Name: 2 Talks! Analyzing streaming data + Columnar Formatted Data for Analytics
Start: 2019-04-30T17:45:00-07:00
End: 2019-04-30T20:00:00-07:00
Location: Bootup

Join us April 30 in Menlo Park for the next Bay Area In-Memory Computing Meetup! Great food, drinks, raffle prizes -- and two stellar talks!

Our speakers:
> Pat Patterson [Director of Evangelism at StreamSets]
> Andy Rivenes [@TheInMemoryGuy: Product Manager at Oracle for Database In-Memory]

AGENDA:
* 5:45 p.m. -- Dinner, drinks & networking
* 6:10 p.m. -- Talk 1 (Pat): "Ingesting Streaming Data for Analysis"
* 7:05 p.m. -- Talk 2 (Andy): “Oracle Database In-Memory – Columnar Formatted Data for Analytics”
* 7: 50 p.m. -- Raffle drawings and closing remarks
{Register here for the raffle: http://bit.ly/April30IMCmeetup }
>>1st Prize: Lenovo Chromebook!
>>2nd Prize: SwissGear Wenger Ibex Laptop Backpack!
>>3rd Prize: Vintage-style "Suitcase Record Player" with 3-Speed Turntable!
* 8:00 p.m. Finis!
****
TALK DETAILS
>> Talk 1 (Pat): 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.
***
See you April 30! Please RSVP because space will be limited!

Tatiana Staffaroni

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2 Talks! Analyzing streaming data + Columnar Formatted Data for Analytics

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Bay Area In-Memory Computing Meetup

2 Talks! Analyzing streaming data + Columnar Formatted Data for Analytics

Bay Area In-Memory Computing Meetup

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