Bay Area In-Memory Computing Meetup
Join us April 30 in Menlo Park for the next Bay Area In-Memory Computing Meetup! Great food, drinks, raffle prizes -- and three stellar talks! RSVP here for this free event. Talks will include: "Ingesting Streaming Data for Analysis in Apache® Ignite™" and "Apache® Ignite™ Troubleshooting."
> Pat Patterson [Director of Evangelism at StreamSets]
> Andy Rivenes [@TheInMemoryGuy: Product Manager at Oracle for Database In-Memory]
> Stan Lukyanov, [Software Engineer at GridGain Systems]
>> Talk 1 (Pat): Apache Ignite provides a distributed platform for a wide variety of workloads, but often the issue is simply in getting data into the database in the first place. The wide variety of data sources and formats presents a challenge to any data engineer; in addition, 'data drift', the constant and inevitable mutation of the incoming data's structure and semantics, can break even the most well-engineered integration.
This session, aimed at data architects, data engineers and developers, will explore how we can use the open source StreamSets Data Collector to build robust data pipelines. Attendees will learn how to collect data from cloud platforms such as Amazon and Salesforce, devices, relational databases and other sources, continuously stream it to Ignite, and then use features such as Ignite's continuous queries to perform streaming analysis.
Pat will start by covering the basics of reading files from disk, move on to relational databases, then look at more challenging sources such as APIs and message queues. You will learn how to:
* Build data pipelines to ingest a wide variety of data into Apache Ignite
* Anticipate and manage data drift to ensure that data keeps flowing
* Perform simple and complex ad-hoc queries in Ignite via SQL
* Write applications using Ignite to run continuous queries, combining data from multiple sources
>> Talk 2 (Andy): Analytic queries typically scan large amounts of data using aggregations to find patterns or trends in the data. In a traditional row-based database this can be slow because each row must be examined to access the columns in a query. Columnar formatted data does not have this problem because just the columns in the query need to be accessed. In addition, columnar formatted data tends to compress well and work well with vectorized processing like Single Instruction Multiple Data (SIMD).
Oracle Database In-Memory can transform existing row-format database objects into an in-memory columnar format. These columnar formatted objects can be queried at orders of magnitude faster speed than the equivalent row format. This session will explore how this columnar format provides such a dramatic performance improvement for analytic queries, and how it works with the rest of Oracle Database so that no application changes are required.
>> Talk 3 (Stan): Whether you are getting started with Apache Ignite or have already deployed, this session is for you. Stan will explain how to set up deployments to make them easier to monitor, manage and keep up and running properly. He'll also hare best practice examples on how to:
* Configure Ignite and GridGain for deployment, management and monitoring
* Leverage log files during troubleshooting
* Use monitoring interfaces and tools such as JMX, Visor and Web Console
* Identify and fix top errors for newly installed and existing deployments
See you April 23! Please RSVP because space will be limited!
* 5:45 p.m. -- Dinner, drinks & networking
* 6 p.m. -- Talk 1 (Pat): "Ingesting Streaming Data for Analysis in Apache Ignite"
* 6:40 p.m. -- Talk 2 (Andy): “Oracle Database In-Memory – Columnar Formatted Data for Analytics”
* 7:20 p.m. -- Talk 3 (Stan): "Troubleshooting Apache Ignite (and best practices)"
* 7: 50 p.m. -- Raffle drawings and closing remarks
* 8:00 p.m. Finis!