Today, to survive and stand out amongst your competition, you have to make data-driven decisions. In order to do that, you need to be collecting and analyzing as much information as possible. But how can you do that in the most efficient way? Data transformation tools are one of the weapons you have in your arsenal to enable you to make sense of your raw data.
Why do you need data transformation tools?
In the data world and in computer science in general, there is a concept called garbage in, garbage out, or GIGO for short. This principle suggests that if your input data is garbage (or in this case unstructured, flawed, or cluttered) then you will get nonsense output (e.g., garbage, or in this case, bad decisions).
Data transformation tools help you with exactly that. They simplify the process of changing the structure or values of data so they end up in the appropriate format for analysis. This process is critical in order to extract actionable insights from your data and improve your business or even your life.
Data transformation tools are growing in popularity for this exact reason and in this article, we will number the top-10 data transformation tools you can use to boost your data analytics projects and eventually improve your business decisions.
The list of top data transformation tools to use in 2022
If you already feel comfortable with handling data, then you probably already know dbt. This software is developed by dbt Labs and it keeps growing in popularity in recent years because it enables analytics engineers to transform data in their warehouses by simply writing SQL-like statements.
With dbt, you can quickly deploy analytics code and create a whole data pipeline for your data transformation needs. It follows software engineering best practices like modularity, portability, CI/CD, and documentation, making it scalable and blazing fast. Whether your data is stored in a cloud data warehouse like BigQuery, Snowflake, Redshift, or in a local database, you can transform, test, and document it via dbt.
dbt has three tiers and you can choose the one that’s best for you:
- Free: This is the “Developer” package and is free forever. It offers one developer seat and some of the most important features like Browser-based IDE, job scheduling & logging, and alerting.
- $50 per month: This tier is best for teams as it boosts collaboration. 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 representative after analyzing your actual needs. This tier offers all the aforementioned features along with Single Sign On option, custom SLAs, and professional services.
Pros & Cons
|Built for scalability and data quality.||Has a steep learning curve for less techy people.|
|Detailed documentation and a large engineering community.||Pricing can scale up in case your team has many people.|
|Support for CI/CD.|
|Natively supports Git integrations.|
Apache Airflow is an open-source workflow management platform to help you with your data engineering pipelines. It was initially started within Airbnb in 2014 and had its initial community release in 2015.
Apache Airflow aims to help you programmatically author, schedule, and monitor your workflows. It’s written purely in Python programming language and you can use all its features to create your workflows without having to deal with command-line or XML. Airflow provides a useful UI to help you monitor, schedule, and manage your workflows, helping you to always feel in control by looking at the status and logs of your completed or ongoing tasks.
Last but not least, Apache Airflow provides many plug-and-play integrations with some of the most popular operators like Amazon Web Services, Google Cloud Platform, or Microsoft Azure.
This is the best part! Apache Airflow is completely Free.
Even if the tool was initially conceived and used by Airbnb, the project was made open-source which means that it’s completely free for you to use as per your requirements. If you want to use an enterprise operator, you can select one of many (e.g. Cloud Composer from Google Cloud platform) but that comes with its own cost.
Pros & Cons
|It’s free.||It focuses mainly on data engineers and tech users.|
|It’s built upon Python which is one of the most popular programming languages.||No versioning in data pipelines.|
|Provides huge extensibility as it allows you to extend the functionality by using plugins or operators.||It’s not intuitive for new users and requires a lot of configuration to start with.|
|Has a big and helpful community behind it.|
Pandas is the most popular software library built for the Python programming language. It’s mainly used for data manipulation and analysis. Initially released in 2008, it quickly became one of the most popular and widely used libraries and is still being used by many individuals and corporations to power their data pipelines.
Pandas earned its position in the data world really fast and not by accident. It’s a fast, powerful, and flexible data analysis and manipulation tool that helps you transform data blazing fast. It’s built on top of the Python programming language and you can use every other Python library alongside making your data pipeline as custom as you please. Python is also adopted by many data and cloud operators like Google Cloud Platform and Amazon Web Services and you can support your data pipelines with other popular tools.
Pandas is free and open-source under the New BSD License which means that you can use it freely without any complications.
Pros & Cons
|Extensible as it’s built upon the Python programming language.||Can only be used by programmers.|
|Great community behind the library.||Has a steep learning curve for non-techy users.|
|Efficient at handling data.||Extensive but complex documentation.|
|Flexibility – providing a huge set of features and data representation structures.|
Trifacta was founded in 2012 and is a privately owned software company located in San Francisco mainly developing data wrangling and manipulation software. Trifacta software is a data wrangling and visual representation platform that helps data engineers prepare, clean, transform, and report their unstructured data.
