What Is ETL And How the ETL process works?

by | Nov 20, 2019 | Data Aggregation, Data Science | 0 comments

Every business or organization are in need of tons of data to carry out their important business decisions. To deal with the situation, the Data Warehousing technique is used for collecting and managing data from multiple sources and fed to provide business insights. Similarly, ETL comes into the picture. It is an integral part of Data Warehousing that helps to transfer data from one source to another.

What is ETL?

ETL refers to the process of transferring data from source to destination warehouse. It stands for Extract, Transform, and Load. Well, the full form itself speaks on the process that the ETL tools follow being a part of the Data Warehousing technique.

The data is foremost extracted from the sources available, and this data is then transformed into the desired format and then loaded to the Warehouse for further analysis.

The ETL process requires active inputs from various stakeholders, including developers, analysts, testers, top executives, and is technically challenging. It may seem too easy to talk about the Data warehousing process, but it isn’t. All of the above processes need extensive research and automation for the perfect result.

The 3-Step process

As we discussed, ETL is a three-step process to carry out the process of data integration from source to destination. The processes are Data Extraction, Data Transfer, and Data Loading.

etl Process

1. Extraction

In this first step, data is extracted from various sources and all the different formats and collected in the staging area. The staging area is the platform where the data is sorted out before sending it directly to the Data warehouse. Now, there are multiple instances where the data extracted may be corrupted and can ruin the entire structure of the Warehouse if not monitored.

There are multiple examples that you can think of under this process. For example, the marketing ETL process, where tons of data is collected or extracted using the process of marketing data integration for the purpose of creating campaigns. All this data is extracted from social media, CRM, ERP tools, etc.

By combining all the data, customers are provided with detailed insights, better analysis, and more enhanced results through these campaigns. Similarly, all these processes are carried out with the help of marketing reporting tools to provide a detailed report to the clients by letting them know where their money is being spent. 

Three ways of typical Data Extraction:

  1. Full Extraction: Well, there are several occasions when the system is unable to keep a record of the Data that has been changed over the years. In order to fix this issue, the entire data is reloaded to get it out from the system. Further, you can check and verify the data and keep the ones that are new.
  2. Update Modification: Many ETL tools or databases have a mechanism where the system notifies you when the record or the data has been changed. Such mechanisms support data replication and help to remove it in the next process further.
  3. Incremental Extraction: While there are some systems that are unable to provide such notifications on any updates or change in data, but surely they will help you to identify which are modified and accordingly data can be extracted.

Furthermore, there are issues of data rollback as well if not monitored correctly, resulting in several issues further. Thus this is deemed to be an important step to be carried out with precision. It can be carried out manually using codes and algorithms, but overall, it will take time and money. Therefore the ETL tools step into action to automate the process faster.

2. Transformation

The next step is followed by Data transformation. Well, this is the process where all the data collected and sorted out are then transformed since each of them is raw and cannot be fed to the Warehouse without converting them to their desired format. A specific set of rules or codes are followed to transform the data into a single readable format.

Some of the Process to carry out the Transformation of Data:

  1. Basic Transformation Process: This is the very basic Process of ETL that is carried out. Here the data is extracted, transformed, and then loaded in the Warehouse for further usage. Nothing new here, just the process system is aware of.
  2. In-warehouse Transformation: Another way to carry out this second step of Data Transformation is through the ELT process. Here data is extracted and kept in the staging area from where it loaded directly to the Warehouse. After this, the transformation of the data is carried out i.e., in the Warehouse.

Both these processes have their advantages and disadvantages. Many systems or organizations carry out the traditional method while some lookout for the ELT method for data Transformations.

There are several sub-processes involved in Data Transformation:

  1. Cleaning: Mapping NULL to 0 or “Male” to “M” and “Female” to “F,” date format consistency, etc.
  2. Filtering: Selective numbers of rows and columns are chosen.
  3. Deduplication: Identification and removal of any duplicate words.
  4. Derivation: Using your data to derive new value from existing data.
  5. Format Revision: Conversion of data/time format, character set, etc.
  6. Joining: Linking of data.
  7. Integration: Reconciling different data names and values for the same data element.
  8. Verification: Unused Data is removed.

