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ETL Solutions, Data Migration and Integration

Acknowledging Data Management
          Best Practices with DataOps


Table of content

Understanding Data Migration Techniques and Best Practices

Data Migration involves extraction, transformation, and loading. Comprising the following steps
  • Transferring data
  • Modification of Workflows and processes
  • Integration with existing solutions
  • Implementation into the new system
The primary flow of data migration involves
  • Extraction
  • Transformation
  • Cleansing
  • Validation
  • Loading

Challenge for Data Migration and Application Performance

  • Application Performance Issues while migrating massive amounts of data during Data Migration.
  • Lack of visualization of Data Migration in Real-Time to monitor the load and throughput of Data Pipeline.
  • No mechanism to validate migrated data to Hadoop on a daily basis.
  • Secure remote access to Data Migration Tool.

Solution Offered to Build Data Migration Platform

  • Develop a Data Migration platform and analyze Hive queries with the latest technologies and distributed Agile Development methods.
  • Build A Data Migration Platform To Migrate Data From Oracle To Hive.
  • Increase Hive Query Performance.

Predictive Analysis Applications in Retail

Predictive Analysis along with Retail have leveraged the power of Big Data by providing improved customer service, targeted promotions, Decision Making, and Supply Chain Analytics.

Real-Time Applications of Predictive Analysis in Retail involve

  • Customer Behaviour Analytics
  • ClickStream Analytics
  • Predictive Maintenance
  • Fraud Detection and Prevention
  • Inventory Management
  • Asset Management
  • Recommendation Engine
  • Forecasting Trends
Increase in Risk management, Precise Marketing and Loss prevention in predictive analysis with Retail has led to improved marketing and customer engagement, price optimization, data-driven insights with the best use of statistical algorithms, data mining, and Machine learning techniques.

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