Data transformation is the process of converting or mapping data from one “form” to another. This involves changing the structure of the data, its format, or both to make it more suitable for a particular application or need. There are several key steps in any data transformation process:

Data extraction: The initial step is to extract or gather the raw data from its source systems. This raw data could be stored in various places like relational databases, data warehouses, CSV or text files, cloud storage, APIs, etc. The extraction involves querying or reading the raw data from these source systems and preparing it for further transformation steps.

Data validation: Once extracted, the raw data needs to be validated to ensure it meets certain predefined rules, constraints, and quality standards. Some validation checks include verifying data types, values being within an expected range, required fields are present, proper formatting of dates and numbers, integrity constraints are not violated, etc. Invalid or erroneous data is either cleansed or discarded during this stage.

Data cleansing: Real-world data is often incomplete, inconsistent, duplicated or contains errors. Data cleansing aims to identify and fix or remove such problematic data. This involves techniques like handling missing values, correcting spelling mistakes, resolving inconsistent data representations, deduplication of duplicate records, identifying outliers, etc. The goal is to clean the raw data and make it consistent, complete and ready for transformation.

Schema mapping: Mapping is required to align the schemas or structures of the source and target data. Source data could be unstructured, semi-structured or have a different schema than what is required by the target systems or analytics tools. Schema mapping defines how each field, record or attribute in the source maps to fields in the target structure or schema. This mapping ensures source data is transformed into the expected structure.

Transformation: Here the actual data transformation operations are applied based on the schema mapping and business rules. Common transformation operations include data type conversions, aggregations, calculations, normalization, denormalization, filtering, joining of multiple sources, transformations between hierarchical and relational data models, changing data representations or formats, enrichments using supplementary data sources and more. The goal is to convert raw data into transformed data that meets analytical or operational needs.

Metadata management: As data moves through the various stages, it is crucial to track and manage metadata or data about the data. This includes details of source systems, schema definitions, mapping rules, transformation logic, data quality checks applied, status of the transformation process, profiles of the datasets etc. Well defined metadata helps drive repeatable, scalable and governed data transformation operations.

Data quality checks: Even after transformations, further quality checks need to be applied on the transformed data to validate structure, values, relationships etc. are as expected and fit for use. Metrics like completeness, currency, accuracy and consistency are examined. Any issues found need to be addressed through exception handling or by re-running particular transformation steps.

Data loading: The final stage involves loading the transformed, cleansed and validated data into the target systems like data warehouses, data lakes, analytics databases and applications. The target systems could have different technical requirements in terms of formats, protocols, APIs etc. hence additional configuration may be needed at this stage. Loading also includes actions like datatype conversions required by the target, partitioning of data, indexing etc.

Monitoring and governance: To ensure reliability and compliance, the entire data transformation process needs to be governed, monitored and tracked. This includes version control of transformations, schedule management, risk assessments, data lineage tracking, change management, auditing, setting SLAs and reporting. Governance provides transparency, repeatability and quality controls needed for trusted analytics and insights.

Data transformation is an iterative process that involves extracting raw data, cleaning, transforming, integrating with other sources, applying rules and loading into optimized formats suitable for analytics, applications and decision making. Adopting reliable transformation methodologies along with metadata, monitoring and governance practices helps drive quality, transparency and scale in data initiatives.