Trifacta aims to become an open, interactive, self-service but enterprise-grade tool that will help you with all your data wrangling needs. It’s currently supported by all major cloud providers like Google Cloud Platform, Amazon Web Services, Microsoft Azure, or even on-premise deployment to let you enable your data pipelines based on your provider of choice. Through Trifacta you are free to generate your pipelines based on the tool of your preference like SQL, Spark, Python, or even dbt.
Last but not least, Trifacta provides unlimited scalability so that performance is never an issue. It comes with built-in governance which means that you can be sure that your pipelines are high-quality and well-tested.
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 great 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: This tier is ideal for large enterprises. 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.
The professional edition offers a 30-day free trial on the cloud provider of your choice.
Pros & Cons
|An all-in-one data wrangling platform.||Pricing can scale up in case your team has many people.|
|Supports many tools and cloud providers.|
|Small learning curve as it provides a user-friendly interface.|
Datameer is a private company founded in 2009 in San Francisco. Datameer aims to enable data engineers and analysts to transform and model data directly in their cloud warehouses via a simple SQL code or no-code interface to solve complex analytical projects.
Datameer can help you explore and transform your datasets using SQL, No Code, or both. This is perfect for tech-savvy teams and for teams with no prior experience with SQL. Datameer is built upon Snowflake. It can cover all your data life cycle journey, from discovery, transformation, deployment, and documentation all within Snowflake.
One of Datameer’s best features is the ability to perform Google-like searches in your database.
This is proprietary software which means it comes with a price. The current pricing details are not provided by the vendor but you can book a demo and try a free trial.
Pros & Cons
|Provides both code and no-code data transformation.||Limited integrations.|
|Has a reasonable learning curve.||It’s not free (although pricing plans are not provided).|
|Provides strong data integration and visualization features.|
Qlik Compose is a product created by Qlik, which is one of the biggest software companies in the business analytics field, founded in 1993 in Lund, Sweden. Qlik with its different products aims to close the gap between data, insights, and action with the only cloud platform built for Active Intelligence.
Qlik Compose is one of the products that will help you accelerate and simplify data warehouse design, development, testing, deployment, and updates.
Qlik Compose main focus is to minimize the time to analytics by simplifying the data warehouse creation and management and the ETL process. By providing important features like the automatically created ETL pipelines, it brings the agile method to data warehousing projects.
Qlik Compose provides intuitive and guided workflows to help IT teams load and sync data from various sources, and streamline data warehouse and ETL generation without coding experience. With Workflow Designer and Scheduler, you can run all data warehouse tasks as a single end-to-end process and monitor their status. You can configure your own rules ensuring data quality across all stages.
Qlik Compose is only one of many proprietary products created by click. To get the pricing details for your specific use case, you can contact the sales department to request a demo and see whether the tool and the pricing model meet your needs.
Pros & Cons
|Intuitive user interface.||It’s not free (although pricing plans are not provided).|
|No-code ETL automations.|
|Integrations with multiple sources and targets.|
|Has a reasonable learning curve.|
Easymorph is software created in 2014 in Toronto, Canada. The idea behind Easymorph was the need for a better data tool suitable for non-technical people. So the software tries to do exactly that. The concept is to be able to retrieve data from anywhere and automate complex data transformation even if you do not identify as a tech-savvy person.
Easymorph provides a whole infrastructure for visual data preparation and ETL. With more than 150 built-in actions you can do exactly what you need without having to deal with SQL and custom scripts. Moreover, Easymorph aims to simplify data retrieval. With a data catalog soon to be announced, you will be able to retrieve data from various sources such as databases, spreadsheets, emails, and even web API endpoints.
The main selling point of Easymorph is the no-code automation. You can automate your ETL processes (data retrieval, transformation, and visualizations) using visual workflows and without writing a line of code.
For the main product (Easymorph desktop), there are two available price tiers:
- Free: The free tier without any cost provides unlimited imports and exports from files and databases, a visual database query builder, and technical support from community forums.
- Professional: The professional tier costs $75 per month billed annually and provides all the features from the Free tier along with some other critical features such as unlimited actions per project, desktop scheduler, and technical support by email during business hours.
