Data transformation is the process of changing the data format, structure, values or arrangement to another particular one. Data transformation is vital toseveral kinds of activities such as data management and data integration.
Methods to Transform Data
- Extraction and parsing
- Translation and mapping
- Filtering, aggregation, and summarization
- Enrichment and imputation
- Indexing and ordering
- Anonymization and encryption
- Modeling, typecasting, formatting, and renaming
Processes such as data integration, data migration, data warehousing, and data wrangling all may involve data transformation.
Data transformation may be defined as these types:
|Constructive||Add, copy, and replicating data|
|Destructive||Deleting fields and records|
|Aesthetic||Standardizing salutations or street names|
|Structural||Rename, move, and combine columns in a database|
2 stages of data pipeline for data analytics projects:
- On-premises data warehouses: use an ETL (extract, transform, load) process, in which data transformation is the middle step
- Use cloud-based data warehouses: can scale compute and storage resources with latency measured in seconds or minutes.
The scalability of the cloud platform lets organizations skip preload transformations and load raw data into the data warehouse, then transform it at query time — a model called ELT (extract, load, transform).
An enterprise can choose among a variety of ETL tools that automate the process of data transformation. Data analysts, data engineers, and data scientists also transform data using scripting languages such as Python or domain-specific languages like SQL.
Benefits of Data Transformation
Transforming data yields several benefits:
- Better-organized: easier for both humans and computers to use.
- Improve data quality and protects applications from potential landmines
(null values / unexpected duplicates / incorrect indexing / incompatible formats)
- Data transformation facilitates compatibility for multiple purposes