context:

With no particular order in mind, the meanings of E, L, and T are:

E – Extract: Extraction involves pulling data from the native database or source. The data then goes into a data lake storage system (ELT).

T – Transform: This step involves the change in the form of information to allow integration with the intended data system and the data such system contains.

L – Load: Loading involves depositing information into the data storage system.

What is ETL?

ETL is a process businesses use to gather, reformat, and store data to represent extract, transform, and load. The first step in the ETL process is extracting data from native databases. Next, the data goes through a cleanup and enrichment process. Finally, the data gets stored in a data warehouse.

etl

Because ETL requires data transformations before loading to the data store, pairing up in relational databases presents no issues. Unlike ELT, which works in tandem with data lakes and accepts structured or unstructured data.

How is ETL Important?

Is ETL still relevant in this age that data is cloud-based? The short answer to that is yes. The benefits ETL provides exceed extracting, cleaning, converting, and translocating data A to B. ETL also provides:

  • Context – Businesses can gain insightful historical context through data.

  • Consolidation – Consolidation of data simplifies analysis and reports.

  • Productivity – Productivity improves with replicable processes that do not need hand coding.

  • Accuracy – Improves data accuracy and auditing capabilities in compliance with standards and regulations.

ETL Pros

  1. Fast Analysis: After ETL rearranges and transforms the data, data queries gain rapidity and efficiency, unlike unstructured data.

  2. Compliance: To assure compliance with data privacy rules, ETL encrypts or removes sensitive data before loading them into the data warehouse.

  3. Environment Flexibility: implementation of ETL can be done on-site or in a cloud-based environment. ETL can take data from on-site systems and load them to a cloud database.

ETL Cons

  1. Rigid Workflow: Modifying the schema of the data warehouse may occur if the warehouse’s data layout does not support valuable new queries.

  1. Speed: As the ETL process involves a transformation in an area before loading, it is not directly available for use in contrast to the ELT, which is immediately available after extraction.

  2. Data Volume: ETL is unsuitable for handling large volumes of data because data transformation is time-consuming. It is perfect for smaller data sets that need more maneuvers because they provide crucial data for analysis.

What is ELT?

ELT means ‘extract, load, and transform,’ a process that assists in the data integration of businesses. Because the process uses a data warehouse to convert data, data staging is unnecessary. ELT employs cloud-based data warehouses to store all data formats, while transformation usually occurs on demand. Unlike ETL, where the data transforms before storage.

elt

The data stored in a data lake that accepts all forms or formats of raw data allows the fluid change in the order of phases in ELT. Once the capturing of data occurs, it loads immediately and then awaits transformation at a later time for analysis.

ELT Pros

  1. Flexible Data Formats: ELT paired with a data lake accepts data of all formats.

  2. Speed of Loading: There is immediate access to data after extraction because the data transformation occurs after loading.

  3. High Data Availability: Data is always available because data load goes to the data lake. Tools (that don’t necessarily need structured data) can easily access these data instantly instead of waiting until data transformation.

  4. Efficiency: Since data transformation usually occurs during analysis, in contrast to converting all the data before loading, resource use is better.

ELT Cons

  1. The flexibility of Environment: Harnessing the advantage of ELT requires pairing with cloud-based processing power and storage.

  2. Compliance: ELT’s integration with the cloud raises unease about data privacy because several regulations are against data storage on servers outside specific borders.

  3. New Approach: As ELT is recent in its development and after cloud computing has attained maturity, it does not have the backing of a large community behind it. Yet.

ETL vs. ELT

The traditional ETL and ELT processes differ more than the switch in positions between the T and L. The location, time, and how data transformation takes happen to be the major determinants.

  • In the ETL process, no raw data is found in the data warehouse because transformation occurs before loading. While in the ELT process, raw data is available in the data lake, and data transformation occurs after data storage.

  • Although ETL and ELT use staging areas, ETL has its staging areas built into the ETL tool. ELT staging areas are in the database.

  • In use for business intelligence and big data analytics, ELT is more functional in processing sizeable data sets. As ELT copies the data in its original form from the source, unstructured and non-relational data are better. A ‘schema on reading” approach functions fine when using analytics on unstructured data.

  • ETL is the preferable approach in contrast to ELT, where large-scale data cleansing before loading the data into a system is necessary.

A comparison between ETL and ELT:

ETL ELT
Order of Process Extract, Transform, Load Extract, Load, Transform
Flexibility Since ETL always follows a linear process, it is inflexible. As transformation is undefined from the start, it leads to a more flexible process.
Source Data Stores structured data. Supports structured, semi-structured, and unstructured data.
Storage Type Function on-site or through the cloud. Performs better with cloud data warehouses.
Data size Suitable for small data sets. Suitable for sizeable data sets.
Scalability Low. High and can be configured to fit changing data sources.
Storage Requirement Low since only the data that transforms goes into storage. Due to the storage of raw data, the storage requirement is usually high.
Hardware Requirement The hardware usually assists in carrying out transformation. ELT tools usually employ the use of available computing powers to convert data.
Complexity of Transformation Data integration professionals with experience in ETL code transformations in the tool. Programmers write transformations (using Java, for example), and the converted data needs maintenance.
Skills Performing extraction, transformation, and loading require training and skills. Since ELT relies primarily on native DBMS functionality, existing skills are applicable.
Suitability Analysts and Data Scientists. SQL coders and report reading users.

ELT or ETL?

Selecting which to use between ETL and ELT all boils down to which of them best serves the data integration needs for the situation. To find out which one suits your use case, evaluate:

ELT Use Case

You

  • Handle lots of data

  • Require quick storage

  • Carry out different styles of analysis

  • Frequently change data sources

  • Possess processing resources

ETL Use Case

You(r)

  • Work with small to medium data sets

  • Data is consistent

  • Analytics must meet the benchmark

  • The business has its storage

  • The cost of data storage is of essence

Conclusion

ETL and ELT processes amplify data quality and improve integration in their own ways. The best approach for a business is the one that best suits the business’s needs.  It is not debatable that either ELT or ETL boosts the visibility of enterprises and data integration.  

Some key takeaways from this article include:

  • Despite ELT’s advantages, several teams will stick to ETL for their deployment, regardless of its legacy infrastructure.
  • ETL functions best in structuring data leading to comprehensive analysis. ELT serves best in supporting all data formats and providing speed. 
  • ETL’s approach aids in quicker, more efficient, and stable data analysis after structuring and transforming the data. Utilizing the data pipeline also ensures compliance standards adherence while handling sensitive data.
  • ELT’s approach ensures ease when storing new, unstructured data and provides flexibility. Also, up-take of data occurs without complex processes, saving time and increasing efficiency.

The answer lies in assessing their strengths and limitations before selecting either for your business data operations needs.

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