10 Best Data Transformation Tools for a Smoother ETL and ELT Process
Data teams deciding on data transformation tools need to consider various aspects before deciding on how they will develop and orchestrate data pipelines. They also need to accelerate infrastructure deployment to deliver at the pace the business requires.
The hurdle to overcome is that doing this well requires a lot of rethinking of legacy processes and technology.
Implementing DataOps, CI/CD, and setting up an ETL or ELT isn’t a straightforward process, which is why teams often go with an incremental approach or set up the basics and end up with technical debt that accumulates substantially over time.
In this article, we’ll go through a list of 10 data transformation tools that will help you get the job done. If you are in the process of evaluating your next ETL/ELT platforms, this article for you.
Side Note: As data professionals, we’ve been around since the early days of data transformation and noticed many flaws within the entire process. There’s a steep learning curve: adding a single tool to the workflow can quickly multiply into a tech stack with multiple SaaS platforms. That’s why we built Datacoves to help you bring everything together to accelerate time to value. If you’d like to learn more about how Datacoves helps you develop and orchestrate data pipelines, you can schedule a free demo here.
Data Transformation in the End-to End Process
Data transformation is the process of converting data from one format or structure to another. It improves the performance of data processing systems and compliance with data governance regulations.
Data transformation is just one of the steps on the road to deriving value from data.
The end-to-end process includes the following steps:
Data Extraction: To extract data from sources such as databases and APIs
Data Loading: To load data into a desired destination such as a Data Warehouse.
Data Transformation: To cleanse and transform data into usable insights based on business needs.
Data Orchestration: To schedule and automate the end-to-end process
Data Delivery: To visualize and support decision-making
Data Observability: To view and get alerted when data issues occur
It’s worth taking each of these steps into consideration when determining the best data transformation tool for your organization.
There is a common misconception that the tool alone will solve all the problems.
However, using the right tools without addressing the underlying processes can lead to a data mess that can exacerbate the underlying issue, costing more time and money. This data mess could easily be avoided in the first place, not just by having the right tools but by also having the modern best practices in place.
What is the difference between ETL and ELT?
Both help businesses extract, load, and transform data, but the sequence of events is different with their pros and cons.
ETL Process: The traditional approach to data transformation. Data is Extracted and Transformed before it gets Loaded.
ELT Process: The modern approach to data transformation. Data is Extracted and Loaded before it gets Transformed.
ELT is generally more effective than ETL processes because it removes the uncertainty of not having the necessary data for future use cases and offers more flexibility in the long term. Since storage is typically affordable, it makes more sense to simplify the ingestion process.
The 10 Best Data Transformation Tools
Here’s a list of the top data transformation tools to manage the ETL process:
Azure Data Factory
Each of these tools falls into one of two categories: code-based or visual/drag-and-drop interface. Both have their own set of pros and cons, which we’ll go through below.
Code-Based Tools for Data Transformation
Code-based tools allow you to transform data by using SQL or Python to explicitly define the transformation steps. Although it requires knowledge and experience, visual tools don’t negate the need to know SQL. This approach gives users a high degree of flexibility and control, and simplifies the maintainability and validation of work before releasing it to production.
Moreover, it is simpler to trace each data transformation step without having a disconnected document explaining what the transformation “should” do.
After having multiple conversations with data teams at enterprise companies, the challenge of developing and orchestrating dbt pipelines is a topic that has come up on numerous occasions.
There are a lot of tools to figure out when it comes to implementing the best practices for digital transformations and custom applications. It’s not uncommon for companies to end up with more than one SaaS platform and tool than they had initially planned. We built Datacoves to eliminate this need by providing the following:
Managed dbt Core
Open-source technologies, meaning there is no vendor lock-in
Managed SaaS or private cloud deployment
Datacoves focuses on helping companies accelerate growth by providing a complete ELT solution, including orchestration and visualization. Therefore, the learning curve for data transformation is minimized because of our best-practice accelerators and the available tool integrations to form an end-to-end platform.
Managed dbt Core: Get full access to dbt through Datacoves, where we provide a structured process for developing dbt pipelines. Configure a dbt environment where you can write data transformations in modular code so it’s easier to test and maintain.
Managed Airflow: Orchestrate data using Airflow, which is an integration that’s available to give companies the flexibility they need for an end-to-end process from data load to activation.
VS Code in Browser: No software installation is required, allowing you to write and edit code from anywhere as long as you have access to a web browser.
Deployable in Private Cloud: Accelerate data transformation and minimize ownership costs while complying with corporate and governmental regulations.
Internal Tool Integrations: Integrate with internal tools like Active Directory, Bitbucket, Jenkins, GitLab, and more.
CI/CD: Deliver high-quality data to users faster with more reliability and efficiency.
How Data Transformation Works With Datacoves
Here is the extended version of the ELT process with Datacoves:
Develop modular code and track version changes that you and your team can view. You’re also able to validate the quality of data transformations with our built-in testing frameworks and generate documents to leave a record of how you’re transforming data.
