List of Best ETL Tools in 2022

The ETL (Extract-Transform-Load) tools have become a necessity in the lives of developers. The basic feature of these tools is extracting, transforming, and loading the data from one data source to another. The reason why ETL tools are required is that many organizations have to process humongous amounts of data from different data sources on a daily basis. It allows the organizations to extract some insightful information from this data and make key data-driven decisions. These ETL tools simplify the development of the data pipelining process and help in managing and monitoring these data pipelines.

There are different ETL tools available in the market that the readers can choose from depending on their needs and comparing amongst these latest ETL tools. Check out the list of popular ETL tools below in the article.

Table of contents

  1. What Are ETL Tools?
  2. ETL vs ELT
  3. Different Types of ETL Tools
  4. List of Best ETL Tools in 2022
  5. Conclusion

What Are ETL Tools?

ETL tools are used for the automation and management of the ETL pipelines. These tools are used to extract the data from multiple data sources by connecting with the databases and storing the data with or without transformation in a data warehouse. Some of the ETL tools also provide testing of the data pipelines and reporting of the executed runs. They have become a more popular method than the traditional extraction methods that require user interference. The advantage of using these ETL applications is that they do not require any user intervention, sometimes even in case of failure. There are 3 ETL steps:

Step 1: Extract

In this step, we obtain the data from multiple data sources. The ETL tools connect to different databases and perform data extraction on a regular basis. ETL extraction can also mean extracting the files that are generated at a specific location. In such scenarios, a file is created, the data is written into it, and the ETL tool is used to extract the file from the location. We can extract both structured and unstructured data into the data warehouse.

Step 2: Transform

When the data is extracted, it usually comes from multiple data sources. This might result in little uniformity or it might require some data cleaning before loading it to the data warehouse. Hence, we need to transform the data before the data loading process starts. The ETL transformation will transform data to maintain uniformity within the data and then transfer it to the data warehouse. ETL transformation types include multiple methods like data cleaning, data deduplication, data joining/splitting, data summarization, etc.

Step 3: Load

This step involves loading the transformed data to a data warehouse. The data can either be loaded all at once which is commonly called as full load or at regular intervals i.e incremental load. After the data loading process is completed, the analysts can make use of this data to obtain insightful information from it. If there is a failure in the ETL data warehouse loading process, proper failure mechanisms must be in place to prevent any data loss.

ETL vs ELT

ETL and ELT are the two different methodologies for streamlining the data processes. Many organizations prefer to use a combination of both these methodologies depending upon the data it is dealing with. The workflow is similar for both methodologies but they vary in the architecture amongst many other things.

ETL (Extract-Transform-Load)

In ETL, the data is first transformed in a staging server, and then the transformed data is loaded into the data warehouse. ETL loads only the transformed data into the data warehouse. Hence, it requires thoughtful planning as raw data is not available.

ETL (Extract-Transform-Load) architecture

PROS:

  • It can reduce the storage space required as we are loading the transformed data. Hence the maintenance costs are reduced;
  • It works efficiently when transforming small amounts of data;
  • Privacy of the data can be handled with the help of transformations before storing it in the data warehouse.

CONS:

  • It reduces the information at hand as the raw data is transformed;
  • For any changes required in the data storing, the transformations need to be changed in advance;
  • If the data size continues to increase, the transformation time will also increase.

ELT (Extract-Load-Transform)

The data is directly loaded into the data warehouse, and some basic transformations are applied in the data warehouse servers. In ELT, the raw data is dumped into the data warehouse, which can help in experimenting with different strategies.

ELT (Extract-Load-Transform) architecture

PROS:

  • More information is at hand as we dump the raw data directly into the data warehouse;
  • Loading time is faster because the data is transformed at later stages;
  • Automation becomes easier as transformations are not involved in the intermediate step.

CONS:

  • If the data contains personal information, there is a risk of a data privacy breach;
  • The storage space required will be higher, which will result in higher maintenance costs.

Different Types of ETL Tools

There are variants of ETL tools available in the market. Some organizations use ETL tools in big data analysis writing SQL scripts for the same. These ETL tools can also be used for business intelligence. ETL tools can be categorized based on their usage and cost. Among different types of ETL tools are the following:

Cloud-based ETL tools

Cloud-based ETL tools are provided over the internet and are usually accessed through the web browser. Many cloud services provide ETL capabilities as a service along with storing their data on a cloud platform. These tools can be used if the data can be exposed over the internet as they are cloud-based.

