12 Data Transformation Tools (With Examples And FAQs)

By Indeed Editorial Team

Published 26 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data transformation is a data processing technique to change the data format into a form that is usable and easy to understand. Data transformation tools make it easy for businesses and organisations to transform data to meet important objectives. Learning about various tools available for data transformation can help you find the best suitable tool according to your requirements. In this article, we define data transformation tools, discuss 12 different tools, understand the steps of the data transformation process and explore some related FAQs.

What are data transformation tools?

Data transformation tools enable you to transform or modify the format or structure of your data. Data transformation becomes necessary when gathering data from various sources, integrating unstructured and structured data or adding more data to the existing data. Selecting the correct tools is crucial, as any fault in this part can cause information loss, incompatibility issues and losing substantial insights.

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12 tools for data transformation

Here is the list of some common tools for data transformation:

1. Hevo

Hevo is a fully automated data pipeline platform that enables the transformation of data and recursive update of transformation methods using Python code. It allows you to connect your data collection to over 100 different sources covering multiple platforms, file types and database structures. Businesses use this platform to automate their data engineering efforts and ensure their teams have access to analytics-ready data in nearly real-time.

2. Matillion

Matillion allows data transformation through SQL functions or its intuitive point-and-click interface. It is a data integration platform for Azure Synapse, Redshift, BigQuery and Snowflake that seamlessly consolidates large data sets for on-demand transformations. With over 70 pre-built connectors, this tool automates extract, transform, load (ETL) processes and makes them simple. It seamlessly integrates with raw data sources and business intelligence and analytics tools. Matillion also provides the flexibility to transform data as required by business intelligence tools.

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3. Qlik Compose

Qlik Compose is a flexible data transformation and ETL automation tool that automates ETL and replication end-to-end, saving data administrators' time and reducing errors due to manual coding. It is a tool with core operational features that allow administrators to effortlessly ingest, synchronise, distribute and merge data. It is a cloud-based analytics platform that integrates real-time data and also offers executive dashboards and business intelligence products. Qlik Compose also helps reduce gaps between data, insights and actions.

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4. Data Building Tool (dbt)

dbt is a free, open-source, command-line solution for quickly transforming data with SQL coding. This tool allows you to create reliable data sets for use in machine learning (ML) modelling, operational workflows and reporting. It also enables you to work with raw data and develop, test, deploy and build applications from a centralised location.

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5. IBM InfoSphere DataStage

IBM InfoSphere DataStage is a robust data transformation platform for gathering, integrating and transforming large data with easy to complex data structures. It supports big data and Hadoop and authorises direct access to large amounts of data stored on a distributed file system. It also enables clients to use new data sources more efficiently through JavaScript Object Notation (JSON) support and a new Java Database Connectivity (JDBC) connector.

IBM InfoSphere DataStage incorporates data from multiple systems using a high-performance parallel framework. It also supports advanced metadata management and big data enterprise connectivity. The platform's scalability enables easy integration of all data types.

6. Rivery

Rivery is a software as a service (SaaS) platform for data extraction, transformation and loading. Its pre-built starter kits simplify starting with the software, guiding you through the data transformation process with pre-made templates. The customisation feature enables you to adjust procedures to the business's specific requirements, maximising data processing efficiency. It integrates with numerous widely used professional services, varying from advertising and promotion to email communications.

7. Dataform

Dataform is a free and open-source tool that enables data analysts to operate all processes within cloud data warehouses such as Panoply, Snowflake, Redshift and BigQuery. It includes all the tools necessary to create workflows that transform unstructured data into usable datasets suitable for analysis. You can use SQLX to quickly build robust data transformation infrastructure from the command line. Each SQLX model is similar to a SELECT statement. As a result, data engineers can use the ref function to quickly create dependencies between tables, allowing them to focus on analytics rather than data infrastructure management.

8. Apache Nifi

Apache Nifi is a data transformation and ETL tool that is free and open-source based on the flow-based programming concept. It allows you to send and receive data, transfer it between different storage options and filter data during transfers. It has a completely customisable graphical user interface and facilitates the creation of robust and scalable directed graphs for data routing, transformation and system mediation logic.

9. IRI Voracity

IRI Voracity is a full-stack ETL and data management platform that supports various data and file types, ensuring to meet your data transformation requirements. With IRI Voracity, you can manage your ETL operations in real-time or batch mode. The tool includes wizards that guide you through the software's various functions along with data cleaning, metadata management and data mapping features.

10. Talend Open Studio

Talend Open Studio is a free data integration tool. It has a drag-and-drop feature that makes it easy to manage your extractions, transformations and loading requirements. You can also use it as a cloud service or on a local device. It is customisable, with over 900 pre-made components that let you connect it to various data sources to speed up your ETL processes.

11. Informatica PowerCenter

PowerCenter data automation software is part of the Informatica data integration software suite. Big businesses that conduct extensive data mining and cleanup widely use this tool. Its automatic error logging and advanced warning systems are some features that enable you to keep your ETL mechanisms properly running while improving accuracy and speed.

12. Dataddo

Dataddo is a cloud-based, no-code data integration ELT platform that integrates, automates and transforms data from almost any online data service. It supports data integration from over 100 different sources, allowing you to set up integrations quickly and start harvesting relevant data. It can transform and load data into various databases, data warehouses, cloud storage and business intelligence (BI) applications, ensuring seamless interoperability with existing IT and BI stacks. Its dashboard view gives you access to transformation structures, allowing you to add or modify new pipelines to convert data from its original format to the access platform of your choice.

Steps involved in the data transformation process

ETL is an acronym for Extract, Load and Transform and it refers to the entire data transformation process. Analysts can convert data to the desired format using the ETL process. Here are the steps involved in the data transformation process:

  1. Data discovery: The first stage involves analysts attempting to comprehend and identify data in its original format and they accomplish this through the use of data analysis tools. This step assists analysts in determining the steps necessary to convert data to the desired format.

  2. Data mapping: Analysts perform data mapping during this phase to identify how to modify, map, filter, join and aggregate individual fields. Numerous data processes require data mapping and a single error can result in inaccurate analysis.

  3. Extraction: This phase involves the extraction of data from its actual source. These sources may be structured, like databases or streaming, such as customer log files from web apps.

  4. Execution: Analysts write code to complete the transformation after the data extraction. They often use ETL tools to create codes.

  5. Review: After data transformation, analysts check the data to ensure the correct formatting of everything.

  6. Sending: The last step is to send the data to its intended location. The destination can be a data warehouse or a database that manages data.

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FAQs related to tools for data transformation

Here are the answers to some frequently asked questions about data transformation:

Why is data transformation important?

Data transformation makes data more organised. Transformed data is easy to use for both humans and computers. Proper data formatting and validation enhance data quality and protect applications from possible inconsistencies like null values, random duplicates, incorrect indexing and inconsistent formats.

What are the types of data transformation?

The four types of data transformation are constructive, destructive, aesthetic and structural. Constructive data transformation involves adding, copying and replicating data. Destructive data transformation comprises the deletion of fields and records. Aesthetic data transformation includes the standardisation of data to match requirements. Structural data transformation involves renaming, moving and combining columns in a database.

Please note that none of the companies, institutions or organisations mentioned in this article is associated with Indeed.

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