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Database In-Memory [clear filter]
Tuesday, March 20
 

1:10pm PDT

Oracle Database In-Memory - Real-Time Analytics
Oracle Database In-Memory introduces a new in-memory columnar format that is compatible with all existing applications. Join Oracle Product Management to learn in detail what motivated Oracle to develop this new technology and how it works alongside the traditional row format to provide a unique “dual format” architecture. This session will explore how Database In-Memory enables "Real-Time Analytics" by allowing analytic style queries to be run directly on source data. It will also explore the key performance enhancing features of Database In-Memory including in-memory scans, predicate push down, hash joins with bloom filters, vector group by aggregation, and new high-performance features like In-Memory Expressions and Join Groups. The session will conclude by exploring how the new in-memory functionality interacts with key database performance features such as Real Application Clusters (RAC), Partitioning, parallel execution, and much more.



Speakers
avatar for Andy Rivenes

Andy Rivenes

Product Manager, Oracle
Andy Rivenes is a Senior Principal Product Manager at Oracle Corporation and is a Product Manager for Database In-Memory. Prior to that Andy was a Product Manager for Heat Map, Automatic Data Optimization, SecureFiles and DBFS. Andy has been working with Oracle products since 1992... Read More →


Tuesday March 20, 2018 1:10pm - 2:00pm PDT
5-Rm 105

2:30pm PDT

Feeding a Hungry World: Using Oracle Products to Ensure Global Food Security
The International Production Assessment Division (IPAD) of the USDA’s Foreign Agricultural Service predicts two of the leading economic indicators: Global Agricultural Output and US Agricultural Output.
The Division uses a convergence of evidence approach that incorporates observation and ground truth but is also heavily dependent on remotely sensed satellite (raster) data.
IPAD’s approach has been to unpack rasters and store them as vectors in database rows and columns and then to create derivative products. Decades of product development were built on this model.
Vectorizing rasters is slow and wasteful. With pixel sizes decreasing from 25 km to 10 km for IPAD's main data streams in 2018, data size and CPU usage are expected to grow by a factor of 2.5.
IPAD will stop preparing rolled up answers to questions that may never be asked. This presentation will focus on the use of Oracle technologies including the native SDO_GEORASTER type, Columnar In Memory, and the Exadata.

Speakers
avatar for Bertin Noutchang

Bertin Noutchang

Sr Software Engineer, USDA/Asrc Federal
Mr. Noutchang has over 25 years of experience in software development, database design and Database administration. He is currently employed by InuTeq LLC as Sr software Engineer and is assigned to the International Production Assessment Division of the USDA Foreign Agricultural Service... Read More →
avatar for Mark Pelletier

Mark Pelletier

Database Engineer Senior Principal, USDA/Inuteq LLC
Mr. Pelletier has approximately 30 years of experience in sviceoftware development, database design, systems architecture and program management. He is currently employed by InuTeq LLC and is detailed to the International Production Assessment Division of the USDA Foreign Agricultural... Read More →


Tuesday March 20, 2018 2:30pm - 2:55pm PDT
3-Rm 103

2:30pm PDT

Accelerating Joins and Aggregations on the Oracle In-Memory Database
Analytic workloads are dominated by joins, aggregations, and scans. In-Memory columnar databases have optimized scans using compressed data formats and SIMD vectorization techniques, but have made little impact to the rest of the query execution plan. The Oracle Database In-Memory (DBIM) Option introduced new SQL execution operators that accelerate analytic queries by optimizing aggregation over joins for star schemas. Group-by expressions are pushed down into the scans of dimension tables, creating a unique key per distinct group called a Dense Grouping Key (DGK). A Key Vector is allocated that maps join keys to DGKs, which is used to filter non-matching rows during the fact table scan. Passing rows are then aggregated directly on compressed codes into DGK-indexed result buffers using SIMD. Our solution replaces traditional join and group-by processing (bloom filters, hash table build/probe, etc) with blazing fast inlined scan operators, improving query elapsed time by as much as 10X.

Speakers
avatar for Shasank Chavan

Shasank Chavan

Vice President, In-Memory Technologies, Oracle
Lead an amazing team of brilliant engineers in the Database organization responsible for developing Oracle's In-Memory Column Store. We currently are hyper-focused on leveraging emerging hardware technologies to build the next-generation data storage engine that powers the cloud... Read More →
avatar for Ekrem Soylemez

Ekrem Soylemez

Architect, Oracle
I have been working in Oracle RDBMS development for 23 years. During that time, I have worked on data storage, Oracle OLAP, and most recently the SQL engine.  My current focus is improving performance of analytic queries, particularly those that are run against In-Memory Columnar... Read More →


Tuesday March 20, 2018 2:30pm - 3:20pm PDT
2-Rm 102
 
Thursday, March 22
 

9:50am PDT

Analytic platform with a hybrid approach of home grown and vendor tools
Adopting any new technology is daunting, in addition to peer pressure, there is also a need to size up as data grows, all without breaking the bank. The original BIWA 2015 presentation was on how any IT shop can build a commodity Hadoop cluster with Dell servers, Redhat Linux, Infiband network cards and Cloudera HDFS. Most of the coding was in Java, and OLH (Oracle Loader for Hadoop) was used to load data into an Oracle 12c in-memory table (presented at OOW15). The expertise of Computer Science faculty, staff and students was used for the full project with vendor support.The group has since enhanced the cluster with SSD (Solid State Drives) and SPARK2, and written Python Spark Machine Learning code to  analyze Gigabyte size aircraft  logs. The visualization of the data using R-Oracle has been planned for the future.

Speakers
AA

Abhinav Arya

Research Intern, Naval Postgraduate School
avatar for Arijit Das

Arijit Das

Research Associate, Naval Postgraduate School
Arijit Das is a computer scientist with BS/MS degrees in Computer Science (MNREC, India & Oregon State University) and an MSEE from University of Nevada Las Vegas. He currently is a research faculty at the Computer Science Department of the Naval Postgraduate School in Monterey, California... Read More →
JZ

Jim Zhou

Researcher, NPS


Thursday March 22, 2018 9:50am - 10:40am PDT
4-Rm 104

3:40pm PDT

Oracle Database In-Memory Implementation and Usage
Oracle Database In-Memory is one of the most fascinating new database features in a long time, promising an incredible performance boast for analytics. This presentation provides a step-by-step guide on when and where you should take advantage of Database In-Memory, as well as outlining strategies to help you ensure you get the promised performance boast regardless of your database environments. These strategies include how to identify which objects to populate into memory, how to identify which indexes to drop, and details on how to integrate with other performance enhancing features. With easy-to-follow real-world examples, this presentation explains exactly what steps you need to take to get up and running on Oracle Database In-Memory.

Speakers
avatar for Andy Rivenes

Andy Rivenes

Product Manager, Oracle
Andy Rivenes is a Senior Principal Product Manager at Oracle Corporation and is a Product Manager for Database In-Memory. Prior to that Andy was a Product Manager for Heat Map, Automatic Data Optimization, SecureFiles and DBFS. Andy has been working with Oracle products since 1992... Read More →


Thursday March 22, 2018 3:40pm - 4:30pm PDT
2-Rm 102
 

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