I’m excited to introduce the Big Data track for Couchbase Connect 2015.

Interested in learning about Spark, Kafka, Hadoop, and NoSQL? You’ve come to the right place. We’re going to spend two days discussing integration, architecture, and solutions – and we’re doing it at Levi’s Stadium in June.

You’ll get to hear from the technical staff at LinkedIn and PayPal. They’re not just implementing big data solutions, they’re creating the blueprints.

In addition, you’ll get to hear from the NoSQL experts at Couchbase, the Hadoop experts at Hortonworks, the Kafka experts at Confluent, and more!

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The Role of NoSQL in a Hadoop
Presented by: Couchbase

NoSQL or Hadoop? No. It’s NoSQL and Hadoop. Understand the differences between NoSQL and Hadoop, their strengths and weaknesses, and how they compliment each other. Let there be no more confusion.

Introduction to the Hadoop Ecosystem
Presented by: Hortonworks

There’s a lot more to Hadoop than HDFS and MapReduce. Learn about the components of Hadoop and its ecosystem. Who better to present than Hortonworks? After all, Hortonworks Data Platform (HDP) includes all of these components.

Blueprints for Big Data Architectures
Presented by: Couchbase

NoSQL and Hadoop compliment each other. Now, discover how and why they’re integrated in real world. These are the blueprints creating by those with big data solutions like Aol Advertising, LinkedIn, LivePerson, and PayPal.

Import/Export Data to Hadoop with Apache Sqoop
Presented by: Couchbase

Learn how to create a big data refinery – collect data in Couchbase Server, export it to Hadoop for refinement, and import the refined into Couchbase Server. It may be old school, but it’s effective.

Streaming Data with Apache Kafka
Presented by: Confluent

Did you know that you can can stream data from Couchbase Server to Kafka as it’s written – inserted, updated, or deleted? The database doesn’t have to be the last stop on the train. It can be the first. The data can continue on. Might as well hear from the team that created Kafka itself.

Analyzing Data with Apache Spark
Presented by: TBD

DAG is in, MapReduce is out. Learn how analyze data in Couchbase Server with Spark and, thanks to SQL for Documents, Spark SQL. Got memory? Analyze lots of data collected in Couchbase Server. Then, write the results to Couchbase Server. Finally, view the results.

Stream Processing with Apache Spark and Apache Storm
Presented by: Couchbase

There’s a place for offline processing. Everywhere else, there’s real-time. Stream processing not only enables real-time analytics, it create data flows. Learn how Couchbase Server can provide data for or consume data from stream processors.

Ingesting and Processing Data with Kafka and Hadoop
Presented by: LinkedIn

Did we mention Kafka and Hadoop? Learn from the company that created its own Kafka to HDFS pipeline (Camus). Enough said.

Creating a Central Data Backbone: Couchbase to Kafka to Hadoop and Back
Presented by: PayPal

Kafka and Hadoop, again? That’s right. Learn how PayPal migrates, analyzes, and monitors cookie data for millions of visitors in real time.

The Yearly Special

Rise of the Machines: Skynet’s Big Data Requirements

Presented by: Me (Arnold was busy)

This is not your father’s Internet. It’s not all status updates, tweets, upvotes, and comments. There’s a few billion people connect to the Internet. However, there’s 20 billion machines connected it – right now. They generate a lot data, and they type faster than us. They consume it too. And, as we saw in Terminator Salvation, network access can be disrupted. What then?

Register for Couchbase Connect 2015


Posted by Shane Johnson, Director, Product Marketing, Couchbase

Shane K Johnson was the Director of Product Marketing at Couchbase. Prior to Couchbase, he occupied various roles in developing and evangelism with a background in Java and distributed systems. He has consulted with organizations in the financial, retail, telecommunications, and media industries to draft and implement architectures that relied on distributed systems for data and analysis.

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