9 Helpful Data Transformation Tools (With Definition)

Making data-driven decisions is necessary today if you want to survive and differentiate yourself from the competition. You must be gathering and analyzing as much information as you can in order to accomplish that. Data transformation tools are one of the tools you have at your disposal to help you make sense of your raw data, but how can you do that in the most effective way?

Garbage in, garbage out, or GIGO for short, is a concept used in the data world and computer science in general. According to this principle, you will receive nonsense output if your input data is junk (or in this case, unstructured, flawed, or cluttered). g. , garbage, or in this case, bad decisions).

Data transformation tools help you with exactly that. They make it easier to modify the values or structures of data so that they end up in the proper format for analysis. This procedure is essential for getting useful information from your data to improve your business or even your life.

For your data transformation requirements, you can quickly deploy analytics code and build an entire data pipeline with dbt. It adheres to best practices for software engineering, such as modularity, portability, CI/CD, and documentation, making it scalable and incredibly quick. You can transform, test, and document your data using dbt whether it is kept in a local database or a cloud data warehouse like BigQuery, Snowflake, or Redshift.

You can programmatically author, schedule, and track your workflows with the aid of Apache Airflow. Without having to deal with command-line or XML, you can use all of its features to build your workflows because it is entirely written in the Python programming language. In order to monitor, schedule, and manage your workflows, Airflow offers a helpful user interface (UI). By viewing the status and logs of your completed or ongoing tasks, you can always feel in control.

Although the tool was initially developed and used by Airbnb, the project was made open-source, so you are free to use it however you see fit. You can choose from a variety of enterprise operators if you want to use one (e g. The Google Cloud platform’s Cloud Composer, but that has a price.

The most well-known software library created for the Python programming language is called Pandas. It’s mainly used for data manipulation and analysis. When it was first released in 2008, it quickly rose to the top of the list of the most widely used libraries and is still in use today by a large number of people and businesses to power their data pipelines.

Pandas gained prominence in the data world quickly and deliberately. It is a quick, strong, and adaptable data analysis and manipulation tool that enables lightning-fast data transformation. It is based on the Python programming language, and you can use any other Python libraries in addition to it to create a customized data pipeline. Many data and cloud operators, including Google Cloud Platform and Amazon Web Services, have adopted Python, and you can support your data pipelines with other well-known tools.

Trifacta aspires to become an open, interactive, self-service tool that will assist you with all of your data wrangling requirements. You can currently enable your data pipelines based on your preferred cloud provider by using Google Cloud Platform, Amazon Web Services, Microsoft Azure, or even on-premise deployment. You are free to create your pipelines using Trifacta using the tools of your choice, such as SQL, Spark, Python, or even dbt.

Using SQL, No Code, or both, Datameer can assist you in exploring and transforming your datasets. This is ideal for both tech-savvy teams and teams without any prior SQL experience. Datameer is built upon Snowflake. All phases of the data life cycle, including discovery, transformation, deployment, and documentation, can be covered by Snowflake.

One of the largest software developers in the business analytics industry, Qlik was established in Lund, Sweden, in 1993. Qlik Compose is one of their products. With the only cloud platform designed for Active Intelligence, Qlik hopes to bridge the gap between data, insights, and action with its various products.

Without requiring coding knowledge, Qlik Compose offers intuitive and guided workflows to assist IT teams in loading and synchronizing data from various sources and streamlining data warehouse and ETL generation. You can run all data warehouse tasks as a single end-to-end process and keep track of their progress using Workflow Designer and Scheduler. You can set up your own rules to guarantee data quality at every stage.

Easymorph is software created in 2014 in Toronto, Canada. Easymorph was created in response to the need for a better data tool accessible to non-technical users. So the software tries to do exactly that. Even if you don’t consider yourself tech savvy, the idea is to be able to retrieve data from anywhere and automate complex data transformation.

A complete infrastructure for visual data preparation and ETL is offered by Easymorph. Without having to deal with SQL and custom scripts, you can complete any task with the help of more than 150 built-in actions. Moreover, Easymorph aims to simplify data retrieval. You will be able to retrieve data from a variety of sources, including databases, spreadsheets, emails, and even web API endpoints, thanks to a data catalog that will soon be made public.

Early in 2011 in Manchester, UK, Matillion was established with the intention of offering business analytics as a service. Nearly ten years later, the business now employs more than 500 people and is worth nearly $1 billion. 5 billion. Even though it may not seem like much to you, it demonstrates the impact it is already having and the market’s need for such a tool.

Meet dbt: The Data Transformation Tool Used by JetBlue, GitLab, Wistia & Away | Fishtown Analytics

Who uses data transformation?

Data transformation may be a common practice among professionals. The process of converting data is typically carried out by developers, data analysts, or data scientists using scripting languages like Python or domain languages like SQL. Professionals in charge of making important business decisions typically review the data for analysis in the final stage of data transformation. These experts could be directors or CEOs, business intelligence analysts or specialists. They may review the transformed data using graphs, reports, or dashboards to better understand their customer base, create strategies to boost revenue, or come to operational decisions.

How does data transformation work?

The process of transforming raw data into a different format is known as data transformation. It is a step in the extract, transform, and load (ETL) process. Businesses extract data from various internal and external systems during this process, loading the data into its final destination, which is typically a centralized data collection known as a data warehouse. Before or after the loading process, data transformation can be used to organize and structure the data in a data warehouse-compatible format. This newly converted data can be used by businesses to make important decisions and achieve their strategic objectives.

