Data or digital information is an essential part of today’s data-driven world. Whether people work with Microsoft Word files, Excel spreadsheets, machine learning platforms, or software for live dealer casino nz, they work with data in different formats. While there are only a few primitive data types (booleans, strings, numbers, undefined, null), the variety of non-primitive data types and structures is much wider. Of course, it is possible to convert data from one format to another with data processing functions in Python or Java. However, hand coding is not the ideal solution for complex data science workflows and quick and low-cost data analysis on a large scale. This is where ETL (extract-transform-load) or data transformation tools come in.
An automatic approach has many other benefits for businesses beyond cost savings. For example, ETL software analyzes large volumes of data in real-time and often has in-build functions to optimize the information and load it into target data warehouses. A deep-level insight into the data helps organizations and companies stay competitive and make intelligent decisions about their businesses. Want to know more about data transformation tools and their functions? Then stay with us and continue reading this article! To illustrate the concept of ETL tools more precisely, we have also made a list of 10 convenient data transformation tools below.
Key Steps In Data Transformation
To understand the data transformation process better, let’s take a look at its phases. Even if we talk about ‘data transformation,’ the whole process of data transformation of ETL tools includes three common steps. They identify and extract data from one or many source locations and convert it into the desired format. Then, the ETL software checks it and delivers the data to the destination. That’s why ETL means “Extract, Transform, Load.”After collecting data, ETL tools do ‘data mapping.’ They create a map or a plan of data transformation. To transform data, ETL tools use different techniques. For example, they clean data if there are some inconsistencies (duplicate data, missing values).
They also join datasets together or filter data, change the structure of the existing dataset, and combine certain values. ETL tools often have in-built discretization functions and may turn continuous variables into discrete values to make them easier to work with. Moreover, one of the most important goals of data transformation is to transform the data so that it matches the business intelligence requirements.
So, there are plenty of options to choose from if you use data transformation software. And to perform them, ETL tools automatically create and run the code. If there are any problems with the code, many ETL tools provide users with a coding option. So the users can change the code if needed. If the dataset doesn’t need to be transformed, ETL skips this step. To organize data, ETL software often uses various machine-learning algorithms and mathematical and statistical functions. To ensure the transformed dataset is consistent and accurate, ETL tools then perform quality checks and data validation. Only if the transformation is correct, ETL software moves to the final step, data delivery. If encoding issues occur, they can lead to serious problems and data inconsistency. It is also important to mention that data transformation also can reduce existing datasets to smaller sizes. To use ETL tools, you don’t necessarily need to be a programmer. Many of them use intuitive interfaces and visual programming languages.
Leading 9 Data Transformation Tools
To streamline the process of data transformation, businesses use different ETL tools available on the market. Each of them has its features, functionalities, usability, benefits, and flaws. Let’s go through the most widely used data transformation tools in the industry
In 2022, this automated ETL tool ranked 27 on the list of Forbes’s top 100 cloud companies. If you want to manipulate cloud data, Fivetran is the best option. Fivetran is one of the best cloud ETL tools as it automatically adapts to schema and API changes, making data access easy and reliable. It helps you develop robust and automated processes using defined schemas. This ETL software allows you to quickly add additional data sources to the workflow.
This open-source data management platform dates back to 2015. It is a platform with a user-friendly web interface where workflows can be created, planned, and tracked. It allows users to combine tasks that need to be performed in a strictly defined sequence according to a set schedule.
The user interface supports visualization of workflows, which helps to track progress and see emerging issues. Apache Airflow has integrations with Microsoft Azure and Amazon Web Services and is completely free to use. However, this ETL tool focuses mainly on data engineers.
Founded in 2011 in Manchester, Matillion is a cloud-native ETL tool. To be more precise, there are two ETL Matillion tools: Matillion ETL and Matillion Data Loader. Matillion Data Loader is free to use, but the main product, Matillion ETL, is not. This tool doesn’t require coding. It is suitable not only for data analysts and data engineers but also for average users. You can use it on such cloud data platforms as Microsoft Azure Synapse, Databricks, and Google BigQuery. Built-in no-code automation and intuitive UI make Matillion ETL one of the best modern ETL tools. Unfortunately, Matillion doesn’t support Git integration.
Dbt by Dbt Labs
Developed by Dbt Laps, Dbt has many more functions than data transformation. Dbt has an SQL transformation workflow and allows users to deploy their analytics code safely. It can extract data from both cloud data warehouses and local databases. You can work in Dbt with SQL and Python. Dbt supports Git integration and has many version control and testing functions.
Designer Cloud by Τrifacta
Founded in 2012 in San Francisco, Trifacta also has developed an ETL tool, Designer Cloud software. This platform helps data engineers and data analysts create pipelines and transform data. Trifacta supports various languages and technologies (Python, SQL, Spark). Its key features are user-friendly UI and a large library of integrations.
It is a cross-platform open-source data transformation and integration tool that provides connectivity to various databases, applications, protocols, and files. It allows developers and average users to integrate information in different formats from various data sources. The tool has an intuitive user interface that requires no coding to configure data integration jobs. Apatar comes with a pre-built set of integration tools and allows users to reuse previously created mapping schemas.
This SaaS data transformation tool was developed in 2012 for the Snowflake Cloud platform. This powerful tool covers all the phases of the data life cycle, including documentation. Datameer has a no-code approach. However, it allows users to write and execute SQL commands. It has special code and low code data transformation solutions for different industries and advanced dataset pipelines.
It is a distributed data management system for fast parallel data loading and processing with a large number of plug-ins for sources and transformations and extensive data handling capabilities. The NiFi UI allows users to switch between design, management, feedback, and monitoring.
Created by Swedish company Qlik, Qlik Compose is a powerful and agile ETL tool. It provides users with various functions to run data warehouse tasks as an end-to-end workflow, from data extraction to documentation. Its non-code automation and user-friendly UI make this software a good option for a wide number of users.
Looking for more data transformation solutions? Then take a look at this list: Talend, Hevo Data, SAS Data Management, SAP Data Services, FLIP, Denodo, Nexla, Domo, IRI, RestApp, Alteryx, Keboola, Estuary Flow, IBM DataStage, Dataform, Pentaho, Stitch, Blendo.
In a modern business environment, correct data at the right time decides everything. There are two ways to organize and analyze data: with custom code and data transformation software. The custom-coded approach is slow and may hinder the scalability of the product. And data transformation or ETL tools are reliable, innovative solutions for businesses. By using them, companies can save time and money, simplify the whole data life cycle, and easily scale up their IT products. To process its data resources in the best way possible, companies should choose the right ETL software.
Before choosing a data transformation tool for your business, ask yourself what the source and destination of your data are. What are the specific needs of your business? Consider that for example Datameer ETL tool has specific solutions for different industries. The 9 ETL tools highlighted in this article will help your business stay ahead of the curve. And if you are looking for more ETL options, there are many other great solutions on the market that are worth your attention. Use them and face all the challenges of big data reality!