All of the above processes prepare the data for further analysis. Data Transformation, in turn, makes Integration easier making it fully compatible with the next process.

3. Load

The last step is where you successfully load the data into its new destination i.e., Warehouse. This is a very crucial process. Indeed that needs proper precision. Well, the Data that needs to be loaded into the Warehouse are huge in number and need to be done at once. Thus, this process must be fully optimized at the earliest to carry out the operating smoothly. 

Two methods to carry out the Load process smoothly:

  1. Full Load: Here, entire data is loaded or dumped into the Warehouse. It surely takes loads of time and labor and may not be the best method to go for. But it depends on the usage organizations will put it to once loaded.
  2. Incremental Load: Well, here, the data is loaded in short increments or intervals. The batches of data are recorded and made sure no data is left in the stack, and the process is carried out smoothly. Further, this process, too, has subdivision depending on the increment type.
  • Batch Increment Load: For loading a large amount of data, you can load bog batched at once.
  • Streaming Increment Load: For loading small data volumes, a streaming increment load mechanism is used.

Challenges in the ETL Process

There are multiple challenges that you may face while trying to carry out the three-step process i.e., Extraction, Transformation, and Loading. This occurs due to improper planning before executing the process.

1. In Extraction

The most basic challenge that every system may face during the extraction of data is the availability of compatible drivers across diverse data sources. Not every ETL tool has the proper API connectors, which allow the process to interface with the source and extract the required data smoothly.

These API connectors have posed to be of a huge advantage such that the data transfer from one application to the other can be made easier without a specific set of codes.

Most of the commercially available ETL tool does not have the necessary plugin to extract data from sources such as e.g., HTML pages, textual data, etc. However, this can be carried out, which requires some complex programming and may incur more cost and time to the entire process.

2. In Transformation

Data Transformation itself is a complex process where data from all the sources are collected for transformation easily readable while loading in the Warehouse. The biggest challenge comes when the data received are of multiple data types not suitable for every ETL tool to be able to curate it according to the need.

Some data might come up, which is beyond the normal range that an ETL tool can optimize and also with Data Mapping. All this data cannot be merged together if there is no uniformity, and thus, the whole complexity increases if not assessed systematically and simultaneously.

3. In Loading

The only issue or challenge that occurs during the process of loading is that it takes time. Also, it totally depends on the volume of data that has been selected to be loaded into the Warehouse. While some may have only small volume or some may choose batches that will delay the other processes after that.

There are certain measures that need to be taken before carrying out the process so as to reduce the unnecessary time taken for loading of the data. The warehouses have been built upon a relational database environment, which means they are able to withhold huge chunks of data without slowing down the process. But the commercial ETL tools need to be designed properly in such a case.

How to Improve ETL performance?

So, we are aware of the fact that the ETL process, once planned properly, provides good results and carries out smoothly. Only the Load process is carried out in a much slower manner since it involves concurrency, maintenance before loading it in the Warehouse. So there are certain methods that you can follow to improve the performance overall.

1. Partition Tables

There are several large tables that need a thorough partition to smaller tables. It is a proven example, where you need to reduce the size of the tables to smaller ones. As you are aware, each table has its own indices, and the indices are too shallow, allowing the system to collect more data onto them for further implementation. Moreover, partitioning helps in carrying out the process of parallel bulk loading of the data to reduce the time taken.

2. Remove Unnecessary Data

It is surely necessary to extract and collect all the data that is available from multiple sources. But instead of sending them to the transformation phase to sort it out, you can do it in the extraction phase itself and remove the unwanted data which are not worthy of loading in the Warehouse. 

It all depends on the type of business you are running and what kind of data you are looking for. Once you have cleared it up, it will eventually speed up the process to a great extent. Furthermore, there are multiple times that the issue of data duplication occurs, and it can be prevented even before entering into the transformation phase. 

3. Data Caching

Caching the data is a great thought out method to improve the ETL process. This will increase system performance faster than before. It only depends on the type of hardware you are using the space it has.

Data caching is the need of the hour not only because it helps the system access the necessary data anytime when needed, but also it allows to access these data directly from the memory where it is stored in.

4. Incremental Loading of Data

As we have already discussed that the slowest process is the Load process since huge chunks of data need to be loaded in the Warehouse. Which is why incremental loading can serve a huge deal to speed up the process. Systems can easily divide the data into smaller sections and send them one by one. 

