Just like you take raw ingredients and transform it into an edible pizza, raw data must be transformed to be usable, too. Data transformation tools take the complexity out of having to manually reformat and adjust data to be able to gain insights, thereby enhancing data management.
Here, we will take a look at how the combination of data aggregation, data cleansing, data mapping, and data transformation techniques are crucial for any business of any size. We’ll culminate by looking at the best data transformation tools worth considering.
What is a Data Transformation Tool?
How do Data Transformation Tools Work?
What are the Best Data Transformation Tools?
What are the Benefits of a Data Transformation Tools?
What are the Types of Data Transformation Tools?
What are Data Transformation Trends?
How to Choose the Right Tool for Your Business
How to Evaluate Data Transformation Tools?
Best Practices for Data Transformation
Data transformation is the process of converting data from various data sources to a usable format so that it can be used for a variety of processes. Data transformation processes are either simple or complex. They can be done using an ETL tool, which stands for extract, transform, load or using Python scripts.
Data transformation helps to organize data for indexing in data warehouses. In many instances, data analysts, data scientists, and developers are responsible for performing data transformation. But, with the aid of data transformation tools and software, it’s been democratized for all.
Data transformation tools automate the data transformation techniques to save time, reduce errors, and streamline the work. Data transformation tools are capable of pulling raw data from different data storage bases and computer systems, streamlining data flows by automating the extraction, transformation, and loading of data.
Since most companies use cloud-based software systems that connect with one another through integrations, data transformation tools can also connect and draw the necessary data for business processes.
Such data transformation tools extract data (E), process it (T) and then move it into a unified data warehouse (L).
In the world of data transformation, one process stands out as a cornerstone: ETL, which stands for Extract, Transform, Load. This critical data integration process is fundamental to how businesses handle and make sense of their data. Let's break down each step of the ETL process to understand its importance in data transformation.
ETL (Extract, Transform, Load) is a three-step process used to blend data from multiple sources. It's often used to build a data warehouse. During this process, data is taken (extracted) from a source system, converted (transformed) into a format that can be analyzed, and stored (loaded) into a data warehouse or other system. Let's delve deeper into each step:
The first step in the ETL process is extraction. During this phase, data is extracted from various source systems. These could include:
The goal is to extract the data with as little impact on the source systems as possible. This often involves scheduling extractions during off-peak hours to minimize the load on production systems.
The transformation step is where the magic happens. Raw data extracted from various sources is cleansed, mapped, and transformed into a format suitable for analysis. This step may involve several operations:
Data models are used to ensure the transformed data is accurate and consistent, facilitating seamless integration with various databases and data warehouses.
The transformation step ensures that once the data is loaded into the target system, it’s accurate, consistent, and useful for analysis.
The final step of the ETL process is loading the transformed data into a target destination, which involves data movement. This is typically a data warehouse, but it could also be another operational system or a flat file. There are two main approaches to loading:
The loading process also needs to ensure that the data integrity is maintained and that the load is verified to ensure completeness and accuracy.
Data transformation tools reformat data from its starting state to a new state for use. Then, the data can be utilized for analysis, aggregation, or to be loaded into operational tools, such as to train machine learning platforms.
There are three main ways in which data transformation tools achieve their goal, namely:
Raw data isn’t intended for analysis. If you want to analyze data, data transformation tools can apply business logic to the raw data to answer questions and better understand trends.
Oftentimes, raw data is extremely large and difficult to handle. Data transformation tools can alleviate the chaos by reducing datasets into more manageable sizes.
It’s no secret that raw data is often rife with inconsistencies, such as field values that mean the same thing but are written differently. To ensure consistency and remove duplicates, data transformation tools help to clean data effectively for application.
It’s time to talk about data transformation tools!
Here’s a quick review of the top-performing tools. While there’s more to learn about each of them, this guide is meant to give you a bird’s eye view of what you can expect so that you can research the tools that catch your eye more thoroughly before moving forward.
As a low-code/no-code cloud-based solution, SolveXia offers automation capabilities to help make the most out of your company’s data. SolveXia provides end-to-end automation for business processes, such as reconciliation, rebate management, regulatory reporting, and data analysis, to name a few.
The benefits include: increased accuracy, reduction of manual errors, saved time, access to audit trails, and the ability to improve processes while maximizing productivity.
By doing so, SolveXia collects, combines, and cleanses data from multiple business sources so you can visualize data in dashboards and automated reports. This way, you can make informed business decisions with no down time.
Since SolveXia connects all systems, including legacy systems, you can free up your team’s time from having to conduct manual tasks and instead allocate that time to gaining valuable insights.
dbt is a transformation tool designed for analytics engineers. Built around SQL, dbt allows teams to transform data already loaded in their data warehouses, empowering them to develop faster and collaborate more efficiently. Its integration with version control and testing tools makes it ideal for maintaining a reliable analytics pipeline across departments.
Alteryx is a Windows-based solution focused on text mining and data analytics. It provides powerful automation and natural language processing capabilities, allowing users to blend and transform data from various sources such as cloud applications and data warehouses. Alteryx excels at simplifying complex data analytics workflows through its user-friendly platform.
Apache Airflow is a powerful tool for managing and automating ETL workflows. It allows developers to orchestrate complex data pipelines and workflows using Python. Airflow is best suited for advanced users who want complete control over their data transformation processes. It integrates well into any existing data environment, making it highly customizable and scalable.
