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