Data transformation tools extract data from various sources and formats, process and refine that data to load it into data warehouses or other tools in the data tech stack. These tools help data teams maintain and update their databases with relevant, useful data crucial for analytics and BI.
This article explores the ten most popular data transformation tools in 2022. These tools have been featured in popular review portals like Gartner.
Here are the ten most popular transformation tools in 2022:
- AWS Glue
- Azure Data Transformation
- Cloud Data Integration for Cloud ETL and ELT by Informatica
- Denodo Platform
- Hevo Data
AWS Glue is a data transformation tool that helps find, process, and merge data for further analytics.
Since it’s part of the AWS suite of services, it integrates well with other AWS offerings, such as Amazon S3, Amazon RDS, Amazon Redshift, and Amazon Athena.
Moreover, it is serverless and you don’t have to worry about your infrastructure overhead, so you only pay for the resources you consume.
What are some of the main data transformation capabilities of AWS Glue?
- AWS Glue lets you merge data across multiple data stores and oversee thousands of ETL processes or workflows at once with a unified catalog.
- You can use AWS Glue Studio to set up and manage various ETL jobs visually, with a drag-and-drop editor.
- When you use AWS Glue for data from various sources, it automatically identifies the data format and suggests appropriate schemas.
AWS Glue resources
Azure Data Factory
Azure Data Factory helps you cleanse, merge, and format enterprise data at scale.
Data Factory is a serverless tool that lets you visually integrate data sources using 90+ built-in connectors. So, while you can write your own code, you can also choose to design ETL or ELT pipelines and let Data Factory generate the code automatically.
Azure Data Factory has been certified by top compliance bodies such as HIPAA and HITECH, ISO/IEC 27001, and CSA STAR.
What are some of the main data transformation capabilities of Azure Data Factory?
- Data Factory offers built-in Git and CI/CD support.
- It uses a pay-as-you-go model, making it easier for you to scale up or down as per your requirements. There are no upfront costs involved.
- The code-free environment enables citizen integrators to drive analytics and BI without engineering or IT support.
- Data Factory provides a data transformation layer that works across your digital transformation initiatives. You can transform faster and more intelligently with intent-driven mapping. It will automate copy activities.
Azure Data Transformation resources
Cloud Data Integration for Cloud ETL and ELT by Informatica
Informatica offers high-quality, cost-effective solutions for your data transformation needs.
With Informatica’s cloud data integration suite, you can perform mass data ingestion across sources, integrate apps data in real-time, and build pipelines without worrying about the overall infrastructure.
The solution can handle ETL and ELT for multi-cloud environment and comes with a free 30-day trial.
What are some of the main data transformation capabilities of Cloud Data Integration for Cloud ETL and ELT?
- You can integrate data with a wide range of processes — ETL, ELT, Spark, or a serverless option.
- The pricing depends on your consumption, making it easier for you to scale your cloud up or down as per the demand.
- The solution integrates with the various tools in your data stack, regardless of whether they’re on-premise or SaaS.
- You don’t need advanced coding knowledge to use the solution as it’s equipped with 100+ pre-built templates for setting up data pipelines.
dbt helps you transform, test, and document data from various sources such as cloud warehouses, data lakes, and lake houses. In addition, it offers support adapters for each technology — Postgres, Redshift, Bigquery, SQL Server, and more.
dbt only does the T in ELT, so it doesn’t extract or load data, just transforms the data already loaded into your warehouse.
dbt offers version control, testing, logging, and alerting to simplify governance. It is compliant with SOC2 Type II, ISO 27001:2013, ISO 27701:2019, GDPR, PCI, and HIPAA.
What are some of the main data transformation capabilities of dbt?
- You can write SQL SELECT statements to transform the data in your warehouse.
- dbt auto-generates dependency graphs and dynamic data dictionaries.
- dbt logs and assets are regarding the transformations you run. They don’t store or reveal any information about the actual data from the warehouse. So, you have complete control and ownership of your data.
Denodo Platform is a data transformation tool built for the logical data fabric, which includes an active data catalog, smart querying, automated cloud infrastructure management, and more.
The platform lets you connect disparate data from various sources, set up transformations as per your use cases, and prepare data in real-time.
It also offers custom training programs for each role within your data teams, such as architects, developers, admins, and business users.
What are some of the main data transformation capabilities of the Denodo Platform?
- Denodo Platform covers the needs of everyone (from business to IT stakeholders) and has an easy-to-use, web-based user interface.
- It supports OAuth 2.0, SAML, OpenAPI, OData 4, GraphQL, and other cloud standards for interoperability in multi-cloud environments.
- The platform automates infrastructure management and offers PaaS support.
Denodo Platform resources
Domo lets you integrate data from various sources with 1000+ pre-built cloud connectors to cut engineering costs and save time. It also offers on-premise connectors and connections to proprietary systems.
After integrating data, you can transform and query it, set up pipelines, use the data to drive data science, analytics, and BI. Domo is compliant with GDPR, HIPAA, SOC 1/2, and ISO standards.