Pros & Cons
|Intuitive user interface.||No custom SLAs for the professional tier.|
|No-code ETL automations.||Limited data catalog (soon to be updated).|
|Has a reasonable learning curve.|
|Has a free version.|
Matillion was founded in early 2011 in Manchester, UK, with the goal of providing business analytics as a service. Currently, almost 10 years later, the company has more than 500 employees and is valued at almost $1.5 billion. While this might not say much to you, it shows the impact it is already making and the need for such a tool in the market.
Matillion is working to simplify your cloud ETL process. There are two products for this case:
- Matillion ETL
- Matillion Data Loader
Matillion ETL and Data Loader are tools that help you migrate and load all of your data into a cloud data warehouse of your choice. Whether your data is stored in an API, Application, database, plain file, or NoSQL database, Matillion will give you the way to extract your data.
Through an intuitive UI, you can take your data from any of these sources, transform it, and load it to the cloud data warehouse of your choice, whether that’s Snowflake, BigQuery, Amazon Redshift, or Databricks.
Matillion Data Loader is completely Free. Matillion ETL, which is the main product, works with credits as its pricing model. In detail:
- Basic: This tier charges $2 per credit and starts with 5 users. It provides unlimited read-only users, cloud-native orchestration, and job automation and scheduling.
- Advanced: This tier charges $2.2 per credit and includes everything in the Basic tier while also providing audit logs and auto-generated documentation and concurrent connections between Matillion ETL and your data warehouse.
- Enterprise: The most expensive tier suitable for large corporations at $2.3 per credit. This tier provides everything in the Advanced tier and also a git repository, external Matillion database, and data lineage which is the process of understanding, recording, and visualizing data as it flows from data sources to consumption.
One Matillion Credit equals one Virtual Core hour and every plan comes with a free trial.
Pros & Cons
|Data Loader is completely free.||Complex pricing model.|
|Support of popular sources and destinations.||Poor Git integration.|
|Very friendly and intuitive user interface.|
|Has a reasonable learning curve.|
|Provides no-code ETL automations.|
Microsoft Excel Power Query
Who is used to handling data and hasn’t used Microsoft Excel? Excel is one of the most popular software for data manipulation. Even if you manipulated data via Microsoft Excel, there’s a chance that you haven’t used Power Query. Power Query is the easiest way to connect, extract, transform, and load data from a wide range of sources.
Using Power Query you can seamlessly access data stored in various sources and reshape it, all without any use of code. It supports a wide range of connectors, such as REST APIs, databases, analytics platforms, cloud providers, and many more. If you have a programming background, you can even use Power Query SDK to build your own custom connector.
With more than 300 transformations, you can manipulate your data as per your requirements and load them into popular BI tools such as PowerBI. And the most amazing thing is that you can do all of these without writing a single line of code and by using software that you know.
Power Query comes with Microsoft Excel by default. This means that in order to use it you will need to acquire a Microsoft Excel license.
Currently, this is doable by purchasing a Microsoft 365 license which starts from $69.99/year or $6.99/month. If you buy the family package (for 2-6 people), it’s $99.99/year or $9.99/month and you can even try it for free for 1 month.
Pros & Cons
|Seamless integration with Microsoft Excel.||No visualization options.|
|Integrations with BI Tools (e.g., Power BI).||License is only via the full Microsoft 365 suite.|
|Reasonable learning curve.|
Dataform is a company that builds tools for data teams to help them manage their data infrastructure. Is a free and open-source tool that helps you manage all your data pipeline processes in cloud data warehouses. With Dataform you can develop, test, and share a centralized data model across all of your teams.
Using Dataform you can write data transformations as code. You can develop your data workflows in SQL with Dataforms’s build-in APIs and syntax. You can have version control over your data workflows and you can inspect all changes before committing. If you notice that something is wrong, you can revert back to a version without touching the command line.
As soon as your data workflows are built, Dataform deploys your SQL definitions to BigQuery, creating all the tables and views while even running tests. After the tests are run, you can trust that you have a documented and well-tested dataset and you can visualize it on your reporting dashboards. Dataform comes to tackle the GIGO principle and ensures that you are not having garbage as an output.
Dataform Core (which is the main product) is open-source and free for anyone to use.
Dataform Web is another product that provides a Web IDE, the ability to schedule a job and be alerted of its status, and the ability to write data quality tests. Dataform Web is currently free but there is a waitlist that you need to join and wait until it’s available to you.