Depending on the differences between the source data’s format and the desired destination format, data transformation can be simple or complex. Organizations have a choice of finishing the data transformation process manually, automatically, or by combining both approaches. Data transformation typically involves several steps, some of which might be:

9 data transformation tools

Tools for data transformation can assist in automating the conversion of data to increase efficiency. These tools can quickly transform a lot of data, frequently in a matter of minutes. To assist you in selecting the right data transformation tool for your company, here are nine of them along with an explanation of their features:

1. IBM DataStage

The data transformation tool IBM DataStage, created by IBM, creates and executes code to convert data. Because the software’s basic edition supports on-premises deployment, data transformation can only take place on-site at an organization. In a cloud environment, DataStage’s upgraded version automates data transformation. DataStage can transform data using both ETL and ELT procedures, so it can happen either before or after the data is loaded into the desired location. Built-in search, automated failure detection, and continuous delivery from development to testing and production are a few of the software’s additional features.

2. Informatica

The Intelligent Data Management Cloud is a data transformation tool that Informatica provides. This platform transforms data on cloud or hybrid infrastructures. Using prebuilt transformations and this platform, you can map data formats without writing any code. The program connects various kinds of data sources in real time by integrating with conventional databases and other programs. Additionally, the platform is compatible with Informatica’s other data management products, such as its data catalog. Depending on various features, such as data sources, Informatica offers a variety of subscription plans. It offers a free 30-day trial for organizations.

3. Matillion

This tool gathers massive amounts of raw data and converts it into a format that can be used for business analytics. It quickly converts data by extracting it from applications, files, and databases without the need for coding. It provides prebuilt connectors to connect to numerous widely used data warehouse solutions. Additionally, you can create new custom connectors for a variety of applications or download free connectors from other platform users. Matillion offers several subscription plans for organizations. Unlimited read-only users, real-time validation, automation, and job scheduling are all included in the company’s basic plan.

4. Talend

Talend provides a platform for data integration that ingests information from various sources and structures it. It connects to on-premises or cloud-based data warehouses and integrates with different data types from different sources. Using a self-service interface, you can quickly and securely transfer data to a data warehouse for analysis. It provides scalability solutions for large volumes of data. The system integrates with a number of reputable cloud service providers, data warehouses, and analytics systems. Businesses can take advantage of a free trial offered by Talend and a range of subscription-based plans.

5. SAP Data Services

Through both ETL and ELT processes, SAP Data Services, a product of SAP, integrates and processes data from SAP and outside sources. The data management platform has a range of data integration, quality, and cleaning capabilities. On the platform, you can develop applications for transforming data. By connecting to new data sources, the software supports databases, applications, files, and transports. It connects to additional external data sources and integrates with other SAP Business Suite applications. For information about pricing, contact the company for a quote.

6. Pentaho

Pentaho integrates and analyzes business data; Hitachi Vantara purchased it in 2015. It can move data of any size or format and connects to numerous data sources. The software supports both hybrid and cloud-based infrastructures. It features a drag-and-drop interface with minimal coding required. Pentaho comes in two variations, including a free open-source community edition. Additional features available in the enterprise edition include a larger library of connectors and technical support. If you’re interested in the enterprise edition, get in touch with the business for a price estimate.

7. Trifacta

Trifacta is a free, interactive cloud platform made for data scientists and engineers. It profiles and prepares data for analytics and machine learning. Data engineering is supported by the software in cloud, multi-cloud, or hybrid environments. Trifacta collaborates with leading cloud service providers to support workloads for data preparation. It automates data visualizations to assist organizations in reviewing and analyzing this data. The platform uses machine learning to assist users in the data transformation process. Trifacta offers three pricing tiers, each of which includes data profiling, offline collaboration, and predictive data transformation. It also offers a free 30-day trial for businesses.

8. RudderStack

RudderStack is a platform for data infrastructure that gathers, modifies, and sends customer data. Its designed for developers, data analysts and product teams. By establishing connections with numerous vendors and sources, it streams data in real time. Before sending the data to a data warehouse or another location, you can transform it after it has been collected. The platform features content recommendations, personalized messaging and customer support. RudderStack provides a platform with a free version that includes more than 150 cloud destinations and support for ETL and ELT operations, among other features. It provides two additional premium versions with cutting-edge functions like data masking.

9. dbt

This software’s analytics engineering workflow transforms raw data using technology created by dbt Labs. It creates datasets for business intelligence tools and operational analytics by developing, testing, and deploying data. SQL-savvy data analysts, engineers, and developers can use this program to create data pipelines and create data transformation code. In-app scheduling, logging, and alerting are also provided by the program to guarantee workflow transparency. For businesses with larger data analytics teams, the company offers two paid versions in addition to a free version of the software for one developer.

Please note that Indeed is not affiliated with any of the businesses mentioned in this article.


What is a data transformation tool?

Tools for data transformation assist in changing data formats, implementing business logic, and other ETL “T” roles. When combining structured and unstructured data from various sources for analysis, this is particularly crucial.

What are some methods for transforming data?

Here, we have listed the top eight data transformation methods in alphabetical order.
  • 1| Aggregation. …
  • 2| Attribute Construction. …
  • 3| Discretisation. …
  • 4| Generalisation. …
  • 5| Integration. …
  • 6| Manipulation. …
  • 7| Normalisation. …
  • 8| Smoothing.

What is data transformation in ETL process?

The process of converting data from one format, such as a database file, XML document, or Excel spreadsheet, into another is known as data transformation. Typically, transformations involve transforming a raw data source into a cleaned, verified, and usable format.

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