What are some of the main data transformation capabilities of Domo?
- Domo offers magic ETL — visually define and sequence operations with a simple drag-and-drop interface.
- You can use MySQL or Redshift expressions to build your data pipelines.
- Domo supports SSO, multi-factor authentication, and provides you with complete activity logs for security audits.
FME is a data transformation tool that offers support for geospatial data. FME lets you discover, profile, and map data so that you can set up data workflows.
FME offers several transformers to extract HTML, update or delete databases, connect with various data sources, and more. These transformers act as the building blocks of your workflow and let you modify data as per your needs.
What are some of the main data transformation capabilities of FME?
- Without writing any scripts, you can use drag-and-drop transformers to build your workflows.
- FME supports dataflows for on-premise, cloud-based, and mobile applications.
Hevo helps you set up data transformation pipelines within minutes without any coding.
Hevo supports 100+ ready-to-use integrations for databases, cloud-based applications, streaming services, and more. Moreover, it can handle millions of records per minute without latency, making it easier for you to scale your pipelines as per your requirements.
Setting up a pipeline is straightforward as once you choose your data source, add your credentials, and choose the destination warehouse to load data, Hevo builds the data flows automatically.
What are some of the main data transformation capabilities of Hevo?
- The interface is no-code and intuitive, so anyone can build data pipelines, which removes the engineering bottleneck and saves time.
- Hevo handles all pipeline operations, saving infrastructure setup and maintenance costs.
- Hevo supports reverse ETL to send warehouse data to any business application.
Matillion comes equipped with pre-built data source connectors for on-premises and cloud databases, NoSQL sources, APIs, business applications, and more. You can also set up custom connectors to simplify data extraction from various sources.
Matillion provides a drag-and-drop interface that makes it easy to create complex transformations without needing any coding skills.
Moreover, Matillion uses the NIST framework for data confidentiality, integrity, and availability. It’s also compliant with industry standards and regulations such as SOC Type II, HIPAA, CSA STAR, GDPR, and CCPA.
What are some of the main data transformation capabilities of Matillion?
- You can automate and schedule pipeline-related jobs and also automatically generate documentation for these processes.
- You maintain control and ownership of your data at all times.
- Matillion supports reverse ETL, so you can write your transformed data back out to your data warehouse or lake.
- You can choose between hourly pricing or pay-as-you-go models, or even have an enterprise contract.
Nexla makes it easy to integrate data from anywhere and transform it into a ready-to-use format.
Nexla enables self-service data preparation, wherein you don’t have to rely on engineering to prepare data pipelines or track lineage because of Nexla’s no-code interface. It offers a rich library of transformation functions to help even business users handle data transformation.
What are some of the main data transformation capabilities of Nexla?
- You can see what’s happening to your data at a glance — pipelines, transformations, integrations, and more.
- Nexla offers automated and continuous schema management, wherein you can detect schemas and observe the changes made to the schemas and their subsets/supersets.
- Nexla offers automated versioning and logs so that you know how data sets have changed and who handled those changes. This makes compliance a breeze and your data more trustworthy.
FAQs on data transformation tools
1. What is the most common way of data transformation?
ETL (extract-transform-load) and ELT (extract-load-transform) are the most common data transformation processes.
2. How do data transformation tools work?
Data transformation tools extract data from various sources and formats, transform it into the desired format, and store it in a repository — data stores, warehouses, or applications.
3. What are some of the benefits of a data transformation tool?
Data transformation is right at the center of data analytics. However, before data becomes useful for analytics, it has to be extracted from various sources and then processed, refined, and transformed into the required format.
That’s why data transformation tools are an essential ingredient of the modern data stack. They also help monitor data quality issues and handle data storage.
4. What are the different types of data transformation tools?
Yes, depending on how you want to transform the data, there are several types of tools:
- Batch: They transfer large volumes of data at a designated (scheduled) time.
- Real-time: They process data as and when it pours into your system.
With open-source becoming popular, several free, open-source ETL tools are also available. Additionally, large tech companies with vast resources tend to set up proprietary ETL tools that meet their unique requirements.
5. How can you evaluate data transformation tools?
Here are some of the things that you should consider before choosing a data transformation tool:
- The amount of data that you have and how it’s stored
- Your existing data infrastructure
- Your data consumers — are they primarily engineers and scientists, or do you also have citizen data integrators, scientists, and business users?
- Your budget
- Your use cases
- Ease of setup and use
- Security and compliance with your local and regional regulatory standards
- The reviews and testimonials on popular review portals like Gartner, G2, or Capterra
Data transformation tools: Related reads
- Data transformation: Definition, process, examples & tools
- Etl vs. Elt: Exploring the differences, origins, strengths, and weaknesses
- Top 6 elt tools to consider in 2022
- Open source etl tools: 7 popular tools to consider in 2022
- Open-source data lineage tools: 5 best tools in 2022