Pros & Cons
|Supports version control.||Less intuitive for non-tech people.|
|Supports SQL-like language.||Free but there’s a waitlist.|
|Provide intuitive WebIDE for most of the actions.||Supports only BigQuery.|
Bonus: accessorial helpful tools for data transformation
Besides all-round tools that help you with many or every part of the data transformation process, there are specific tools that can help you with only a specific part of the pipeline. These tools can be just as useful as they can help you optimize or automate only a specific part.
Coupler.io is a no-code data importing solution which allows you to pull data from multiple sources and load it to a few destinations. For example, you can automate the export of datasets from Airtable to your Excel workbook every Friday without a single line of code.
Coupler.io is not a data transformation tool, but you can use it in combination with some of those mentioned above, for example, Excel Power Query. In addition, for some integrations, Coupler.io provides data transformation functionality, such as filtering, selection of columns, etc.
Automatic data refresh is the main benefit you can get with Coupler.io. This means that you can automate data flow on a custom schedule, for example, every hour from Monday to Friday. This feature is available to all users on all subscription plants.
In addition to the capability to select how often your data is refreshed, you can also change the import mode from Replace to Append. The former fully replaces the data with the newly imported records, while the latter adds the new rows to those that were imported before. This is quite useful for making a history log of changes to your data and simply for backup purposes.
Coupler.io comes with a 4-tier pricing model to meet every need. Specifically:
- Free: This tier is free forever and ideal for users with a side hustle that want to automate their data import. It offers an unlimited set of users and integrations, 100 monthly runs with a thousand rows per run, and an hourly automated data refresh.
- Professional: This tier works best for individuals with an increased need for import rows. It costs $24 per month and increases the Free tier’s runs to 2,500 with 10,000 rows per run.
- Squad: Ideal plan for small teams who collaborate on multiple sources. It costs $49 per month providing 5,000 monthly runs with up to 50,000 rows per run. Additionally, you can schedule an automated data refresh every 30 minutes.
- Business: The largest tier which is perfect for companies with multiple teams. With $99 per month, you can have 25,000 runs per month with more than 100,000 rows per run and you can schedule your automated data refresh to run every 15 minutes.
All tiers come with a 14-day free trial and you can even save 20% on every plan by paying annually.
Project Jupyter is an open-source project driven by the community to help develop open-source software and services across multiple programming languages.
The most common usage of Jupyter is through Jupyter notebooks which is a way to construct a book and document from computational material.
Many data scientists and engineers use Jupyter notebooks for data explorations. Usually using Python, you can load the libraries of your choice, import your datasets, and start exploring creating graphs and representations.
Moreover, you can transform your data using the programming language of your choice and export them in the format of your preference. Jupyter notebooks work as a middleware providing the user interface.
Last but not least, you can share the final result (notebook) with other people and they can simulate exactly what you created so they can see the process.
As with almost every open-source project, Jupyter notebooks and everything within the Project Jupyter is free to use unconditionally.
Google Data Studio
Google Data Studio is an online tool created by Google for converting datasets into customizable reports.
Initially introduced in 2016, it was part of the enterprise Google Analytics 360 suite. As it was quickly loved by the community, in May 2016, Google announced the free version of Data Studio for individuals and small teams.
Through Google Data Studio, you can create meaningful, interactive reports about your data. Visualization is a core part of every stage of the data transformation process as you may need to visualize each stage and see what needs to be done further.
With a wide range of connectors, you can easily plug your dataset and start playing with visualizations diving deep into your data. The interface follows the WYSIWYG paradigm which corresponds to “What you see is what you get”. You can easily create visualizations without any coding experience and start analyzing your data.
Currently, Data Studio and all its features lie under the Google Marketing Platform offering and are completely free for use. Any third party can create their own custom set of visualizations or connectors which may come with a cost.
Which data transformation tool is best for you?
In this article, we went through the 10 best transformation tools in 2022. These tools offer a wide range of features and are there to assist you in every stage of your data transformation needs.
Moreover, we’ve seen some bonus tools that can help with the data transformation before loading them to a particular tool. In our article ETL vs ELT, you can find more information regarding which approach is best for you.
Many of the tools we’ve shown are free but come with a steep learning curve and you’re paying the cost in configurations and maintenance. Others come with a price but can do a lot of stuff automatically.
The perfect tool is the one that is ideal for your case. So before choosing, make sure to consider your needs along with your pricing capabilities.Back to Blog