Scalable Big Data Search's Power in Achieving 40% Faster Processing Of Source Feeds

Client Challenges

• Due to a large swathe of industries, services, and operative geographical locations, the client was pushed to the corner by the huge, almost unmanageable amount of data
• The data became much heavier and complex, as it had large-sized content forms like images, graphs, reports, visual illustrations of various market conditions
• The challenge accentuated further since the client had no effective measures to handle and store new emerging forms of content as well as the huge amount of existing data
  Hampered by this severe limitation, the client wasn’t able to
• Have a quick search on huge volume of data
• Have a Near Real Time (NRT) data search capabilities
• Perform faceted search /full-text search

Our Solutions

• Considering that the real pain point of the client was their inability to manage, store and contextualize the huge chunk of data, we devised the solution accordingly
• For proper imbibing of data, we ensured data ingestion into Hadoop
• To ensure secure data storage, we suggested storage into NoSQL Columnar Database HBase
• To bring more elasticity to the client’s search, we indexed data into Search Cluster which was controlled by HBase co-processor