Name: Drill down into Apache Drill! Plus, Pinsight Media+ Hadoop and Hive use case!
Start: 2014-10-23T17:45:00-05:00
End: 2014-10-23T20:00:00-05:00
Location: Penton Inc

Wow! We have a great lineup for our next meetup! Very exciting!

Jim Bates, Senior Systems Engineer from MapR will talk to us about Apache Drill and it's capabilities including a demo!

Piero Ferrante from Sprint Pingsight Media+ leads the Kansas City Data Science meetup and will talk to us about how they leverage Hadoop and Hive at Pinsight Media+

Jim's talk abstract:

In today's world, you need to be able to keep up with complex, rapidly evolving data, without having to wait for traditional RDBMS data management processes before you can start exploring new data and developing new insights. You also need to be able to use familiar tools to develop those insights, including the SQL query language and the tremendous toolsets built on top of it.

Apache Drill enables analysts and data scientists to perform fast SQL queries on Big Data, drawing both on traditional DBA-managed tables, as well as data in files and NoSQL databases whose structures may be complex and rapidly evolving.

In this presentation we'll describe how Drill meets this challenge, we'll demonstrate Drill in action on a live Hadoop cluster, and we'll show how you can easily get started using Drill even on a single machine without Hadoop.

Piero's focus: 1) Overview of Hadoop' at Pinsight Media+

2)The types of data sources are and how they are ingested into Hadoop

3) The different Hive queries run and lessons learnt around when to use Hive and when not to

Raj Nair

Kansas City Data Engineering at Scale

Technology

Cloud Computing

Hadoop

Big Data

Data Analytics

Predictive Analytics

Data Mining

MapReduce

Machine Learning

Data Visualization

NoSQL

Apache Hbase

Apache Spark

Apache Storm

Drill down into Apache Drill! Plus, Pinsight Media+  Hadoop and Hive use case!

Penton Inc

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Kansas City Data Engineering at Scale

Drill down into Apache Drill! Plus, Pinsight Media+ Hadoop and Hive use case!

Kansas City Data Engineering at Scale

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