J Transform

Solution to enable Analytics on MongoDB data
By moving data to Hadoop Data Lake In Analytics Star Schema

Features of JTransform

Move MongoDB data to hadoop data lake for consolidated analysis across other data sources

  • JTransform makes it very easy to move mongoDb data to hadoop data lake and schedule this data transfer.
  • It is possible to move complete mongodb collection or only updated / added documents directly into HIVE tables with hive merge support for updates.
  • Defining data movement jobs is few minutes job with JTransform, saving considerable custom ETL dev test time.
  • Once in hadoop data lake, JTransform enables multiple opportunities of analytics across all data sources at organisation

Convert complex JSON document to relational schema to enable self serve reporting from any BI tool

  • In JTRansform, it is very easy to transform complex JSON to relational schema. JTransform has its own set of data transform functions, which are specially made to extract data from complex JSON structure, these are very easy to understand and use.
  • JTransform framework allows implementation of your own custom transform function. This way every possible complex extraction definition is supported by JTransform.
  • Defining transform definition is few minutes job with JTransform, no need to have custom ETL for same saving dev, test.
  • Star / Snowflake schema of data mart enables full Power of BI tools, enable non tech business users with full access to data.

Accommodate changing business requirements to data mart schema with very quick turnaround time

  • Business requirements of analytics keep on changing. So does schema of data mart.
  • JTransform way of transform to relational schema is very easily extendible. Addition of new schema, addition of fields, update to existing transform definition, all of this is few minutes task through UI. This saves considerable dev, test efforts for equivalent custom ETL.
  • Most importantly turnaround time of any change request is very quick with no impact on robustness of ETL after change.

Scalable ETL on scalable data lake to support big data stored in mongoDb

  • JTransform is built on top of hadoop data lake and utilises hadoop toolset to perform core transformations. So it is inherently scalable. It supports processing of very large data quickly
  • As data in your mongoDb scales up, JTransform will make sure that transform time remains same by scaling up cluster horizontally.

How do we help?

End to end data lake bringup and maintenance

Bring up an enterprise grade robust and scalable hadoop big data lake and reporting data mart Suitable to your use cases and data scale needs. Resolve source data complexity and bring your existing data on board maintain this platform to make sure use cases are served on time

Implement data engineering automation

Setup automated data ingestion pipeline. Design and build reporting data mart. Build ETL workflows as per analytics requirements

Implement data analytics use cases

Analyse your data, your use case needs Architect, Implement end to end ETL + interactive data store load + API layer Make MIS reporting, Machine Learning and Real Time reporting possible on this platform

We will assist to make data work for you abstracting out underlying complexity.

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