Home » 20 Best ETL Tools For Data Management In 2022 – Techlion

20 Best ETL Tools For Data Management In 2022 – Techlion

by Julia
ETL tools

ETL tools are programmes that aid in the extraction of data from various sources; the scrubbing of data for consistency and quality; and the consolidation of this information into data warehouses. When used correctly, ETL technologies make it easier to manage data and improve the quality of data by providing a uniform way to enter, exchange, and store data.

This video gives an excellent introduction to ETL tools and methodologies.

Data-driven companies and platforms benefit from ETL technologies. The main advantage of customer-relationship management (CRM) platforms, for example, is that all business operations are carried out through the same interface. This makes it easy for teams to share CRM data, giving a more complete picture of how a business is doing and how far it has come toward its goals.

Let us now look into the four types of ETL tools that are accessible.

Categories of ETL Tools

ETL tools are classified into four groups based on their infrastructure and supporting organisation or provider. These categories are specified below: enterprise-grade, open-source, cloud-based, and bespoke ETL tools.

1. Enterprise Software ETL Tools

Business software Commercial organisations provide and support ETL tools. Because these firms were the first to push ETL technologies, their solutions tend to be the most mature in the industry. This involves providing graphical user interfaces (GUIs) for designing ETL pipelines, supporting the majority of relational and non-relational databases, as well as substantial documentation and user groups.

Because they are more complicated, enterprise software ETL systems usually cost more and need more training and integration services to set up.

2. Free ETL Tools are Available

It’s no wonder that open-source ETL solutions have entered the market with the development of the open-source movement. Many ETL solutions are now free and include graphical user interfaces for developing data-sharing procedures and monitoring information flow. One of the best things about open-source solutions is that businesses can look at the tool’s architecture and add to its capabilities.

But because open-source ETL systems aren’t often backed by businesses, they might be different in terms of upkeep, documentation, ease of use, and capabilities.

3. Cloud-based ETL Tools

As a result of the broad use of cloud and integration-platform-as-a-service technology, cloud service providers (CSPs) increasingly provide ETL tools based on their infrastructure as a result.

The effectiveness of cloud-based ETL solutions is a distinct benefit. Cloud technology delivers excellent latency, availability, and flexibility, allowing computer resources to grow to match current data processing demands. If the company also uses the same CSP to store its data, the pipeline becomes even more efficient because all of the steps happen in the same infrastructure.

One disadvantage of cloud-based ETL tools is that they only function within the context of the CSP. They don’t support data stored in other clouds or on-premise data centres unless it has been moved to the provider’s cloud storage first.

4. Tools for Custom ETL

Companies with extensive development resources may create proprietary ETL tools in mainstream programming languages. The ability to develop a solution tailored to the organization’s needs and workflows is a fundamental advantage of this approach. SQL, Python, and Java are popular languages for developing ETL systems.

The most significant disadvantage of this technique is the number of internal resources necessary to develop a bespoke ETL tool, including testing, maintenance, and upgrades. Another factor to consider is the training and documentation requirements to onboard new users and developers, who will all be unfamiliar with the platform.

Now that you know what ETL tools are and what types of tools are available, let’s look at how to assess these solutions to find the best match for your organization’s data processes and use cases.

How to Assess ETL Tools

Every organisation has a distinct business model and culture, which is reflected in the data that a firm gathers and cherishes. However, there are common criteria against which ETL systems may be measured that will be applicable to any organisation, as mentioned below.

  • Use case: A crucial factor for ETL solutions is the use case. If your business is small or your data analysis needs are simple, you might not need as strong a solution as a big company with a complicated dataset.
  • Budget: Another key consideration while selecting ETL software is the cost. Although open-source technologies are often free to use, they may lack the functionality and support of enterprise-grade products. If the product needs a lot of coding, you should also think about how much it will cost to hire and keep developers.
  • Capabilities: The greatest ETL solutions may be tailored to match the data requirements of various teams and business processes. One way ETL systems may ensure data quality and decrease the effort necessary to examine datasets is through automated features such as de-duplication. Furthermore, data connectors simplify platform sharing.
  • Sources of data: ETL systems should be able to meet data “where it exists,” whether on-premises or in the cloud. Organizations may also contain complicated data structures as well as unstructured data of various types. A perfect solution would be able to take data from any source and store it in standardised formats.
  • The data and code fluency of developers and end users is an important factor. For example, if the tool necessitates manual coding, the development team should preferably be able to utilise the languages on which it is based. If the user does not understand how to write complicated queries, a tool that automates this process is excellent.

