Data transformation is a critical part of any data science project. It involves taking raw data and transforming it into something that can be used in machine learning (ML) and decision making. As the volume of data increases, the need to quickly and accurately transform this data also grows. Fortunately, there are many tools available today to help with this process.
In this article, we'll explore eight of the best data transformation tools for 2023 and how they compare against each other. We will then explain why these tools can be beneficial for your business and showcase what they can do for you.
Data transformation can be one of the most time-consuming and frustrating parts of any data analysis process. Whether it’s turning your database into a usable format, trying to use complicated code languages like Python or SQL, or transforming data into the right structure for machine learning models – manual coding is tedious, slow, and prone to errors. It can exhaust business resources, but luckily there is an alternative: using tools for data transformation.
Not only do such tools provide more accurate data insights, but they also save time and money. Here are the top three reasons why you should consider using a tool for data transformation:
With a data transformation tool, manual coding is no longer necessary. These tools can be used to convert large amounts of data quickly and easily without any coding knowledge or experience required.
As a result, you don’t have to spend hours trying to figure out the best way to transform your data into its desired format or learning complicated code languages like Python or SQL in order to do it correctly.
Tools available on the market today are much cheaper than hiring someone with expertise in data science or coding who would charge per hour for custom code development.
Furthermore, you don’t need any additional hardware as most tools run on cloud-based services where all the resources needed for operation are provided by the software, often at no extra cost to you.
Using a tool specifically designed for data transformation will help ensure accuracy throughout the process, due to built-in checks and safeguards. The possibility of human error is a lot higher when manually setting up a dataset for ML.
Overall, tools for data transformation can be incredibly helpful for businesses looking to improve their analysis and save time and money. Investing in one of these tools could make a huge difference in the quality of your analysis and insights into your data.
Using a tool for data transformation can be incredibly helpful for businesses looking to improve their analysis and save time and money. From Akkio’s automated pipelines to Hevo Data’s more technical tools – there are many options available that can help you get the most out of your datasets while avoiding manual coding which could take hours or days depending on the complexity of your project.
Some data wrangling tools are more suited to experienced data engineers, who need specific features such as version control systems or pre-built connectors for Amazon Redshift, while other SaaS tools are more user-friendly and self-service, designed for data analysts or even business users who are just getting started with the ELT/ETL process.
Here’s a look at 8 popular tools.
Akkio is a cloud platform and fully no-code AI platform with an automated data pipeline. This means that data integration, data aggregation, and data modeling is easy with a few clicks; Akkio can also automatically refresh insights with job scheduling.
After connecting and preparing data sources, Akkio’s no-code AI flow editor helps to quickly build and deploy ML models in any setting. Just as you can connect data sources from tools like Hubspot, Salesforce, Google Sheets, or even Google BigQuery, you can also integrate the finished ML models into your existing systems with our API.
Simply put, data teams can connect any data formats with our no-code data flow. Scalability is also central to Akkio's data infrastructure, so you can connect big data from data lakes and data warehouses for use cases like fraud detection.
In one example, financial services teams can use Akkio to build and integrate a credit approval model into a landing page chatbot for loan applications. This would allow them to process and approve loan requests in real time. Financial teams also use Akkio to detect fraud, model costs, and more.
Similarly, sales and marketing teams are using Akkio to automate lead scoring, predict and prevent customer churn, analyze customer lifetime value, and optimize marketing campaigns. Akkio's starter plan starts at $60/month, which scales depending on the needs of your business.
Excel is the most used data analysis tool in the world, and this feature provides a quick and easy way to transform data. Power Query is an Excel function that helps users extract, transform and load (ETL) data for analysis.
It allows you to quickly turn a large database into a single table, import external data and convert it into more digestible formats that can be used to get insights, match data from multiple columns, and more.
The transformed data can then be used in Excel to create charts and reports or exported as CSV files for further manipulation in other tools. Power Query does not require any coding knowledge or experience – just some basic understanding of how spreadsheets work. There’s no cost associated with using Power Query since it’s already included in Microsoft Office products.
dbt (Data Build Tool) is an open-source command line tool designed specifically for transforming datasets for ML modeling, reporting, and operational workflows. This tool has been praised by many experts as one of the fastest ways to process large volumes of complex data sets at scale due to its speed in generating queries without sacrificing the accuracy of results. dbt also supports a variety of different data sources, allowing users to work with intricate datasets from multiple sources.
However, it is important to note that the query and code language used in dbt (SQL) requires an advanced level of expertise which makes it better suited for businesses with teams of data scientists who are more technical.
Datameer works specifically with the Snowflake Data Cloud, making it especially useful for companies already utilizing Snowflake’s services. Datameer offers unique features like its search function which lets you search through a database and its data lineage feature which helps track how your data was processed. It also provides full management for metadata like tags and descriptions so you can organize your datasets more efficiently.
Hevo Data is a cloud-native platform that helps automate data transformation pipelines using either a drag-and-drop interface or their Python SDK.
Hevo allows users to create an end-to-end pipeline without having to write any queries or maintain databases – all from one central dashboard showing all elements of the pipeline with alerts and activity logs. It supports both streaming and batch methods as well as real-time integration from over 100+ sources such as Salesforce, Google Analytics, and more.
Hevo Data also offers a free 14-day trial for those who want to test it out before committing.
Matillion is a cloud-native data transformation software offering two main products: Matillion ETL and Matillion Data Loader. It provides users with an easy drag-and-drop interface that simplifies complex data transformations so users can quickly map, transform and cleanse their data without needing to write any code.
It ships out with over 100 pre-built components which makes it one of the fastest ways to get your data ready for ML models or reporting needs. Additionally, you can easily automate pipelines and schedule them as needed – making your processes even easier.
Trifacta is another great tool for transforming semi-structured datasets into structured ones that can be used in other tools such as TensorFlow or Scikit Learn. Its platform visually represents the user’s data allowing them to manipulate it intuitively using its built-in algorithms like advanced matching capabilities, cleansing functions, and more.
Trifacta is also supported by major cloud providers like Google Cloud, AWS, and Microsoft Azure, allowing you to use the cloud-based version of Trifacta for data processing and storage.
Informatica is a cloud-based data management tool that helps users transform different types of data sources in real-time with pre-built transformations – no coding needed.
It is best suited for businesses with large datasets and multiple sources (including both SQL and NoSQL databases). Furthermore, its integration capabilities allow it to be used across different departments within an organization, making it more versatile than other tools on the list.
Akkio is a great choice for businesses of any size and type looking to simplify their data transformation process. Its visual platform makes it easy for users with no experience in coding or data science to use, but also allows experts to create complex pipelines quickly.
Best of all, it doesn’t require costly training for the models like other “legacy” AI platforms like Google Cloud do – making Akkio an ideal option for businesses looking to get the most out of their data without breaking the bank.
Despite being among the most affordable tools on the market, Akkio offers an array of features that make it highly competitive among enterprise solutions, including blazing-fast ML model building and integration with your existing systems.
Data transformation is an important part of any data science project and applying AI and ML to your business. It is a crucial initial step that can help you make more accurate analyses and better business decisions.
Akkio is the ideal tool for businesses looking for a no-code way to quickly transform their datasets into clean, structured data that’s ready for model building and integration with their existing systems. With its affordable pricing plans and intuitive user interface, Akkio makes it easy even for those without coding or data science experience to get started transforming their data in minutes.