EasyMorph is an intuitive platform that automates data transformation without the need for programming. It eliminates the reliance on Excel, VBA, Python, and SQL, providing features like scheduling, web APIs, and a data catalog. This makes EasyMorph a valuable tool for non-technical users looking to automate repetitive data tasks and achieve faster data transformations.
Google Cloud Dataform is a SQL-based tool designed to simplify data transformation on Google Cloud’s BigQuery platform. It allows teams to automate and manage data workflows, with features like version control and testing to ensure reliable transformations. Ideal for businesses that use BigQuery, Dataform streamlines the ETL process by enabling teams to build analytics-ready datasets with minimal coding effort.
Matillion ETL is a cloud-native data transformation tool that offers pre-built connectors and data processing components, making it easy to load data into cloud data warehouses. It also supports Python scripting for more complex data processing tasks. Matillion’s ability to handle reverse ETL ensures that transformed data can be written back into your data warehouse or lake, making it a versatile tool for modern data teams.
Datameer is a powerful no-code/low-code data transformation tool built for Snowflake users. It allows data teams to automate and explore insights using an intuitive interface, while still offering SQL-based functionality for more advanced transformations. Datameer simplifies data wrangling, enabling businesses to leverage the full potential of their Snowflake environment.
Talend is a comprehensive data integration platform designed for large-scale ETL processes. It helps businesses transform and manage vast amounts of data by collecting, cleansing, and validating data from multiple sources into a unified format. With its extensive library of pre-built connectors, Talend integrates smoothly with cloud, on-premises, and hybrid environments. The tool also prioritizes data quality, ensuring accurate and reliable insights for analytics and business intelligence.
Informatica is a leading ETL tool known for its ability to handle complex data transformation tasks across various environments. It offers a robust set of features for both batch and real-time data processing, making it an excellent choice for large enterprises. With its intuitive interface, extensive connectivity options, and strong security features, Informatica simplifies the data transformation process for both technical and non-technical users, ensuring data is transformed and governed efficiently across the organization.
With the only growing amount of data available to businesses, data transformation tools are becoming increasingly sought after. Data affects every aspect of a business, from customer service to supply chain management to financial control, and everything in between.
Data transformation tools provide various benefits like:
When selecting data transformation tools, it's important to understand the various types available and how they align with different business needs. These tools can be categorized based on their processing methods and their target market segments.
With the increasing speed and volume of data, many companies prefer real-time data transformation tools to enable on-the-spot analysis and provide the most relevant and timely insights.
Along your journey to find the best data transformation tools, it’s good to know what you can expect from data transformation trends going forward. This can help you during your decision-making as to what to look out for.
Across business departments, people have different needs when it comes to data. Data teams and data transformation tools have to be equipped to move data between applications and at the same time so that no one gets held up in their workflows.
To take it back to our pizza analogy from the get-go, raw data needs to be cleaned, just like you want to make sure your ingredients are fresh and clean before cooking with them. Data cleansing is the process that ensures datasets are free from duplicates, missing values, or incorrect data.
Small data is useful and easy to access. Wide data is what makes it possible to gain insights from small, large, structured, and unstructured sets of data. The combination of the two is providing businesses with broad and valuable information.
Of course, when it comes to data, security is always a top priority. This stands true when dealing with datasets and data transformation, which is why companies turn to modernized data infrastructure and software tools to help secure their data.
Before we dive into the best data transformation tools, here’s a guideline to help you assess which tool is most fitting for your business’ needs. Keep in mind these considerations and features:
Data transformation tools, in theory, shouldn’t require heavy IT support or data analysts to set up. After all, the point is to democratize the functions. Low-code and no-code tools allow for instant deployment so you can be up-and-running in no time.
Data mapping matches fields between databases. Some data transformation tools offer this feature so that you can visualize all data together.
If you’d like to automate and schedule tasks, look out for job scheduling which allows you to review a project timeline or visual dashboard to check task statuses.
To help streamline the set-up process, you can choose a tool that has data transformation templates. They are typically set up by industry, so you’d select a template from your relevant industry like healthcare, finance, or marketing, and then use a template to run the process.
With a fresh look at some of the most popular tools, you may be wondering how to choose the best one.
Before you make your final decision, review the following factors:
With these answers in mind, the decision could possibly become very clear! To make your decision even easier, we invite you to demo SolveXia for yourself so you can see how data automation can boost your business!
To ensure successful data transformation, organizations should follow best practices such as:
By following these best practices, organizations can ensure that their data transformation efforts are successful and provide valuable insights to support business decision-making.
Having access to data transformation tools is like having access to an automatic pizza dough roller and wood-fire oven – you’ll get the optimal result every time!
With the fast pace of data, your business relies on the ability to perform analysis in a timely manner to prosper and function efficiently. Data transformation tools are a key piece of the puzzle, and automation software like SolveXia offers these capabilities and so much more!
Book a 30-minute call to see how our intelligent software can give you more insights and control over your data and reporting.
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Download our data sheet to learn how you can run your processes up to 100x faster and with 98% fewer errors.
Download our data sheet to learn how you can run your processes up to 100x faster and with 98% fewer errors.
Download our data sheet to learn how you can run your processes up to 100x faster and with 98% fewer errors.
Download our data sheet to learn how you can run your processes up to 100x faster and with 98% fewer errors.
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