20 Best ETL Tools For Data Management In 2022

Next, we’ll look at specific tools you can use to power your ETL pipelines and put them into one of the above categories.

Oracle Data Integrator (ODI) is a platform that allows enterprises to create, manage, and maintain data integration workflows. ODI is capable of handling a wide range of data integration demands, from high-volume batch loads to service-oriented architecture data services. It also lets you run tasks in parallel, which speeds up data processing, and comes set up with connectors for Oracle GoldenGate and Oracle Warehouse Builder.

Oracle Enterprise Manager can be used to keep an eye on ODI and other Oracle technologies to get a better view of the whole toolset.

#2. Fivetran

Fivetran

Fivetran’s platform of useful solutions strives to make your data management process easier. The simple programme keeps up with API upgrades and fetches the most recent data from your database in seconds.

Fivetran provides data security services, database replication, and 24/7 support in addition to ETL tools. Fivetran takes pride in its near-perfect uptime, allowing you to contact its staff of engineers at any time.

#3. incorporate.io

incorporate.io

Integrate.io is a leading low-code data integration platform with a broad offering (ETL, ELT, API Generation, Observability, Data Warehouse Insights) and hundreds of connectors for quickly building and managing automated, secure pipelines. Get access to data that is regularly refreshed to assist in giving meaningful, data-backed insights to decrease your CAC, boost your ROAS, and drive go-to-market success.

With any data volume or use case, the platform is extremely scalable, allowing you to simply aggregate data into warehouses, databases, data storage, and operational systems.

#4. IBM DataStage

IBM DataStage

IBM DataStage is a data integration solution with a client-server architecture. Tasks are generated and executed from a Windows client against a central data repository on a server. The tool is meant to handle ETL and extract, load, and transform (ELT) models, as well as high-performance data integrations from many different sources and applications.

IBM DataStage is designed for on-premise implementation, but there is also a cloud-enabled version available: DataStage for IBM Cloud Pak for Data.

#5. Talend Open Studio

Talend Open Studio

Talend Open Studio is an open-source tool for quickly building data pipelines. Data components from Excel, Dropbox, Oracle, Salesforce, Microsoft Dynamics, and other data sources may be linked to run tasks using Open Studio’s drag-and-drop GUI. Talend Open Studio has connectors that let you get data from relational database management systems, software-as-a-service platforms, and packaged applications, among other places.

#6. SAS Data Management

 SAS Data Management

SAS Data Management is a data integration platform designed to connect to data in any location, including the cloud, legacy systems, and data lakes. These linkages give a comprehensive perspective of the business operations of the firm. The technology makes processes better by reusing data management rules and letting people who aren’t in IT take data from the platform and evaluate it.

SAS Data Management is very flexible and can work with a wide range of computer systems and databases. It can also connect to third-party data modelling tools to make results that are interesting to look at.

#7. Singer.io

Singer.io

Singer is an open-source scripting technology designed to improve data flow between applications and storage in an enterprise. Singer talks about how the link between data extraction and data loading programmes makes it possible to get data from anywhere and put it anywhere else.  The scripts use JSON so that they can be written in any programming language. They also use JSON Schema to provide rich data types and enforce data structures.

#8. Pentaho Data Integration

Pentaho Data Integration

Pentaho Data Integration (PDI) automates data integration activities such as data capture, purification, and storage in a uniform and defined manner. This information is also given to end users so they can analyse it, and IoT devices are able to access data to improve machine learning.

PDI also offers the Spoon desktop client, which can be used to create transformations, schedule jobs, and start processing processes by hand when needed.

#9. Dataddo

 Dataddo

Dataddo is a no-code, cloud-based ETL tool that enables both technical and non-technical people to integrate data in a flexible manner. It has a wide range of connections, completely customizable metrics, a centralised system for managing all data pipelines at once, and it can be easily added to the current architecture of technology.

Users may deploy pipelines within minutes of creating an account, and any API updates are managed by the Dataddo team, so pipelines don’t need to be maintained. On request, new connections can be added within 10 business days. GDPR, SOC2, and ISO 27001 compliance are all supported by the platform.

#10. Hadoop

Hadoop

The Apache Hadoop software library is a system designed to assist massive data set processing by dividing the computational burden among computer clusters. The library is intended to identify and manage errors at the application layer rather than at the hardware layer, ensuring high availability while pooling the processing power of numerous computers. With the Hadoop YARN module, the framework also lets you schedule tasks and manage cluster resources.

