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Big Data


Build profitable and scalable Big data structure for your enterprise. An architecture which is not only sustainable by also expansive and modular in design. This will help justify the investments and streamline your value realization by design.

With the advent of Big Data, enterprises have access to the 100% enterprise data available including trillions of transactional data from enterprises:

Handling massive amounts of data poses its own challenges, and the ROI of big data initiative is achieved based on use case criticality and modularity of big data Architecture.

The essential components of a Big Data Architecture are:
The logical architecture of Big Data Enterprise is as shown below:
The essential components of a Big Data Architecture are:

Azure Hosted

SQL ServerAnalytical Data Store
Azure StorageRaw Data store
HDInsightData Processing Layer
Azure Express RouteCloud Integration

AWS Hosted

AWS RedShiftAnalytical Data Store
AWS S3Raw Data store
AWS EMRData Processing Layer
AWS VPC, AWS Cloud Watch, AWS Direct ConnectCloud Integration
Creating the Business Value Layer – ‘The Business Data Lake’:

The Data Lake is the hub of business value proposition for establishing a Big Data ecosystem. While the ROI of the ecosystem will depend on the use cases chosen and their criticality, the Business Data Lake helps in accelerating the value realization.

What is a Data Lake?

The Data Lake is a centralized pool of disparate data sources in one location. Data Lakes promise rich analytical insights through faster data ingestion ,and focus on storing data from disparate sources ignoring the governance part of it.

How to enable the business layer?

Making a ungoverned and uninformed Data Lake business centric requires establishing the following layers and critical consideration of the parameters described below

Layers for business mapping:
  • Process maps linking business processes to data
  • Business maps outline the starting point and ending point along with usage based data
  • Registers and Journals storing the semantics and information of data
Parameters to take care of:
  • Audience like Data scientists, Data Analysts, EDW requiring custom data views
  • Data Lake Skills provision a democratic data view and self-service view at one end of value chain to complex insights and outcomes creation by Analytics scientists
  • Data Traceability through provisioning Data Source Catalogue, Business Glossary, Access Audit and versioning and Model usage repository

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