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Table of contents
Understanding ELT
Working of ETL
Benefits and Challenges of ETL
ETL Tools
Prioritizing Big Data Quality
Using Hadoop as ETL
Frequently Asked Questions
Write some of the importance of ETL testing?
Mention some of the tools that can be sued in ETL testing.
What do you understand by data warehouse?
What is Data Mining?
Last Updated: Mar 27, 2024

ETL in Big Data

Author Naman Kukreja
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Leveraging ChatGPT - GenAI as a Microsoft Data Expert
Prerita Agarwal
Data Specialist @
23 Jul, 2024 @ 01:30 PM


Every programmer and developer needs to deal with data. The requirement for data is increasing day by day. Most of the services you use in your day-to-day life require data so that they can provide you the result accordingly.

So have you ever wondered how the application or website processes the data we entered and gives the result accordingly?

This happens because of Extract Transform and Load procedures. In this, we get all your data, transform it according to t use requirements, and then provide you with the modified data. We will learn all about these while moving further in the blog, so let’s get on with our topic without wasting any time further.

Understanding ELT

ETL (extract, transform, and load) is a process that integrates the data from several sources into a consistent and single data store that is then put into a destination system or data warehouse.

ETL was established as a procedure for loading and integrating data front, calculation, and analysis as databases became more popular in the 1970s, ultimately becoming the dominant method for data warehousing initiatives

 for processing data.

Machine-leaning workstreams and data analytics are built on top of ETL. ETL cleanses and organizes data using business rules to meet Business Intelligence objectives, such as monthly reporting. Still, it can also handle more complex analytics to enhance back-end operations or end-user experiences.


The most noticeable distinction between ETL and ELT is the sequencing of operations. ELT copies or exports data from the source locations. Rather than putting it into a staging area for transformation, it puts the raw data straight into the destination datastore, which will be changed as required.

While both methods employ a range of data repositories, including databases, data warehouses, and data lakes, they each have their benefits and drawbacks. ELT is especially effective for large, unstructured datasets since it allows for direct loading from the source. Because data extraction and storage do not need significant preparation, ELT may better fit extensive data management. On the other hand, the ETL process requires a more excellent upfront definition. Specific data points and any relevant "keys" must be identified for extraction and integration across diverse source systems. The data transformation business rules must be built even once that task is accomplished. This effort often depends on the data requirements for a particular form of data analysis, which will define the extent of data summarization required. While the introduction of cloud databases has boosted ELT's popularity, it comes with its own set of drawbacks, such as the fact that best practices are still being defined.

Summarising the above discussion, Extract, load, and transform (ELT) is an acronym meaning extract, load, and transform. It does the same tasks as ETL but in a different sequence. Databases in the early days lacked the technological capacity to alter data. As a result, ETL tools extracted the data to an intermediate place where the transformation could be completed before putting it into the data warehouse. However, this limitation is no longer an issue due to technological advancements such as massively parallel processing systems and columnar databases.

Consequently, without needing an ETL server, ELT tools may convert data in the source or destination database. When it comes to massive data, why should you utilize ELT? Performance is improved, and scalability is more straightforward. The data is transformed using ELT's structured query language (SQL). Many classic ETL programs also provide ELT, allowing you to choose between the two based on your needs.

Working of ETL

In working, we will see the working of every step.


Raw data is exported to a staging area from source locations during data extraction. The data management team can extract the data from available sources and in both structured and unstructured forms. The sources can be:

  • SQL servers
  • Flat Files
  • Web Pages
  • Email
  • CRM and ERP systems


The raw data undergoes data processing in the staging area. The data is converted and consolidated in this step to prepare it for its intended analytical use case. The following tasks can be included in this phase:

  • The data is filtered, cleansed, de-duplicated, validated, and authenticated.
  • Using raw data to do computations, translations, or summarizations. Changing row and column headings for uniformity, converting currencies or other units of measurement, modifying text strings, and more are all examples.
  • Audits are carried out to guarantee data quality and compliance.
  • Removing, encrypting, or safeguarding data that is regulated by business or government
  • To meet the schema of the destination data warehouse, the data is formatted into tables or connected tables.


The converted data is transported from the staging area to the target data warehouse in this last stage. This usually entails a complete load of all data, followed by periodic loading of incremental data updates and, less often, full refreshes to wipe and replace data in the warehouse. The process is automated, well-defined, continuous, and batch-driven in most enterprises that employ ETL. ETL is often performed during off-peak hours when traffic on the source systems and the data warehouse is minimum.

Benefits and Challenges of ETL

ETL systems perform data cleaning before importing data into a separate repository, which improves quality. ETL (extract, load, transform), change data capture (CDC). Data virtualization integrates increasingly larger volumes of data that change or real-time data streams, while ELT (extract, load, transform) changes data capture (CDC). Data virtualization integrates increasingly larger volumes of data that change or real-time data streams.

