This post shares a slide deck and video recording of the differences between an event-driven streaming platform like Apache Kafka and middleware like Message Queues (MQ), Extract-Transform-Load (ETL) and Enterprise Service Bus (ESB).
Closed Big Data Loop: 1) Finding Insights with R, H20, Apache Spark MLlib, PMML and TIBCO Spotfire. 2) Putting Analytic Models into Action via Event Processing and Streaming Analytics.
See how stream processing / streaming analytics frameworks (e.g. Apache Spark, Apache Flink, Amazon Kinesis) and products (e.g. TIBCO StreamBase, Software AG’s Apama, IBM InfoSphere Streams) are categorized and compared. Besides, understand how stream processing is related to Big Data platforms such as Apache Hadoop and machine learning (e.g. R, SAS, MATLAB).
Data Warehouses have existed for many years in almost every company. While they are still as good and relevant for the same use cases as they were 20 years ago, they cannot solve new, existing challenges and those sure to come in a ever-changing digital world. The upcoming sections will clarify when to still use a Data Warehouse and when to use a modern Live Datamart instead.
The following slide deck shows plenty of different technologies (e.g. REST, WebSockets), frameworks (e.g. Apache CXF, Apache Camel, Puppet, Docker) or tools (e.g. TIBCO BusinessWorks, API Exchange) to realize Microservices.
Apache Hadoop is getting more and more relevant. Not just for big data processing (e.g. MapReduce), but also in fast data processing (e.g. stream processing). Recently, I published two blog posts on the TIBCO blog to show how you can leverage TIBCO BusinessWorks 6 and TIBCO StreamBase to realize big data and fast data Hadoop use cases.
Challenges, requirements and best practices for creating a good Microservicess architecture, and what role an Enterprise Service Bus (ESB) plays in this game.
An intelligent business process (iBPM, iBPMS) combines big data, analytics and business process management (BPM) – including case management! This post implements a use case using big data / fast data analytics with TIBCO ActiveMatrix BPM, BusinessWorks, StreamBase, Spotfire and Tibbr.
Use Cases and Success Stories for In-Memory Data Grids, e.g. TIBCO ActiveSpaces, Oracle Coherence, Infinispan, IBM WebSphere eXtreme Scale, Hazelcast, Gigaspaces, GridGain, Pivotal Gemfire (Presentation by Kai Wähner at NoSQL Matters 2014 in Barcelona) – NOT SAP HANA 🙂