Data Transformation

Data transformation has never been easier. By standardizing and simplifying the process, Alooma lets you combine your data from multiple sources to a single destination with minimal planning and effort.

Data Transformation, without the catch

In the past, data transformation created problems all through the process: during discovery, rule generation, execution, and even review. Alooma handles all the above, simply, flexibly, and on the fly!
Integrate and ingest within minutes

Integrate and ingest within minutes

Alooma supports hundreds of inputs, including all of the most popular. Whether high or low volume, and structured or unstructured data, Alooma can do it.
Data discovery, without a hitch

Data discovery, without a hitch

Discover the structure of your source data with no effort, and easily map, filter, join, or aggregate your data for transport to its destination.
One-click input-to-mapping

One-click input-to-mapping

Create a new data source and map it automatically to its destination with Mapper's OneClick mode. Drill down to finer detail when you need to with Custom mode.
Fast and easy transformation, on the fly

Fast and easy transformation, on the fly

Cleanse values, enrich data, discard undesired events, split single events, generate notifications, and even gather metrics on incoming data with a code snippet in Alooma's Code Engine. Execute your transformation with lightning speed and super low latency.
Move it all to one format, one location

Move it all to one format, one location

Alooma takes the busywork out of data discovery, mapping, and transformation to make your data migration easier than ever.
Compliant and Secure

Compliant and Secure

Alooma is proudly 100% SOC 2 Type II, ISO27001, HIPAA, and GDPR compliant, and our supported cloud service providers meet the strictest standards in the industry. Rest assured — your data is secure.

Learn more about data transformation

What is 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.

When is data transformation necessary?

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.

How is data transformation accomplished?

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.

How Alooma can help

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.


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