Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
The idea behind this is to address both the operational cost savings things like a data pool or ETL / batch replatforming brings to the table as well as the previous business value discussion, but all with (initial?) lower IT budgets
2010 Tom Davenport in HBR
To meet the needs of Creating Value from
Big Data SQL represents a new architecture for querying data in its natural format, wherever it leves, and – when running on Oracle Big Data Appliance and Oracle Exadata – provides a world-class Big Data Management System.
Big Data SQL’s Smart Scan capability radically reduces the cost of joining data with Oracle Database as well. When a join between massive data in Hadoop and smaller data in Oracle occurs, Big Data SQL can process rows using Bloom filters. This ensures that only data from Hadoop which meets the join conditions are transmitted back to the database.
As before, this can reduce the amount of data being transmitted and processed by the database by an order of magnitude or more. But in this case, Oracle Database is responsible for joining a part of average sized tables. By processing data at the source, whether it’s stored in Hadoop or Oracle Database, Big Data SQL ensures the best possible use of all the compute resources in a Big Data Management System.
Big Data SQL’s Smart Scan capability radically reduces the cost of joining data with Oracle Database as well. When a join between massive data in Hadoop and smaller data in Oracle occurs, Big Data SQL can process rows using Bloom filters. This ensures that only data from Hadoop which meets the join conditions are transmitted back to the database.
As before, this can reduce the amount of data being transmitted and processed by the database by an order of magnitude or more. But in this case, Oracle Database is responsible for joining a part of average sized tables. By processing data at the source, whether it’s stored in Hadoop or Oracle Database, Big Data SQL ensures the best possible use of all the compute resources in a Big Data Management System.