7 Best Data Transformation Tools in 2022
By Laurent Mauer · August 23, 2022 · 11 min read
Data transformation tools are also called ETL (extract, transform and load) tools because they extract data from its source, transform it according to user requirements, and load it into the target database / data warehouse.
There are many types of ETL or reverse-ETL tools available on the market today — some are used for transactional processing while others are built specifically for data analytics.
Data transformation tools have become very popular in recent years as they provide a convenient and efficient way to convert your data from one format to another.
The main purpose of a data transformation tool is to help you convert unstructured or semi-structured data into a clean, well-organized format that can be activated in SaaS applications used by your Ops & Business teams.
This article will look at some of the more popular data transformation tools available today and how they work.
Summary
What is a Data Transformation tool? And why it is essential for your business
The term “data transformation” is frequently used in the context of data integration, where it refers to the process of moving data from one system to another.
Data transformation tools can be used to convert disparate or incompatible data sources into a common data format, such as XML or JSON.
They may also be used to change the structure of a database or spreadsheet, often by adding new fields or removing unnecessary ones.
A data transformation tool may also be used on its own to clean your data, without any other software, for simple tasks like filtering records and sorting lists by field values.
Many data transformation tools provide graphical interfaces for building rules that specify how data should be transformed.
Some allow users to create rules using SQL queries if they know SQL; others use more intuitive visual programming languages similar to those found in modern programming languages like JavaScript or Python.
There are many benefits to using a data transformation tool over manual processes:
- It saves time and money: Data transformation tools automate repetitive tasks so that they can be performed quickly and accurately. This means that less people in Tech & Non-Tech teams need to be involved in the process, saving your company time and money.
- It’s repeatable: Data transformation tools allow you to repeat the same task over and over again without having to reenter all the information each time. You simply select the source file and destination file and then select which transformation rules you want applied.
- It’s scalable: With a set of rules defined for each transformation step, you’re able to scale up or down as needed by simply changing the size of your datasets or number of records being processed at once.
List of 7 best Data Transformation tools
1. RestApp
RestApp is the next-gen Low/No Code Data Pipeline Tool to activate your data with a modern visual interface, here’s the complete guide to use it.
RestApp not only integrates with any sources and destinations via No-Code pre-built connectors but also lets you model and transform your data with a graphical user interface in a Low/No Code environment to help you work with SQL in any technical and non-technical teams.Â
Key Benefits of Using RestApp:Â
- Free plan & Free trials available to get you started.
- A graphical user interface to simply Drag-and-Drop SQL & Python built-in functions.
- Monitoring and Observability with a granular data lineage to bring you visibility at each step of the pipeline through Alerts (webhooks) and Logs
- Scalability ensured since RestApp is based on Apache Spark to provide you high performance in computingÂ
- Workspace to collaborate securely with any stakeholders: teammates, partners, clients.
Pricing
RestApp provides a fair and transparent pricing model with a usage-based approach that charges exclusively on the processing time spent by users, and not the number of connectors used nor the number of users (Editor or Viewer).
2. Alteryx
Alteryx is a low-code/no-code platform for quickly transforming raw data into actionable insights.
The Analytic Process Automation (APA) platform is used in the solution to enable end-to-end automation for data science, machine learning, and analytics operations.
This platform also provides standalone tools, such as a Transformation Tool, which may be used to, among other things, define data types, clean up missing values, encode data, and pick features.
Alteryx is an on-premises solution; however, the firm is trying to become increasingly cloud-based. This is most likely why the business just bought Trifacta, a cloud platform for profiling, preparing, and pipelining data that is open and interactive.
Pricing
Alteryx price is strongly affected by the fact that it is an Enterprise product that provides a pricing plan for individual users. Alteryx Designer’s standard license costs $5,195 per year, while the server version costs $55,000+ per year.
Pros and Cons of Alteryx
Pros
- Built for large companies
- Dense documentation to get started
- Large analytics and engineering community
- Reliable for data science and ML projects
Cons
- On-premise solution from the ground
- Tech and Non-Tech teams cannot work together
- Complex and long implementation
- Requires to rely on a network of integrators
- High pricing fees for any plan
3. Dbt
Data teams may utilize dbt to change data in their warehouses by simply writing select statements. Dbt is in charge of converting these select statements into tables and views.
Dbt performs the T in ELT (Extract, Load, Transform) procedures; it does not extract or load data, but it does transform data that has already been imported into your warehouse.
Pricing
Dbt offers three tiers to meet the users’ needs:
- Free: It offers one developer seat and some of the most important features like Browser-based IDE, job scheduling and logging, and alerting.
