In this article, we will provide you with general information about data transformation and will find out more about the data integration process: what are the main challenges that we can face, why we need data transformation, and how to use it in our business.
Data transformation is the process of converting data from one format or arrangement to another. This also involves the data integration platform and destination system. It's similar to data cleansing and is required for task data management and raw data.
Data transformations can be classified as a variety of different activities. Depending on the needs of your project, business, target format, enterprise, and organizations, you can restore data by removing nulls or duplicate data, converting data types, database, data sources, source format, or the whole form.
The process of data transformation has two stages. Let`s find out more about each of them!
Discover the structure of the data transformations that must take place. Data mapping to find out how to map individual fields, as well as alter, enter, filter, and aggregate them. Perform data research, which requires identifying the issues and all types of data for which you are operating.
Take the information from the source. The sources may and will change and can include structured sources (databases or streaming sources) as well as log files from customers that use your web applications. Combine the data, convert the format to your specifications, edit text strings, and join rows and/or columns to complete the transformations.
Data transformation performs a variety of functions. Businesses frequently need to convert data to compare it to another data set. This allows people to make good decisions based on many types of sources rather than just one. Data transformation in marketing allows you to compare data from different campaigns, allowing you to make data-driven decisions on how to sell your product better.
There are two methods that give us more information on how to transform the data: On-premise ETL tools - ETL (Extract, Transform, Load) tools like those take a lot of the pain out of scripting the transformation because they automate the whole process.
They are hosted on business sites and often require extensive expertise and/or cost a lot. Creating a script - To extract and transform data, use scripts such as SQL or Python. This is a labor-intensive procedure. Cloud-based ETL tools - these ETL tools are based in the cloud data warehouse, which allows you to use the vendor’s infrastructure and expertise.
Same as many analytics systems, data transformation is often time-consuming, expensive, and slow. As a result, it`s preferable to use an ETL solution that can speed up the operation and help many organizations.
The data transformation process consists of four major steps.
First, let's try to answer these questions:
It's important to identify the priorities of your data transformation plan at the start of the process. Otherwise, it's easy to get lost in the numbers, and you could end up with nothing useful at the end.
These are the issues that must be addressed during the data analysis stage. Dimensional modeling, which produces two types of target tables for transformed data, is a great way to format the data interpretation. Now, let`s find more information about fact tables and dimension tables that might be useful for your business.
Fact tables: They are used to store the outcomes of the incidents being calculated and to address the question "how many" (from the dimension table). Periodic snapshot (summary of events over a daily interval of time), transaction (recording of events), and accumulating snapshot (captures the execution of a procedure, the steps of which can occur at irregular intervals, within a single record.
Dimension tables: They provide meaning for your data by answering questions like "who, what, where, when, why, and how." Dimension tables have been referred to as the “soul of the data warehouse” since they contain all entry points as well as descriptive labels that allow the system to be used for the analysis.
You can run a quality check on the data once you know what types of data formats you're dealing with and what your expectations for the transformation are. This will allow you to recognize any possible issues with your data collection, such as corrupt values or missing data points. This is a critical move because any problems with your data set will ruin your process later on. Before moving on, make sure you fully search through the package.
Now that you've considered the consistency of your data source, you can begin the process of translating the data. As previously stated, data conversion entails replacing a part of your source data with data that meets the formatting specifications of your target data format. For example, you might be converting an old HTML file written in an outdated HTML version into HTML5 - the most recent HTML standard.
Part of this step will include replacing HTML tags that are no longer available, such as dir>, with a list tag supported by modern HTML, such as ul>. It is a game-changer and includes the extract transform and load. These transformations can be carried out manually, by scripting, or through the use of a cloud-based ETL tool. As you can see, data translation involves more than just replacing specific pieces of data. It also includes restructuring the entire file from the database.
Now that you've transformed the data, it's critical to ensure that the quality of the data was preserved during the transformation process. It's important to analyze the data for errors, missing information, and other mistakes that may have happened during the data translation process.
Even if your data was perfect before the transition, it is possible that errors were made during the process, so make sure you consider them. We recommend Whatagraph as a quick and easy data transformation solution. It's a data transformation tool that can aggregate all campaign data into a single dashboard in real-time. It displays data in automated reports and well-designed custom dashboards. Whatagraph will help anyone become a data analyst by transforming the data quickly and securely.
There are two types of Data Transformations:
Batch Data Transformation (Traditional Data Transformation) - Data transformation has been a bulk or batch process in which developers write code or apply transformation rules in a data integration tool and then run that code or those rules on large amounts of data. This method will follow the same linear series of steps as the data transformation process mentioned above. The word "micro-batch" is often used when data must be converted and forwarded with low latency. That appeals to small batches of data that can be processed quickly and efficiently.
Interactive Data Transformation - Interactive data transformation is a new capability that enables business analysts and consumers to directly interact with large datasets through a visual interface, understand the characteristics of the data, and alter or correct the data via simple interactions such as clicking or selecting specific elements of the data.
Although IDT follows the same process steps as batch data integration, the main difference is that the steps are not always followed linearly and usually do not necessitate significant technical skills to complete. Several firms, mostly start-ups, such as Trifacta, Alteryx, and Paxata, provide interactive data transformation tools. They want to be effective in the data analysts. AIDT solutions have a unified visual interface that integrates previously separate measures such as data quality, data mapping, sales data, data discovery, data management, raw data, and data preparation.
Data Transformation Rules are a series of computer instructions that specify consistent manipulations to be performed to convert the structure and semantics of data from source systems to target systems. The most popular types of Data Transformations Rules are Semantic Rules, Taxonomy Rules, and Reshape Rules.
Semantic Principles - These guidelines define the definitions of data elements and how businesses use them to characterize their domain. What is a good transaction? And how should the final settled sum be calculated after refunds are taken into account? Each data provider has its semantics that makes sense in the context of its activities but must be reconciled by the data aggregator. Now, here is more information about each of them.
Reshape Rules - They define how to gather data elements from the source side and distribute them on the target side. For example, a retailer can include all transaction data in a single file. Still, the aggregator must separate it into three tables: one for transactions, one for retailer data, and one for customers.
Taxonomy Rules -These rules associate the columns and values of the data sources with the columns and values of the target data.