#11. Azure Data Factory

Azure Data Factory

Azure Data Factory is a pay-as-you-go serverless data integration solution that grows to match computational demands. The service supports both no-code and code-based interfaces and can get data from over 90 built-in connections. Also, Azure Data Factory and Azure Synapse Analytics work together to make data analysis and visualisation better.

For DevOps teams, the platform also supports Git for version control and continuous integration and continuous deployment workflows.

#12. AWS Glue

AWS Glue

AWS Glue is a cloud-based data integration tool for technical and non-technical business users that supports visual and code-based clients. On the serverless platform, you can find services like AWS Glue Data Catalog, which helps you find data across your company, and AWS Glue Studio, which lets you build, run, and maintain ETL pipelines graphically. Custom SQL queries are now supported by AWS Glue for additional hands-on data interactions.

#13. Stitch

Stitch

Stitch is a data integration solution that can pull data from over 130 platforms, services, and applications. The technology centralises this data in a data warehouse without the need for human coding. Stitch is open-source, which allows development teams to expand the tool’s support for new sources and functionality. Furthermore, Stitch focuses on compliance, giving users the ability to evaluate and regulate data in order to fulfil internal and external regulations.

#14. Google Cloud Dataflow

Google Cloud Dataflow

Google Cloud Dataflow is a fully managed data processing service that was designed to maximise computing resources and automate resource management. The service is designed to reduce processing costs by utilising flexible scheduling and dynamic resource scaling to guarantee that consumption meets requirements. Also, when the data is converted, Google Cloud Dataflow uses AI to help with predictive analysis and finding outliers in real time.

#15. Skyvia

Skyvia

 

Skyvia offers a fully configurable data sync. You may extract everything you want, including custom fields and objects. Skyvia also keeps you from having to change the way your data is organised because its primary keys are made automatically. Skyvia customers can also import data into cloud apps and databases, copy cloud data, and export data to CSV to share.

#16. Informatica PowerCenter

Informatica PowerCenter

Informatica PowerCenter is a metadata-driven platform aimed at increasing business-IT cooperation and optimising data pipelines. PowerCenter parses sophisticated data formats such as JSON, XML, PDF, and machine data from the Internet of Things and automatically verifies converted data to enforce established standards.

The platform also has pre-built transformations to make things easier, as well as high availability and optimal performance to meet the needs of computing.

#17. Dextrus

Dextrus

Dextrus supports self-service data entry, streaming, transformations, cleaning, preparation, wrangling, reporting, and modelling for machine learning.

Features:

  • Make batch and real-time streaming data pipelines in minutes, then use the built-in approval and version control system to automate and run them.
  • Create and manage a simple cloud data lake for reporting and analytics on cold and warm data.
  • Using visualisations and dashboards, you can analyse and acquire insights into your data.
  • Prepare datasets for sophisticated analytics by wrangling them.
  • Make and use machine learning models for exploratory data analysis (EDA) and prediction.

#18. Ab Initio

Ab Initio

Ab Initio is a Massachusetts-based private enterprise software company that was founded in 1995. It has offices in the United Kingdom, Japan, France, Poland, Germany, Singapore, and Australia, among other places. Ab Initio is an application integration and large volume data processing company.

The Component Library, the Operating System, the Graphical Development Environment, the Enterprise Meta>Environment, the Data Profiler, and the Conduct>It is among the six data processing products included. “Ab Initio CO-Operating System” is a drag-and-drop GUI-based ETL tool.

#19. Apache Nifi

Apache Nifi

The Apache Software Foundation created the Apache Nifi software project. The Apache Software Foundation (ASF) was founded in 1999, with its headquarters in Maryland, the United States. ASF’s software is Free and Open Source Software distributed under the Apache License.

Using automation, Apache Nifi facilitates data flow across diverse platforms. The data flows are made up of processors, and the user can design their own. These flows may be kept as templates and then combined with more sophisticated flows in the future. These sophisticated processes may then be easily distributed to many servers.

#20. Sybase ETL

Sybase ETL

Sybase is a market leader in data integration. The Sybase ETL tool is designed to take data from various data sources, transform it into data sets, and then put it into the data warehouse.

Sybase ETL includes sub-components of Sybase ETL Server and Sybase ETL Development.

Data pipelines can be powered by ETL technologies.

ETL is a fundamental method in which businesses construct data pipelines to provide their leaders and stakeholders with the information they require to function more effectively and make better choices. By using ETL solutions to power this process, teams can get to new levels of speed and consistency, no matter how complex or varied their data is.

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