To achieve the above-described data transformation categories and procedures, companies may use robust tools. The following are the top data transformation technologies available today:
1. IBM InfoSphere
IBM InfoSphere DataStage is a simple tool for data integration. It can clean, edit, or convert data. It allows data to be transferred instantly to data lakes and warehouses. IBM offers unique data integration capabilities that can be used for virtually any organization use case.
On-premise data integration packages offer both traditional and modern data synchronization and virtualization options. IBM offers a wide range of pre-built connectors, functionalities, and other services. IBM will continue to improve its cloud integration tool, which is the best on the market.
2. SAP Data Services
Data transformation, profiling, and text data processing can all be done on one platform with SAP Data Services, an ETL tool. SAP Data Services, an SAP product, processes data from SAP and other sources using both ETL or ELT procedures.
Data management platforms offer a variety of capabilities, including data integration, quality assurance, and cleansing. The platform allows you to create data-transforming apps. The software can connect to new data sources and supports database management systems, files, applications, and other functions. It can also link to other SAP Business Suite apps and interface with them.
3. Dataform
Dataform is a free, open-source data transformation tool that allows you to manage all your processes in cloud data warehouses. It includes Panoply and Snowflake as well as Redshift and BigQuery. SQL is a command-line tool that allows you to create robust data transformation pipelines.
Data engineers might use the ref function to quickly establish dependencies between tables. Now they can focus on analytics and less on maintaining the data infrastructure. Dataform is an excellent data transformation tool for SQL-loving data engineers.
4. Azure Data Factory
Data Factory by Microsoft Azure allows you to clean, merge and format enterprise data at a large scale. Data Factory can automatically generate your code, or you can choose to build ETL and ELT pipelines.
Azure Data Factory has been accredited by top compliance organizations such as HIPAA, CSA STAR, and HITECH. Data Factory's data transformation layer can be used for your digital transformation projects. It uses a pay-as-you-go strategy that makes it easy to scale up and down according to your requirements.
5. Qlik Compose
Qlick Compose is an ETL automation and data conversion tool that allows data administrators to eliminate the need for tedious and error-prone manual code coding. Qlik Compose dramatically reduces the risk, cost, and time involved in data transformation for business intelligence projects. It optimizes the design of data warehouses and creates ETL code automatically.
Qlik Compose Data Warehouse allows you to easily create, manage, update, and maintain data warehouses such as Oracle, Amazon Redshift, or Azure Synapse. Qlik Compose is able to automate the process of sending reliable, trustworthy, and consistent data to big data platforms such as Apache Hadoop.
6. Data Builder Tool (dbt).
You can immediately begin data transformation by writing code and then take control of the entire data pipeline. Natively, Git integrations, version control, modularity, logging, and modularity are all supported. Converting SQL queries into tables or views in well-known data warehouses such as BigQuery, Redshift, and Snowflake is easy because each data model is just a simple SELECT statement.
A neat feature of dbt is the "ref function", which allows people to refer to one data model within another. This strategy also aids in the automatic generation of dependency graphs. Their materialized tables are updated in the correct order.
Data Transformation is Crucial
Why is data transformation so important in the age of AI? Have a look on this five key Points to get an answer.
1. Data Transformation Makes it Easier to Analyze Global Markets.
If a campaign crosses international borders or serves customers in different countries, one will likely need to use certain currency changes. It is important to be strategic and cautious in such situations. You have two options: drift into the black ocean depths, or remain warm by your figurative reef.
It is important to compare historical and current currency rates when dealing with money.
When reviewing past success metrics, it could be helpful to update time zone data to reflect the global launch. This is only one example of the impact data transformation can have on financial success.
2. It can Transmit Altered Data to the Intended Recipient
Google Data Studio is one of the most popular visualization tools. It's free, reliable, and stable.
Its main drawback is its inability to gather information from more than one source. People often observe clients start their data journeys by collecting all their data into a large Google Sheet, then applying multiple data transformations to it, before integrating it with an application.
This is one benefit of data transformation, i.e. it prepares data for consumption by the intended recipient.
3. Optimize Data Usage
Data transformation can simplify data for both humans and machines. Companies are constantly producing data from increasing numbers of human and machine sources.
Standardization can help companies manage their data more effectively by standardizing different data types.
Data transformations make it easier for users to access the data they need, and this makes it much quicker to apply these modified data for business purposes.
After being modified and standardized, your data is stored in a location that makes it easy to retrieve and access. This can lead to better customer service, easier information sharing, and more effective communication, just to name a few.
4. Improved Data Quality Leads to Better Analytics Results
Because businesses often base critical decisions on data, they must be vigilant about the quality of their data.
If data is not accurate, staff and business executives may miss vital information that can help them achieve their goals. Bad data can lead to a host of hazards and expenses.
Data transformation can help your company get rid of inconsistencies and other quality issues like missing values.
Data transformations can be used to identify and fix inconsistencies and fill in any gaps. This will ensure that the data is accurate and high quality.
The use of transformation tools can greatly improve data quality and efficiency in data analysis if they are used correctly.
5. Data Applications are Expanding in Scope
Once you have converted the data, more programs can use it. This is because of the formatting requirements specific to certain programs.
A company might have to alter the data from one application that collects consumer information. Then, it can analyze in another application.
Data transformations allow for greater data utilization because they make data more accessible to other applications.
Much of the data that corporations acquire is not yet analyzed. The transformation standardizes and improves the usability of your data to make it easier to extract the maximum value.
Takeaway
Data transformation is an integral part of any IT organization. You need interoperability in order to fully utilize the potential of your IT systems. Data transformation allows information assets to be shared across platforms and systems. Data transformation also allows for greater standardization and quality improvement of enterprise data, allowing you to use it to create exponential value.