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Kai Wähner (Twitter: @KaiWaehner, Blog: www.kai-waehner.de/blog) is an IT-Consultant in the Java EE, SOA, Cloud Computing and Big Data world. In his real life, he lives in Erlangen, Germany and works for TIBCO (www.tibco.com). Besides solving huge integration problems for large companies, Kai writes articles for magazines and speaks at international IT conferences such as JavaOne. Feel free to contact him via Twitter, LinkedIn or Email. Kai is a DZone MVB and is not an employee of DZone and has posted 51 posts at DZone. You can read more from them at their website. View Full User Profile

Hadoop, Big Data and Data Warehouse: Friends, Enemies or Profiteers?

05.14.2014
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Apache Hadoop, Big Data and Data Warehouse (DWH) – Friends, Enemies or Profiteers? What about Real Time?

Slides from my talk “Hadoop and Data Warehouse (DWH) – Friends, Enemies or Profiteers? What about Real Time?” at JAX 2014 (Twitter #jaxcon) in Mainz are online. JAX is a great conference with interesting topics and many good speakers!

Content (Data Warehouse, Business Intelligence, Hadoop, Stream Processing)

Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, velocity and variety of corporate data. New business models based on predictive analytics, such as recommendation systems or fraud detection, are relevant more than ever before. Apache Hadoop seems to become the de facto standard for implementing big data solutions. For that reason, solutions from many different vendors emerged on top of Hadoop.

But hold on… Companies have spent a lot of many to implement a data warehouse for the same reason in the last decades. Both, Apache Hadoop and data warehouse were invented to store and analyze big data. This session explains the different architectural and technical concepts of Apache Hadoop and a data warehouse. The following questions will be answered: When to use which alternative? Does a data warehouse even have a future at all? Or how can we combine both alternatives?

However, Hadoop and a Data Warehouse cannot solve every big data problem. Complex event processing and real-time analytics have to be solved in another way. So, in-memory computing and streaming platforms are good alternatives or complements to Hadoop for processing and analyzing big data. For that reasons, an almost unimaginable number of solutions for big data emerged on the market. This session shows and compares the most important concepts and solutions for processing and analyzing big data, and discusses how they complement each other.

TIBCO Products (Spotfire, StreamBase, BusinessEvents, BusinessWorks) and Real World Examples

I discuss a good big data architecture which includes Data Warehouse / Business Intelligence + Apache Hadoop + Real Time / Stream Processing. Several real world example are shown. TIBCO offers some very nice products for realizing these use cases, e.g. Spotfire (Business Intelligence / BI), StreamBase (Stream Processing), BusinessEvents (Complex Event Processing / CEP) and BusinessWorks (Integration / ESB). TIBCO is also ready for Hadoop by offering connectors and plugins for many important Hadoop frameworks / interfaces such as HDFS, Pig, Hive, Impala, Apache Flume and more.

Slides

Here are the slides:

As always, I appreciate feedback and discussions.

Kai Wähner


[Content from my blog: http://www.kai-waehner.de/blog/2014/05/13/hadoop-and-data-warehouse-dwh-friends-enemies-or-profiteers-what-about-real-time-slides-including-tibco-examples-from-jax-2014-online/ ]

Published at DZone with permission of Kai Wähner, author and DZone MVB. (source)

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