When businesses gather data and process it for analytics purposes, they carry out many steps in due process. One such crucial step is transforming the data so that it matches the requirement of the business intelligence (BI) or data warehouse tools. If the transformation phase goes wrong, you might lose valuable insights, lose data, or face compatibility issues with the tool where you plan to process the data. Hence, choosing the right data transformation tool is necessary before you begin the project. But how do you do that with so many tasks and responsibilities on your plate? You do market research! Not to worry, since we have already done that for you. We have explored functionalities, features, pricing models, usability, etc., and have come up with the following data transformation tools you must try out yourself.
What Is Data Transformation?
Data transformation is the second step of the Extract, Transform, and Load (ETL) process, where your data science team transforms structured or unstructured data in a constant form that meets your business needs. It involves the following processes:
Standardizing data to convert all the data into one specific format Cleaning the raw database, like inaccuracies and inconsistencies removal Combining elements of data from multiple data models or data mapping Getting relevant data from sources other than existing databases or data augmenting
Experts also apply several business logic and rules during the data transformation process. Such rules and rationales help data scientists to produce actionable insights that will help boost business growth.
Features to Look for in Data Transformation Tools
#1. No-Code and Low-Code
Transforming your data should be easy, and most data analytics team members should be able to do this independently. You must not go for a tool that requires advanced coding skills. Look for apps that offer an easy workflow. When the task requires a few codes, an automated code-completion bot should scan the keywords you type and show the syntaxes you should use.
#2. Optional Scripting Features
For troubleshooting and complex cases, there should be a coding option so that experts can resolve issues.
#3. Data Mapping
You can only get holistic insights for business growth by mapping multiple models of data into a common visualization. Hence, before buying a data transformation tool, ensure it offers data mapping.
#4. Automation
In a data transformation project, your team needs to perform the following tasks regularly:
Receive and send emails with attachments Web requests and API calls Coding on PowerShell Running third-party apps Managing files
These are repetitive tasks. You should choose an app that can automate these tasks so that you can rely on a small data analytics team and reduce overhead expenses.
#5. Job Scheduling
The app should help you schedule tasks, get task statuses, and more from a visual dashboard or project timeline.
#6. Data Transformation Templates
Look for software that offers some popular data transformation templates that most industries use. This will help you to transform unstructured and unorganized data in a flash by just using a template. All you need to do is choose an industry like digital marketing, health care, manufacturing, eCommerce, and more. Now that you have learned the basics like data transformation definition and the features to look for, find below some outstanding tools you need to try out now:
EasyMorph
EasyMorph brings the data superpowers into the hands of your team, even without any coding skills. Now, you can say goodbye to cumbersome spreadsheets and scripts of Excel, SQL, VBA, or Python. It comes with 150+ built-in actions that you can use for the automation and transformation of visual data. Hence, teams can spend less time on data-related tasks and have to rely less on their IT department. This platform allows you to automate complex data transformations and retrieve data from anywhere. Its UI is simplistic and completely visual. Hence you do not need to know SQL or programming to run this software. Highlighted features of this tool include:
Scheduling data transformation and retrieval in the ETL process Gathering, publishing, and distributing data Web APIs and webhooks for cross-system integration Data Catalog for supervised data delivery to business users Sparing your desktop from performing heavy computation tasks
With EasyMorph, companies can organize their data in the searchable Data Catalog that facilitates seamless and governable self-service. All the team members have access to the data and can retrieve it from any remote location. Moreover, there is no need to bring the data in a file or database as this software can pull data from web APIs, remote folders, spreadsheets, text files, and cloud applications. Using this platform, you can also create internal apps for integrating data and actions of various systems. These apps not only improve team productivity but also reduce the hassle of maintenance.
Qlik Compose
Are you tired of preparing your company data for analytics? Worry no more as we introduce you to Qlik Compose, a data transformation tool that can automate the process and transfer data at a great speed. You can also use this software as an agile ETL automation tool that frees the data admins from tedious manual coding. It significantly reduces the time, chance of error, and cost of data transformation by automatic ETL code generation and data warehouse design optimization. This tool can increase the speed of the ETL process and data lake creation by 10 times. Moreover, it can also design, generate, load, and update warehouses and data lakes at high speed. Companies using this platform can also create end-to-end workflow automatically and effectively implement the best practices for analytics projects using templates. It also empowers the data admins with the following operational features:
Easily ingest, sync, distribute and accumulate data Reduce production impact with zero-footprint architecture Automate data extraction from heterogeneous sources with Qlik Replicate integration Option to choose a model-based or data-based method for data warehouse development CDC technology for real-time data extraction, loading, and synchronization
Above all, Qlik Compose effortlessly integrates with different ETL solutions like SSIS ETL and functions as a proficient tool for cloud and SQL migration.