Open-source ETL tools

Open-source ETL tools are free-to-use software that can be used either over the internet or within the network. These ETL tools are generally based upon the python framework. The python based ETL tools have become a popular choice in the open-sourced segment.

Enterprise software tools

Enterprise ETL tools are application-based commercial tools that are provided as a product by several companies. These tools require licenses to be procured as they are commercial and can be used internally within the network as well as over the internet.

Reverse-ETL tools

Reverse-ETL refers to the process of syncing/copying the data from the data warehouse to the other operational tools that are being used by the business teams. In other words, it is the opposite of the ETL or ELT process where the data flows into the data warehouse. Such integrations are very useful when the data or insights need to be shared among different departments of an organization. This helps the audience in making key decisions about their services or products across the organization.

List of Best ETL Tools in 2022

Skyvia

Skyvia ETL tool logo

Skyvia is a universal SaaS (Software as a Service) data platform, which offers code-free solutions like data integration, data management and cloud backup. Skyvia supports a wide number of cloud applications, databases, file storage services and cloud data warehouses. Users can work with data of different cloud apps with different API in a uniform way as with relational data. The Data Integration product of Skyvia combines ETL, ELT as well as reverse ETL functionality.

Skyvia is an entirely cloud-based solution, and the Skyvia ETL tool can be considered one of the most popular among users. To use the platform, you need only a web browser. No locally installed software is required.

PROS:

  • 80+ connectors for integration and growing;
  • Trials and free plans are available;
  • Simple user interface and monitoring of the package runs;
  • Ability to perform complex data transformations with ease;
  • Scheduled package runs.
  • Failure alerts and detailed logs;
  • Almost code-free querying of data sources;

CONS:

  • There are limits of free usage;
  • Streaming data sources are not supported at the moment.

Pentaho

Hitachi logo

Pentaho is a business intelligence tool that provides data integration, reporting, dashboards, etc. It is provided as an open-source as well as commercial ETL tool. The open-sourced i.e. community version provides limited capabilities whereas richer features are available in the licensed version. It runs as an application that organizations can use for their on-premise requirements.

PROS:

  • Pentaho has a good community support as it is one of the most popular tools;
  • The licensed version can connect with most of the necessary data sources;
  • It provides a simple user interface for beginners to start their journey;

CONS:

  • The open-source version requires a few more capabilities to get a good hands-on with the product;
  • Troubleshooting for errors can be troublesome at times;
  • It does not provide support for creating custom connectors.

Oracle Data Integrator

Oracle ETL tool logo

Oracle Data Integrator is a product provided by Oracle for data integration and other ETL purposes. It is available as a cloud-based ETL tool as well as an enterprise ETL tool. It can connect with various data sources and is designed for data sizes of different volumes. It also provides ELT workloads for the scenarios where the data can be transformed after it has been loaded into the data warehouse.

PROS:

  • It can be easily integrated with other Oracle applications;
  • It supports huge data loading capabilities;
  • Easy to scale for large workloads in terms of data and users.

CONS:

  • The pricing model is higher compared to its peers;
  • The user interface has fewer features compared to its peers;
  • Real-time data integration requires more stability.

Talend Open Studio

Talend logo

Talend Open Studio is one of the many versions of ETL tools provided by Talend. Talend Open Studio is an open-sourced version of it. Talend also provides commercial products like Talend Data Fabric for organization-wide use that provides advanced features like maintaining the integrity of data and its governance. It is a Java-based application and can be accessed through Eclipse IDE.

PROS:

  • The variants of licensed products allow users to choose the one according to their usability;
  • It provides drag and drop components to easily connect them and run ETL pipelines;
  • Easy to use for developers familiar with Eclipse IDE.

CONS:

  • Specific skill sets will be required for complex ETL pipelines development;
  • Integration with some data sources can be difficult and require assistance;
  • The user interface is not friendly for a non-technical audience.

Informatica PowerCenter

Informatica PowerCenter ETL tool logo

Informatica PowerCenter is a data integration tool that is used for streamlining the data pipelining processes. It connects with different data sources and processes the data. It can also be used for data governance and to maintain the security of the data by providing role-based access. It provides a user-friendly GUI, making it easy for the users to use and maintain the organizational data. It also can be accessed on the cloud, using Informatica Cloud Services.

