Data Engineering Resources

Data Transformation Process: 4 Types & Benefits

Data transformations are often the most complex and the costliest part of the extraction, transformation, and loading (ETL) process. They can vary from simple data conversions to extremely complex data aggregating techniques.

Enterprises use data warehouses to accumulate data from multiple sources for data analysis and research. Since organizational decisions are often made based on the data stored in a data warehouse, all its components must be precisely tested.

What is Data Transformation?

Data Transformation is the process of changing the data format, or data model of a set of data. Transformation is a key piece of the ETL, or exchange, transform, load process, Transform is the process of converting extracted data from its previous form to map it into the proper form for its end destination database. The transformation itself may take place either inside or outside the end-destination database and can involve a wide variety of changes to the data, from column storage types, file types, or even encoding types. Data transformation occurs by using rules or lookup tables or by combining the data with other data or even code.

Data Transformation is one-third of the Extract, Translate & Load (ETL) process used to support enterprise data warehousing and data integration.

The Data Transformation Process: 5 Steps

There are five main steps involved that data analysts, data engineers, and data scientists can perform to execute data transformation processes.

  1. Data discovery - Data discovery is the process of identifying and interpreting data in its original format. Typically done using a data profiling tool, this step allows developers to determine what needs to be done to the data to transform it.
  2. Data mapping - Data mapping is when data professionals plan the transformation by matching data fields and elements from one source to another.
  3. Transformation logic generation - In this phase, the logic needed to transform the data is implemented, either through data transformation tools or by developers writing scripts.
  4. Transformation logic execution - This is the step where the data undergoes the transformation, using operations like aggregation, format conversion, or merging.
  5. Review - The review step is where developers examine the code to ensure that the output data is accurate and meets the transformation requirements.

4 Benefits of Data Transformation

  • Getting maximum value from data - Data transformation tools allow companies to standardize data to improve accessibility and usability
  • Managing data more effectively - Data transformation refines metadata to make it easier to organize and understand what's in your data set
  • Performing faster queries - Transformed data is standardized and stored in a source location, where it can be quickly and easily retrieved
  • Enhancing data quality - The process of transforming data can reduce or eliminate quality issues like inconsistencies and missing values

The Importance of Data Transformation for Businesses

Businesses generate massive amounts of data to gather insights and enhance the bottom line. They use data transformation to convert disparate data into formats that are consistent and usable. The uniform data can be easily consolidated into storage to then enrich and analyze for accurate business insights.

Upon transformation, the following data-driven use cases are possible:

  1. Advanced analytics - Data teams can generate data models that can then be used to build dashboards that analyze and visualize information to gain deep insights.
  2. Data quality - Data teams can ensure the validity and quality of data after it is transformed and address any issues that arise during the transformation process.
  3. Machine learning and AI - Transformed data can aid in AI initiatives by assisting data teams in forecasting, predictive modeling, and process automation.

How Data Transformation Works

Data transformation starts with mapping. Data mapping determines the connection between the data elements of two use cases and defines how the data from the source application is transformed before it is loaded into the target. Data mapping produces a set of instructions or metadata that is needed before the actual data conversion takes place.

The structure of stored data may also range between data consumers, requiring semantic mapping prior to the transformation process. There are two approaches to transformation: ETL & ELT:

  • Traditional ETL Multistage Data Transformation - Extracted data is moved to a staging area where transformations occur prior to loading the data into the warehouse.
  • Modern ELT In-Warehouse Data Transformation - Data is extracted and loaded into the analytics warehouse, and transformations are done there. This is sometimes referred to as Extract, Load, Transform (ELT).

In-warehouse transforms are gaining traction driven by two factors:

  • The high performance and scalability of the modern analytics database
  • These types of transforms are expressed in SQL, the data manipulation language of choice

4 Types of Data Transformation

Basic Transformations

  • Deduplication - Duplicate records result in incorrect answers to queries. A common transformation is removing dupes.
  • Format Revision - Date/Time conversions, units of measure, and character set encodings are common for multinational corporations.
  • Cleaning - Null handling, standardization on things like M/F for gender is critical for grouping dimensions and getting correct summarization of metrical values.
  • Key Engineering - Occasionally, the connection between data stored in various databases is some function of a key. In these cases, key restructuring transforms are applied to normalize the key elements.

Advanced Data Transformations

  • Predication/Filtering - Only move data that satisfy the filter conditions
  • Summarization - A key element of Business Intelligence. Values are aggregated stored at multiple levels as business metrics
  • Derivation - Applying business rules to your data that derive new calculated values from existing data - for example, creating a revenue metric that subtracts taxes
  • Splitting - Splitting a single column into multiple columns
  • Data Validation - Can be a simple "if/then" calculation or can be a multi-valued assessment
  • Integration - Similar to Key Engineering: Standardize how data elements are addressed. Data integration combines different data keys and values for data that should be the same
  • Joining - Standard database joining, and more exotic joining from API or unstructured sources

3 Data Transformation Challenges

There are a few challenges to think of when transforming data, including:

  1. Data quality - Issues with data quality may arise in the discovery and mapping processes, which must be addressed before attempting to transform the data.
  2. Complex implementation processes - Data transformation processes can take a lot of time and development resources. The cost of transformation can also be high, depending on the amount of data, development resources, and tools the organization chooses to leverage.
  3. Data management restraints - Managing large volumes of data generated from new applications and emerging technologies presents challenges in efficiently handling, managing, and documenting data transformation processes.

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