- $50 per month: The plan costs $50 per month per developer seat and offers all the features of the free tier along with up to 5 concurrently running jobs and API access.
- Custom: This tier is ideal for large enterprises. The actual cost is estimated by a salesperson after analyzing your actual needs.
Pros
- Designed for scalabilty and data quality
- Deep documentation
- Large engineering community
- Native Git integrations
Cons
- Natively SQL based and not NoSQL
- Requires high skills in SQL
- Pricing rapidly scales up when team members increase over time
With RestApp, be your team’s data hero
by activating insights from raw data sources.
4. Hevo Data
Hevo Data, is a No Code Data Pipeline tool that helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process.
Hevo also supports reverse ETL to send warehouse data to any business application.
Pricing
The solution provides a free plan and free trials to help you get started with data pipelines.
It is a clear choice when it comes to focus on ETL processing tools with pricing based on lines processed.
Pros and Cons of Hevo data
Pros
- You pay what you use
- Design for scalability and data monitoring
- Full end-to-end data pipeline to save time and money when setting up infra and maintenance
Cons
- No Code Data integration
- Only SQL based to transform data
- Steep learning curve for less/non techy people
5. Matillion
Matillion comes ready to integrate data sets from all cloud and on-premises databases, portable and scalable NoSQL data stores, APIs and business applications, and more. You can create entire data sets using your own connectors or plug into a few pre-built ones.
Matillion provides valuable features like automation and pipeline-related jobs scheduling and also helps in generating documentation for these processes.
Pricing
Matillion offers flexible pricing for data integration and transformation services, offering the smallest plan for $1.79 per hour, the Large plan for $3.49 per hour, and the XLarge plan for $6.49 per hour. Matillion also offers an Enterprise program that provides full customization to your data integration and transformation services.
Pros ans Cons of Matillion
Pros
- Free plan is available
- Deep integrations library
- Built-in No Code ETL automations
Cons
- Complex and rigid pricing model
- No Git integration
- Requires high skills in SQL
- No modern friendly user interface
6. Datameer
Snowflake is the foundation of Datameer.
Datameer provides an innovative data modeling and transformation toolset that allows non-coders to participate in the analytic engineering process and enhances collaboration between citizen data users and engineers to accelerate datasets’ creation for various purposes: analytics, machine learning, and reporting.
Datameer may assist you in exploring and transforming your datasets through SQL, No Code, or both. This is ideal for both tech-savvy teams and those with no prior SQL expertise. It may cover the whole data life cycle journey within Snowflake, including discovery, transformation, deployment, and documentation.
Pricing
The pricing plans are not provided on the website.
Pros ans Cons of Datameer
Pros
- Deepest integration with Snowflake
- Provides both code and low code data transformation
- Tech and Non-Tech teams can work together
Cons
- Limited integrations
- Non publicly available pricing
- No free plan available
7. Trifacta
Trifacta aspires to be an open, interactive, self-service but enterprise-grade tool for all of your data wrangling requirements. It is presently supported by all major cloud providers, including Google Cloud Platform, Amazon Web Services, Microsoft Azure, and even on-premise deployment, allowing you to activate your data pipelines based on your preferred provider. Trifacta allows you to create pipelines using the technology of your choice, such as SQL, Spark, Python, or even dbt.
Trifacta has infinite scalability, ensuring that performance is never an issue. It includes built-in governance, so you can be confident that your pipelines are high-quality and well-tested.
Pricing
Trifacta has a three-tier pricing model and you can choose the one that’s best for you:
- Starter: This is the starter module and starts at $80 per user per month (+$0.60 per vCPU/hour). It provides important features like predictive data transformation, connectivity to cloud data warehouses, and data profiling while you get support from the Trifacta community.
- Professional: This tier is for small teams and organizations. It starts from $400 per user per month (+$0.60 per vCPU/hour) but on an annual contract. This tier includes everything in the Starter tier but also universal data connectivity, scheduling data pipelines, and a shared customer success manager that will help you deploy and solve any issue with the platform.
- Enterprise: The actual cost is paid annually and is estimated by a sales representative. This tier offers all the features in the previous two tiers along with Single Sign On option, API access, and a dedicated customer success manager.
Pros ans Cons of Trifacta
Pros
- User-friendly interface
- Large library of integrations
- End-to-end data pipeline tool
Cons
- Pricing rapidly scales up when team members increase over time
Conclusion
This article has guided you about some of the best data transformation technologies on the market.Â
You may improve your data for data integration and data management by using data transformation. Businesses select data transformation technologies that provide safe transformation, enrichment, and cleaning at an affordable cost.
If you’re interested in starting your data journey, check out our website or book a demo.
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