PROS:

  • Simple user interface and monitoring of the jobs;
  • Powerful ETL tools with the ability to perform complex transformations with ease;
  • It supports parallelism and easy scheduling of the jobs.

CONS:

  • It might consume a high amount of computational resources based on the volume;
  • PowerCenter can cost more than other existing products;
  • It does not provide an open-sourced version even with limited functionalities.

Fivetran

Fivetran is an ETL tool that has been providing ETL data integration services since 2012. It is a licensed tool that allows enterprise cloud-based solutions. It is one of the most widely used ETL tools in the market. It also provides different solutions based upon the needs of the users such as enterprise, data integration, and data replication.

PROS:

  • It provides over 150+ connectors for integration with different data sources;
  • The pricing model is based upon the active row i.e. if a row is updated any number of times, it is still considered as a single row;
  • It supports SQL-based transformation after the data loading is completed.

CONS:

  • It does not provide an open-sourced version of its software;
  • It does not allow the transformation of the data before sending it to the warehouse;
  • It does not offer an on-premise solution.

Stitch

Stitch logo

Stitch is another cloud-based ETL platform that can be used to integrate with different data sources. It offers fully managed data pipelining processes to integrate data to the data warehouse. It was acquired by Talend in 2018. After that it continues to operate as an independent unit.

PROS:

  • It can be easily integrated with Singer, which is an open-sourced tool from Stitch;
  • It offers a volume-based pricing model that will allow users to choose according to their usage;
  • It supports a high number of data sources that are either built in-house or community-supported.

CONS:

  • It does not offer real-time data synchronization;
  • The tool is more developer-focused that might not be useful for the non-developer audience;
  • It offers a limited number of destinations for loading the data.

Airbyte

Airbyte logo

Airbyte is an ELT tool that executes automated ELT pipelines along with monitoring their logs. Currently, it provides an open-source version and a cloud version with an enterprise version coming in the future for organizations that need an on-premise solution. It provides ELT capabilities where the data is fetched, loaded, and then transformed according to the use cases.

PROS:

  • Capability to connect with a huge number of data connectors;
  • Create custom connectors easily using their CDK;
  • Easy and error-free deployment through Docker.

CONS:

  • The cloud version is currently only available in the US;
  • An on-premise solution is not available;
  • Some connectors are community-based and thus are not thoroughly tested.

Singer

Singer logo

Singer is a python-based open-source tool that allows data extraction from different data sources and consolidation to multiple destinations. It contains two main components i.e taps and targets. Taps are nothing but data extraction scripts that allow us to fetch the data from different sources. Targets are the data loading scripts that load the contents to a file or a database.

PROS:

  • Quick and easy setup process;
  • All the taps and targets are independent, which allows us to set up only the required tools;
  • Easy to develop and modify taps and targets according to the requirements.

CONS:

  • A limited number of data loading targets;
  • It lacks transformational capabilities;
  • Not useful for a non-technical audience.

Xplenty (Integrate.io)

Integrate.io (Xplenty) logo

Xplenty (Integrate.io) is a cloud-based ETL tool for integrating with different databases. It provides a code-free environment that allows the organizations to scale up easily. It allows the organizations to integrate their ETL pipelines, process and prepare the data for analytical purposes over the cloud.

PROS:

  • It integrates with a huge number of data sources;
  • A code-free platform that allows beginners to work on it easily;
  • It allows creating dependencies between different ETL pipelines.

CONS:

  • It cannot synchronize the data in real-time;
  • It does not support on-premise solutions;
  • It does not support data replication use cases.

Conclusion

The ETL tools are a perfect way for organizations to streamline and maintain the data pipelining process, data governance and to monitor these processes daily. The decision on choosing the right ETL tool for you depends on multiple factors like use cases of the organizations, connection to the data sources, skill sets for using the application, ability to provide role-based access and data governance, budget, etc. The open-source ETL tools are free but certain expertise is required for the development and maintenance of the workflows. In the segment of cloud-based ETL tools, Skyvia ticks all the boxes for essential features required in organizations for their data integration purposes.

Prathamesh Thakar
Prathamesh Thakar
Associate Data Science
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