DBT
When it comes to moving reliable data at a faster speed, DBT enables data teams to function like software engineers. This platform lets the teams generate trusted datasets for ML modeling, reporting, and operational workflows. The working process of this tool is simple. Businesses can deploy it safely and let the team members work on it in collaboration through Git-enabled version control. Companies can also test every model and share the automatically generated documentation with the stakeholders. Finally, it takes care of dependency management and lets you write modular data transformations in .sql or .py format. Notable features of this tool are:
Generate a paper trail of validated assumptions for collaborators Automatically create data dictionaries and dependency graphs Implement protection policies on branches for governed data moving Security measures with SOC-2 compliance, CI/CD deployment, RBAC, and ELT Data governance with version control, alerts, logging, and testing
Besides, DBT can generate codes using Macros, auto-complete commands, and ref statements. Supporting SQL and Python modeling facilitates a shared workspace that the data science and analytics team can use.
Domo
Domo data transformation tool that can cater to the needs of business users and IT departments alike. Everyone can have equal accessibility to the data for analytics from this platform which has a drag-and-drop UI and supports complex SQL transforms. This tool offers you various approaches for dataset transformation, such as generating visual data integration flows, using MySQL or Redshift SQL expressions, and data blending operations. What’s more, you can create a workflow once and make sure it automatically applies to business logic during every data update instance. Also, Domo notifies you with alerts when data transforms fail. Some of its top features are:
Clean, join and transform data sets without SQL coding Explore data and perform manipulative actions such as filter and group Visualize data flow by dragging and dropping data sets 1000+ pre-built cloud connectors and numerous on-premises connectors
Businesses can also generate quick and responsive transformations with the tools to extract new insights. Moreover, you can combine large datasets of multiple platforms into one dataset.
Matillion
Matillion is a cloud-native data transformation tool with ETL compliance. Hence, it can use the ETL process for database movement from one warehouse to another or one cloud to another. Some notable features of this data transformation tool are:
Reduce time to data insights and application to business scenarios Scale up anytime by using virtually infinite processing capabilities Better data security Complex business rules for challenging data sets Makes processed data accessible by the right team Streamlined and automated data preparation
The best thing is the platform offers affordable pricing plans for SMBs and premium services for enterprises. Whether you get a subscription for SMBs or enterprises, you get enterprise-grade support for all the tiers. Furthermore, once you buy Matillion Credits, you can use them on any Matillion platform, like Data Loader, ETL, etc.
Datameer
Datameer is a popular data analytics tool if you use the Snowflake data-as-a-service platform for cloud data storage and analytics. The Snowflake platform needs you to run codes to transform data before you can get actionable insights. It increases overhead costs since you need to keep a few coders in the payroll. Instead, you can move on to Datameter and forget the coding part in Snowflake. Its subscription packages are ridiculously affordable, and hence you save a lot. Apart from a no-code approach, the tool lets you execute data transformation in native SQL commands-based models using the SELECT statement. And, when needed, both non-programmers and programmers can work on the same project by combining SQL with no-code in its modular data transformation workspace. Furthermore, Datameer follows a real-time processing workflow. For example, it covers the whole data life cycle journey, like discovering data, data cleaning, data deployment, data cataloging, organizing data insights, etc., within the Snowflake cloud platform in live mode. Moreover, it offers dedicated data transformation solutions for finance, healthcare, telecommunications, retail and eCommerce, energy, utility, hospitality, and travel.
IRI
IRI is the automatic alternative to the conventional data transformation process, where you need to use Perl scripts, SQL database management, ETL tools, and custom programs. The conventional process is complex, costly, and error-prone. Instead, IRI’s data transformation tool makes your life easier. It offers everything that you need in a data transformation project, and these are:
Data aggregation Cross-calculating from large data sets Customized data transformation rules Data formats and keys Data lookup Match or join multiple data models Apply pivot formatting or remove pivots Cleanse or scrub data Re-format and re-map Data merging and sorting Data filtering
In data science, the main problem is the speed of processing because we are talking about millions of data rows and thousands of data columns. Both the ETL and SQL operations tend to slow down as you input larger datasets. IRI resolves this by using a proprietary program known as SortCL. It comes out of the box in IRI’s apps like the CoSort package and the Voracity platform. In a nutshell, the tool can process huge fact-table, roll-up aggregates, and drill-down with outstanding speed, accuracy, and efficiency.
Final Words
You must use the right techniques and tools to process your data resources. It will help you invest your business capital in the right direction and full fill your short-term or long-term business goals. If you do not follow this concept, investments in your data science project will be pointless. Hence, use any of the above data transformation tools to put your data resources and teams to good use. When trying out, do consider an app’s specialty business scopes. Otherwise, you may not get easily digestible data that you can load in business intelligence (BI) apps. We have outlined the features and functionalities elaborately, so finding the right data transformation tool from this list should not be a problem for you or your team of data scientists. You may also be interested in data lake vs. data warehouse.
