Simpler, Smarter and Faster Insights: Big Data Analytics Processing on streaming, hot and historical data

Simpler, Smarter and Faster Insights: Big Data Analytics Processing on streaming, hot and historical data

Over the last few years, organizations are looking towards the Lambda architecture to handle analytics in real-time in a speed layer while simultaneously ingesting data into a batch layer for long-running complex analytic models. But this architecture is not a “silver bullet”. Blending batch and speed layer views takes time, is complicated, and does not support fast decision-making. Lambda cannot execute complex processing such as correlating current events with historical context; and it cannot properly serve applications that require real-time analytics, such as fraud detection, dynamic pricing, live risk analysis, predictive maintenance, personalized offers and more.

This session introduces a new method that streamlines big data architecture; with one data ingestion layer and a unified API to the speed layer and data lake.

Leveraging the speed of in-memory computing and intelligent tiered storage; streaming, hot and historical data is immediately searchable, queryable, and available for real-time AI and machine learning models, resulting in smarter and faster results.

Benefits include:
Faster & Smarter Insights
-Real-time access and analytics on frequently-used mutable data and historical data with out-of-the-box ETL
-Acceleration of batch analytics from days to hours, or hours to minutes

Faster time-to-market
Agile application development leveraging unified API access and analytics - (Spark ML) and query (Spark SQL) - to reliable, strongly-consistent data across real-time and historical platforms

Greater Simplicity
Simpler operations and data governance with an automatic lifecycle policy
Seamless multi-region and multi-cloud replication for data lakes and data warehouses
 

Schedule:

Schedule

Room:

Room
Regency Ballroom A

Tracks:

Speakers
Rajiv
Shah
Director of Solution Architecture and Professional Services
at
Gigaspaces
Rajiv Shah is the Director of Solution Architecture and Professional Services at GigaSpaces and a key member of the in-memory computing, big data, and machine learning/AI innovation teams. He’s responsible for determining and meeting customer requirements and participates in determining roadmap and vision. Rajiv is also active in the company’s strategic technology partnerships. He has held strategic roles in several other companies, including Chase, Wipro, and Scholastic.

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