You develop in a VS Code environment that can be configured with a vast array of VS Code extensions and Python libraries All the modern data tools you need are provided in a structured workspace:
Is Datacoves Right for You?
It’s suitable for medium and large companies that lack the expertise or don’t want to create and manage complex data processes and need the flexibility that complex enterprise processes require.
Data teams can use all the components provided within the dbt ecosystem in a structured, methodical way with Datacoves. This means you’ll have a simplified dbt experience, yet you’ll still see the same results of dbt when used to its full potential.
Smaller companies also gain competitive advantages with Datacoves because they’ll be able to implement DataOps, follow best practices, and get a fully managed VS Code environment accelerating time to value.
dbt Cloud allows businesses to build and maintain data pipelines. It’s a cloud-based platform with a web-based IDE that allows you to transform data within a cloud data warehouse. They can help you reduce the time spent setting up an end-to-end solution.
Modular Code: Write data transformations in modular code.
Version Control: Monitor changes to your code and seamlessly collaborate with team members.
Data Testing: Built-in testing framework for validating the quality of data transformations.
Documentation: Generate documents for data transformations so you know how your data is being transformed.
CI/CD: Streamline the data transformation process.
Is dbt Cloud Right for You?
dbt Cloud works well for organizations looking to reduce the time and effort required to transform data pipelines.
Since dbt Cloud is a web-based IDE, it may feel limited for data teams that would rather use a VS Code environment. Moreover, dbt is not deployable in a company’s private cloud. It also typically requires other SaaS tools for complicated data pipelines, making it more difficult to manage unless you have the necessary integration experience with each of those SaaS tools.
Most importantly, dbt Cloud is focused solely on the data transformation step of the ELT process. Hence, you are unable to load VS Code extensions nor additional Python libraries. An enterprise with any level of complexity will also need a full-featured orchestrator.
3. Apache Airflow
Apache Airflow is an open-source platform for workflow management. You can orchestrate and schedule data pipelines. It’s a scalable and flexible platform that’s based on Python. You can also define your own operators with Airflow.
ELT pipelines: Airflow is a tool for organizing full ELT pipelines. But, it still requires your expertise to build them using Python.
CI/CD tools: You can use CI/CD tools to assist with deployment.
Data Extraction and Load: With airflow, you can create Python scripts that extract and load information from other data sources.
Python Libraries: Airflow can be paired with Python libraries like Pandas to shape and aggregate data.
Machine learning: Schedule the training of machine learning models.
Is Apache Airflow Right for You?
Apache Airflow works well for those needing a scalable data transformation tool with an open-source platform. It’s particularly a good choice for businesses mainly using Python to manage their data.
However, Airflow is primarily an orchestrator. That means you may end up building complex code in your data pipelines. Therefore, developing and maintaining this complexity requires experience and technical expertise. Managing the infrastructure for Airflow is not trivial and also requires an understanding of tools like Docker and Kubernetes.
SAS is a solution that allows you to transform and prepare data for analysis. It offers a wide range of features for data transformation, including data cleaning, data integration, and data mining.
Data Cleaning: Remove duplicate records, correct errors, complete missing values, and so forth.
Data Integration: Combine data from different departments or systems into a single dataset.
Data Mining: Identify patterns and trends in your data.
Data Visualization: Create charts and graphs to visualize data.
Is SAS Right for You?
SAS is ideal for companies with complex datasets, such as those in financial services, healthcare, and retail industries. Additionally, it’s ideal for professionals with advanced skills and knowledge in data transformation.
With that in mind, there are better solutions than SAS for those less experienced in programming and data management, as SAS licensing can be quite expensive.
SQLMesh is a complete DataOps solution for data testing and transformation. Teams can use SQLMesh to collaborate on data pipelines when transforming data.
Semantic Understanding: SQLMesh can understand the SQL written so you can write code efficiently and avoid errors.
Simplified CI/CD: SQLMesh can identify the changes made to data pipelines and apply only the necessary updates to each environment.
Column-level Lineage: Get a better understanding of the relationships between your data and the transformation process.
Transpilation: Run your SQL on multiple engines so that it’s easier to migrate data into a new platform.
Is SQLMesh Right for You?
SQLMesh is well-suited for businesses with SQL and Python expertise that need to collaborate on complex data transformations and pipelines. Although other open-source tools are available, teams can use SQLMesh to maintain data quality and perform unit testing of their transformations.
SQLMesh may not be ideal when you only need to perform simple data transformations. In this case, there are other more straightforward tools available. Moreover, SQLMesh may not be for you when your primary focus is on real-time data processing.
Visual ELT Tools for Data Transformation
Visual tools make the ELT process more straightforward by removing the need to manually write code. It works by dragging and dropping pre-built components into a canvas. This makes them ideal for data teams who aren’t as experienced in programming.
The biggest advantage of graphical tools for ETL is that people who are less comfortable with code can use them. Conversely, drag-and-drop tools typically don’t offer the same level of flexibility and control as code-based tools, which can complicate the process of debugging data pipelines and long-term maintenance.
