Data Transformation, without the catch
Integrate and ingest within minutes
Data discovery, without a hitch
Fast and easy transformation, on the fly
Move it all to one format, one location
Compliant and Secure
Learn more about data transformation
Data transformation involves converting one form of data (for example CSV, XML, JSON, or a database format) into another, as part of the effort to move data from one location to another.
Data transformation may be simple or complex, and manual or automated, depending on the changes your data may require between source and destination. Learn more about data transformation.
Data transformation is generally necessary in the following circumstances:
- when data lives in different locations and formats, and must be combined to exist in only one;
- when data no longer belongs to a single department, but to an entire enterprise;
- when data that has long been siloed internally is to be taken into general, even public, use;
- when data is unintelligible between applications and databases across the enterprise;
- when data needs to be migrated from one data location to another.
When data is transformed, it may be adjusted to conform to new schemas, formats, and compliance requirements to which it previously could not. With a solid data-transformation effort, data can reach its potential in delivering meaningful benefits to its intended audience.
Data discovery happens when profiling scripts or tools are used to elicit the structure and unique characteristics of the source data. At the data mapping stage, data is assessed — and rules created — for mapping, modifying, joining, filtering, or aggregating the data.
During code generation, executable code is produced to transform data according to the mapping rules. Code execution involves running the rules against the data to create the desired transformation. Finally, the transformed data is reviewed to ensure the transformation happened successfully, and any anomalies and errors are addressed.
Occasionally, process improvement takes place once everything else is complete, where code is optimized, lower-priority errors in the code are fixed to relieve technical debt, or new requirements are implemented.
Firstly, Alooma lets you pull in data from multiple sources, whether on-prem or in the cloud.
Alooma's Code Engine lets you add custom code to transform your data any way you need. Among other things, you can use it to cleanse and enrich incoming data.
With our Mapper, you can use automatic one-click and custom mappings, on either structured or semi-structured data. You can also, on the fly, define data types and specify the destination for your data.
Our Mapper also handles schema and type mismatches during processing — you can catch any schema and type, adjust it in real time, and import it into your data warehouse. Whether your source data comes in flat files, RDBMS, S3 buckets, CSVs, JSON, or something else, with Alooma, you can import it all and combine it to a single data store.