Increase usage of digital technology is generating a huge amount of data. Data is growing exponentially and becoming the strategic asset of organizations. Data has potential to transform existing business and create new business opportunities. However, existing data warehouse solution or design is not capable to handle volume, variety and velocity of new data. As lot of people say it is not new technologies but data which disrupted the existing data warehouse solutions. By each passing day disruption is accelerating in the market of data warehousing solutions.

This disruption because of data is making organization think alternative solutions for build data solutions which are beyond traditional data warehouse solutions. Essential component of these solutions are following

  • Ability to handle huge volume, variety , velocity and complexity of data.
  • Overall system should be able to handle unstructured data. This leads to schema on read vs schema on write design patterns.
  • Develop insights with agility. Support machine learning and predictive analytics.
  • New development modes with role for data scientist and sandpit for model building.
  • Integrate with traditional data warehouse for existing workflow and use cases.
  • Multiple deployment model on premise, cloud and hybrid.

Hadoop and related open source technologies are playing significant role in development of modern analytics systems. However, deploying and development on these technologies alone is complex and in many cases it involves steep learning curve. Microsoft data platform and analytics solutions has evolved into complete big data platform with SQL Server technologies and Hadoop echo system components. At innovyt we believe following Microsoft technologies can provide complete big data solution . We have built our own framework to configure and ingest data for Microsoft platform . Our approach can help organization in time to market for big data solutions.

Various combination of above technologies can be leveraged depending upon the current infrastructure, future needs, type of data and other organization context. In quite a few scenario our customer tends to start with optimizing the current implementation of data warehouse by offloading ETL process. Next step after data Data Warehouse Optimization is to create Enterprise Data lake which becomes single repository for all data from organization. Each organization is different and their big data implementation journey is different as well. However, their common design pattern and best practices that can be leverage to build a roadmap for successful implementation . Our team has converted these best practices into implementation framework. Feel free to contact us for further discussion.

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