It can be either in small volume or larger volume, depending on the organizations and usage of the data. If the company needs a large chunk of data, then the loading needs to be done for large files and vice versa.

5. Using Parallel Bulk Load

Instead of carrying the loading process serially, the process can be made to happen parallelly. This method is only possible and works well when the tables have been partitioned into multiple smaller tables with indices.

Sometime it may not work if the machine is working on 100% CPU functions. This can be improved if the CPU is upgraded, which will eventually speed up the system.

6. IoT Data Integration

Using future technology to carry out the Process of IoT data integration will increase productivity at a rapid pace. With the use of the Internet of things in your business activities to sort out all the data you have collected in multiple ways, such as improving customer experience, predictive analysis, etc. For this, there are many data integration tools using IoT as their base technology to collect, sort, and transform the data for further use.

Best ETL Tools in the Market

In today’s business era, the word “data” holds a crucial place when it’s about making the right business decisions. No matter how small or large it is, every company wants the best ETL tools to carry their data integration processes. ETL stands for Extract, Transform, and Load and has made the daunting and sometimes tedious task of data analysis easier and convenient. By choosing the best ETL tools, you can extract data from multiple source systems, transform it into an easy-to-understand format, and load into a database or warehouse of your choice. 

Here, we are listing down the best ETL tools that will make the process of data management easier, along with improving data warehousing.

  • IBM (Infosphere Information Server): Infosphere Information Server is an end-to-end data integration platform that helps you understand your complex data and derive critical values for your business. The tool is mainly designed for large scale organizations that run on on-premise databases. With the help of various plug-ins, the tool supports SAP and automates business processes that help you save costs. It is a licensed tool that allows you to perform real-time data integration to make the right decisions at the right time. 
  • Oracle: The tool provides multiple data warehouse solutions for both on-cloud and on-premise systems. With this tool in your data integration processes, you can better optimize your customer experience and boost your business’s operational efficiency.
  • Blendo: It is a cloud-native ETL tool that allows you to perform the data integration process in real-time. The tool emphasizes on the extraction of data from different sources and loading the same into the destinations, skipping the transformation step. If you are looking for tools that can support SaaS offerings, without binding you with compliance requirements, then Blendo is the tool for you.
  • Amazon Redshift: One of the most cost-effective tools that help you analyze all your data within minutes. With this tool in your data integration processes, you can effortlessly run complex queries against big data silos.
  • Improvado: This tool will allow you to aggregate data from multiple marketing platforms and load it directly into your preferred data warehouse. It will help you to automate the task of manual reporting, thus saving your time and effort.
  • MarkLogic: With this tool at your end, you can make your data integration process easier and quicker. It offers a range of enterprise features that help you break the complex data silos like a pro. It queries different types of data, such as documents, relationships, and metadata.

Benefits of investing in the best ETL tools:

Using the right tools will help your business in the following ways:

  • Help you better analyze your crucial business data so that you can make the right decisions.
  • Break down the data silos and address concerns that matter to your business the most.
  • Effectively move your data from several source systems to a data warehouse.
  • Verifies data aggregation, transformation, and calculation rules.
  • Allow you to perform data comparison between the source and target systems.
  • Make it easier for you to perform complex transformations and securely store a large amount of data.
  • Transforms the extracted data into a single and lucid format.
  • Enhance the operation efficiency of your business by decoding the complex data.


ETL is surely an important process that needs to be carried out as frequently as possible. The reason being that data record is being changed quite frequently in the coming days, and thus, it needs to be fed to the company warehouse so that it can be put to the desired usage.

These tools can be used for a variety of applications such as Data Warehousing, Data mining, Data conversion, Data Integration, Data Grabber, and much more. Moreover, we have the data aggregation tools that provide in-depth analysis and reports of the data collected from multiple resources using the Process of Data Aggregation to analyze competitors, carry out campaigns, etc.

The need for ETL tools has increased a lot over the years since it has become difficult to maintain a large chunk of data, and it needs proper optimization before it can be put to use by other companies. It is no more a fact that data is the biggest asset currently in every company, and the need for ETL tools has thus become more than important.

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