Informatica helps you turn your data into an asset. It’s a cloud-based or on-prem solution for data management with numerous data transformation libraries and APIs available.
PowerCenter: Enterprises can use this to manage large and complex data pipelines.
Cloud Data Integration: Cloud-based integrations that allow you to move data.
Data Engineering Integration: A solution designed to assist data engineers with code development, version control, and CI/CD.
Data Engineering Streaming: Manage streaming data pipelines with data ingestion, processing, and visualization.
Is Informatica Right for You?
Informatica can be a good choice for large enterprises and data professionals looking to quickly transform large volumes of complex data using an on-premise solution. It can also be a good choice for companies that need to comply with industry-specific data standards.
However, it may be too complicated to use for some organizations. Informatica requires a team of experienced data engineers with the necessary skills and experience. DataOps can also be a challenge. Since you’ll be dealing with multiple things simultaneously, it’s easy to get lost in the process when you don’t have the full technical expertise.
Moreover, it’s an expensive solution. There are other more affordable alternatives.
Talend is a cloud-native platform deployable on public cloud solutions such as AWS, Azure, and GCP. They also offer an on-prem solution and provide a variety of components and custom connectors for data transformation.
Talend Open Studio: An open-source data integration tool for smaller workloads.
Talend Data Fabric: Manage the data integration process. Maintain data quality and governance.
Cloud Data Integration: Cloud-based data integration service with a graphical user-friendly interface for creating and managing data transformation tasks.
Built-in Data Catalog: Discover new data assets across your organization.
Is Talend Right for You?
Talend works for most businesses and data professionals. It’s particularly well-suited for those who need to:
Transform data from a variety of sources.
Migrate data to a new system.
Build and maintain a data warehouse.
Check and resolve data quality issues
Still, you may want to consider other options when prioritizing DataOps and performing highly specialized data transformations such as machine learning or NLP. Talend enterprise licenses may also be costly.
8. Azure Data Factory
Azure Data Factory helps you simplify the data transformation process at scale. You’re provided with a code-free and code-centric experience for orchestrating data transformation pipelines.
Built-in connectors: A variety of built-in connectors for popular data sources are available.
Data Orchestration: Schedule your data pipelines.
Built-in Components: Use built-in components to reshape data.
Is Azure Data Factory Right for You?
Azure Data Factory could be the right option for data professionals working within the Azure ecosystem. Azure may be worth considering when you’re looking into data warehousing using Azure Synapse and Azure DataOps and not just ELT.
However, Azure Data Factory might not be the best option when you’re on a budget. As with any visual ELT tooling, DataOps and pipeline maintainability may be more complex leading to an increased total cost of ownership.
Matillion is a cloud-based data transformation tool that provides you with on-premises databases, cloud applications, and SaaS platform integrations.
Cloud-native architecture: Matillion runs in the cloud and allows you to push down transformation logic to leverage the scalability and performance of cloud data platforms such as Amazon Redshift and Snowflake.
Visual Interface: Create data pipelines using a graphical interface, reducing the need to write code.
Library of Connectors: Access a library of pre-built transformations and connectors across a range of data sources.
High-Code and No-Code: Supports both hide-code and no-code development, making it accessible for beginner and intermediate users.
dbt Component: With the dbt component, you can embed dbt within a Matillion pipeline.
Is Matillion Right for You?
Matillion’s pre-built connectors and visual interface makes it an ideal solution for less experienced data professionals. The disadvantage is that it can be costly for businesses on a budget. Moreover, you must ensure that Matillion supports your specific requirements and how you intend to perform the data transformations. Care must be given to the long-term maintainability of pipelines that are both visual and code-based.
Getting started with Matillion is simple because they use a drag-and-drop interface for building data pipelines. But like with any other visual tool, there is still a learning curve and it’s typical to have a mix of code and visual components in a production data pipeline.
Alteryx simplifies the data transformation process. You can automate advanced analytics and prepare data through self-service. It’s an effective solution that makes it easier for teams to collaborate. Unlike the other visual tools above which are typically used by Data Engineers in IT, Alteryx is more widely adopted in less technical departments of an organization. It’s also typically paired with visualization tools like Tableau.
Drag-and-drop User Interface: The visual interface makes building and collaborating on data transformation workflows easier.
Data Loading: Connectors to popular databases and services allow you to integrate different data sources
Machine Learning: Alteryx may also be used to create simple machine learning models in a visual way
Is Alteryx Right for You?
Alteryx is a good option to help ensure teams are on the same page throughout the data workflow. Data transformation projects can be shared and feedback provided seamlessly, making collaboration easier.
The downside is that Alteryx is costly compared to the other tools on this list. Moreover, there is still a bit of a learning curve, even if you’re experienced in data analytics. You should also check that Alteryx aligns with teams for effective collaboration.
How Datacoves Can Help You Transform Data
Data transformation is a process that’s prone to multiple errors along the way. While many tools listed can help you reduce friction, they must be carefully evaluated. With Datacoves, you’ll be able to implement best data practices and DataOps so that you have a smooth process with a minimized learning curve.