ETL Tools

There are many commercial and open-source tools available to use their services. The features of the tools are discussed below:

  • Ease of use: Leading ETL technologies to automate the whole data flow from data sources to the destination data warehouse. For extracting, processing, and loading data, several tools offer rules.
  • Drop-down interface: It can be used for specifying data flows and rules.
  • Managing complex data: This will assist with complex calculations and string manipulations.
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Prioritizing Big Data Quality

In the era where data is everywhere or big data, getting the proper perspective on data quality may be difficult. With the vast majority of substantial data sources, you must assume that you are dealing with unclean data. One thing that makes social media data so beneficial to organizations is the sheer volume of apparently random and unrelated data. You begin by scanning petabytes of data, unsure of what you will discover once you start looking for patterns in the data. You must understand that there will be a lot of noise in the data. Only through searching and pattern matching will you be able to locate any glimmers of truth amongst some highly soiled facts. Of course, specific significant data sources, such as RFID tags or sensor data, have more well-defined regulations than social media data. Although you should anticipate occasional inaccuracies, sensor data should be pretty clean. When evaluating large volumes of data, you should always consider the data's quality.

You must follow a 2phase approach to ensure data quality:

  • Phase1: You must look for the big data pattern without concerning data quality.
  • Phase2: Apply the same data quality criteria to your new data sources as your old ones once you've found your patterns and established findings essential to the company. You don't want to acquire and manage extensive data that isn't relevant to your company and might pollute other data pieces in Hadoop or other big data platforms.

Recognize that high-quality data is required for a firm to make solid business choices when you begin to integrate your big data research into your business process. This is true for both extensive data and conventional data. Consistency, correctness, dependability, completeness, timeliness, rationality, and validity are aspects of data quality. To improve data consistency, data quality software ensures that data items are represented in the same manner across multiple data storage or systems.

In the data quality process, data profiling tools are used to assist you in understanding the content, structure, and condition of your data. They gather data on the qualities of data in a database or other data repository to start the process of transforming the data into a more trustworthy format. The tools examine the data for discrepancies and inaccuracies. They may make modifications and repair faults as a result of these issues. The tools assist in discovering overlapping data by checking for appropriate values, patterns, and ranges. For example, the data profiling process determines if the data is intended to be alpha or numeric. The tools also look for dependencies or analyze how data from other databases connect to the data in question.

Big data profiling tools work similarly to conventional data profiling techniques. Hadoop data profiling tools will provide you with crucial information about the data in Hadoop clusters. These methods may be used to hunt for matches and reduce duplications on massive data sets. Consequently, you'll be able to verify that your big data is accurate and consistent. Hadoop technologies like HiveQL and Pig Latin may be employed for the transformation process.

For example, one data store could utilize two lines for a customer's address, whereas another would only use one. Because of the differences in how data is displayed, erroneous information about consumers might arise, such as one client being misidentified as two separate customers. When a firm buys things, it may use hundreds of different permutations of its name.

Data quality software may be used to find all permutations of the company name in your various data warehouses, ensuring that you are aware of everything this client buys from you. This is known as delivering a single perspective of the client or the product. Data quality software compares data from several systems and cleans up or deletes superfluous information. The data quality process gives the company information that's simpler to utilize, analyze, and comprehend.

Using Hadoop as ETL

According to many enterprises employing big data platforms, ETL solutions are excessively sluggish and onerous to utilize enormous data. According to some, Hadoop may be used to manage portions of the transformation process and enhance the ETL and data-staging operations. Regardless of the data's original format, you may speed up the data integration process by putting unstructured and conventional operational and transactional data straight into Hadoop. After the data is imported into Hadoop, typical ETL tools may be used to integrate it further. The analytics process is sped up when Hadoop is employed as an assistant to the ETL process.

Hadoop's usage as an integration tool is still a work in progress. Traditional ETL vendors, including Informatica, IBM, Talend, Pentaho, and Datameer, now include Hadoop in their integration services. Developers may execute data quality and transformation activities that were previously impossible using Hadoop's capabilities as a massively parallel system. Hadoop, on the other hand, is not a stand-alone ETL solution.

Frequently Asked Questions

Write some of the importance of ETL testing?

It Ensures the efficient transfer of the data and its quality from one system to another, Ensuring that all the data is transferred and in bulk.

Mention some of the tools that can be sued in ETL testing.

Oracle Data Integrator, SAP Data Services, Hadoop, Talented Open studio, SAAP Data Manager, etc.

What do you understand by data warehouse?

Compiling and arranging data from diverse sources into a single database is required to provide significant business insights. Data is cleansed, merged, and aggregated in a data warehouse to assist management decision-making. A data warehouse may hold object-oriented, integrated, time-varying, and nonvolatile data.

What is Data Mining?

It includes looking for and detecting hidden, relevant, and possibly valuable patterns in massive data sets, also known as KDD (Knowledge Discovery in Database). The discovery of previously discovered links among data is a crucial objective of data mining. Data mining may extract insights that can be utilized for marketing, fraud detection, and scientific discoveries.


In this article, we have extensively discussed the ETL, i.e., Extract, Transform and Load. It's working with the individual working of all these three components, Its challenges and benefits, And ETL tools followed by Big Data Quality and using Hadoop as ETl with a reasonable explanation and examples in detail.

We hope that this blog has helped you enhance your knowledge of Data integration. If you are interested in learning more about Big data, you must refer to this blog. You will get a whole idea about big data, its types, characteristics, and more with proper explanation and suitable examples.

If you want to practice some SQL queries regarding big data, you must refer to this link. Here you will get the list of top 100 problems of SQL in big data that will help you a lot to practice and understand the topic with much clarity. If you would like to learn more, check out our articles on Code studio. Do upvote our blog to help other ninjas grow.

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