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Managed Data Transformation

Data is critical to the success of day-to-day business operations.

Big Data is not just a buzzword, but is also an accurate description of the data challenges faced by large and small enterprise solutions.

  • Digital universe of data has grown by a factor of 10 between 2015 and 2020!
  • The number of mobile phone users has reached 6.1 billion and continues to , resulting in more data
Less than 0.5% of data collected is ever analysed or used

This is where  Data Science and Data Transformation comes to play.

Data transformation, also known as ETL (Extract/ Transform/ Load) is the process via which data is converted from one format or structure to another.


Moving Data

Moving your application as a whole into the cloud, or a new data store in the cloud, you will need to change the type of data that you have.

Aggregating Data

If you want to aggregate and compare data from different sources, then data transformation is the way to do that.

Enriching Data

If you want to add information to your data or enrich it, then data transformation is useful in such a scenario. Data transformation helps when you want to perform lookups, add timestamps or add geolocation data to your existing data.

Analysing Data

If you have some form of streaming or unstructured data that you want to analyse along with some structured data that you have, then you can perform data transformation to achieve that.

Data Transformation Challenges


Transforming your data requires a team of experts in the domain and substantial infrastructure costs.


Before transforming the data, you need to cleanse it extensively. This is a time-consuming process, especially when you are dealing with unstructured data.


The process of transforming data and extracting it is slow. The transformation is done in batches and can require as much as 24 hours for one batch to complete.




  • Discover data by identifying the sources and the types of data
  • Determine and discover the data that needs transformation
  • Determine how to map, join, modify, filter and aggregate individual fields of data



  • Extract the data from the source. A source can be a database, streaming source, log files or structured sources
  • Transform the data. Transforming might include aggregating the data, editing text strings, joining columns and rows or converting the data from one format to another
  • Determine how to map, join, modify, filter and aggregate individual fields of data