What is Data Transformation?

What is Data Transformation? | Databricks Skip to main content

Summary

Data transformation turns raw, inconsistent data from multiple sources into standardized, high-quality datasets that are ready for storage, analytics and decision-making.

Data transformation spans a wide range of operations — from cleansing, de-duplication and validation to aggregation, enrichment, joining and format changes — to improve usability, compatibility and trust in the data.

Effective data transformation combines robust ETL patterns, performance optimizations and modern tooling (like Spark Declarative Pipelines and Lakeflow Jobs) to handle batch and streaming workloads at scale on the Data + AI Platform.

What is data transformation? Data transformation is the process of taking raw data that has been extracted from data sources and turning it into usable datasets. Data pipelines often include multiple data transformations, changing messy information into clean, quality, trusted data that organizations can use to meet operational needs and create actionable insights. The data transformation process is a key process in data engineering . Why is data transformation important? The data transformation process is critical for realizing value from raw data. Organizations collect vast amounts of data that may include inconsistencies, missing values or duplicates. By cleaning and transforming datasets, leaders can be assured they are making decisions based on trustworthy, timely and accurate data. Data transformation helps improve data quality by enhancing its compatibility and usability. By standardizing formats to ensure consistency across different data sources, it becomes easier for leaders to analyze and integrate quality data into their decisions. Standardized data also reduces friction between collecting and storing data across various systems. In a business environment where data is sourced from multiple channels, maintaining uniform data is essential to achieving a cohesive understanding of information. By resolving inconsistencies and preparing it for storage in a data warehouse or data lake, data transformation creates a robust foundation for analytics, reporting and decision-making. Data transformation is also crucial for identifying and removing any duplicate records and cleaning up any missing values within a dataset. Missing or duplicate data can skew analysis and hinder the ability to draw meaningful conclusions. Additionally, data transformation ensures compatibility with various analytical tools and techniques. Raw data, for instance, often exists in disparate formats or structures, and this can be difficult to integrate with different analytical software or platforms. Standardizing data helps seamlessly integrate it across platforms so it can be mined for patterns and insights. Data transformation and ETL Data transformation is at the heart of extract, transform, load ( ETL ). This is the process data engineers use to pull data from different sources, transform it into a usable and trusted resource and load that data into the systems end users can access and use downstream to solve business problems. In the transform stage, data gets cleansed, mapped and transformed, often to a specific schema. Data transformation examples Several different types of transformation can be used to ensure the quality and integrity of data. These range from data de-duplication, which increases data quality and performance by removing duplicated data, to data refining, which increases quality by filtering out data that is irrelevant, to data integration, in which different data types are merged into the same structure. Data transformation in practice: Navy Federal Credit Union Navy Federal Credit Union is the largest credit union in the world, serving 13 million member-owners. Their priority is to provide a personalized, omnichannel experience to their members. But to understand their members better, they needed to ingest and analyze online telemetry data in real time. To accomplish that, Navy Federal turned to Spark Declarative Pipelines and Databricks SQL. Using Spark Declarative Pipelines, Navy Federal was able to complete a proof of concept in a week; develop, test and figure out a CI/CD process in three weeks; deploy the pipeline to production just before the start date of the first wave migration; and release the dashboard just a few days later. “The simplicity of the Spark Declarative Pipelines programming model combined with its service capabilities resulted in an incredibly fast turnaround time,” Jian (Miracle) Zhou, Senior Engineering Manager at Navy Federal Credit Union, said. “It truly allowed us to get a whole new type of workload to production in record time with good quality.” For more types of data transformation and their definitions, see the next section.

REPORT The agentic AI playbook for the enterprise

Read now

Types of data transformation The data transformation process can be accomplished via several different techniques, depending on the data and end transformation goal. These may include: Bucketing/binning: Dividing a numeric series into smaller “buckets” or “bins.” This is done by changing numeric features into categorical features using a set of thresholds. Data aggregation: The process of summarizing data so it can be better used in reporting and visualization. Aggregation can be achieved by using different methods, such as using the sum, average or median of values over time, space or other dimensions. Data cleansing: The process of increasing data accuracy and quality by deleting inaccurate, incomplete or out-of-date information. Data de-duplication: A compression process in which duplicate copies of data are identified and removed to speed up the data transfer process. Data derivation: Creating rules to extract only the specific information needed from the data source. Data enrichment: A process of enhancing existing data using external data sources to expand a data field or supplement missing data fields. Data filtering: Refining data to eliminate irrelevant data to display only the information that is needed. Data integration: Merging different data types into the same structure. Data integration standardizes disparate data so that it can be analyzed as a whole. Data joining: An operation that merges multiple database tables using a common data field into a single dataset. Data splitting: Dividing a single column into multiple columns in order to analyze the data. This...