Comparison: Data Preparation vs. Inline Data Wrangling in Machine Learning and Deep Learning Projects

Posted in Analytics, Big Data, Business Intelligence, Hadoop on February 13th, 2017 by Kai Wähner

I want to highlight a new presentation about Data Preparation in Data Science projects:

“Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling  in Machine Learning / Deep Learning Projects”

Data Preparation as Key for Success in Data Science Projects

A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project.

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Difference between a Data Warehouse and a Live Datamart?

Posted in Analytics, Big Data, Business Intelligence, In Memory on October 9th, 2015 by Kai Wähner

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.

What is a Data Warehouse (DWH)?

A Data Warehouse is a central repository of integrated data from more disparate sources. It stores historical data to create analytical reports for knowledge workers throughout the enterprise. A DWH includes a server, which stores the historical data and a client for analysis and reporting.

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Intelligent BPM Suite (iBPMS): Implementation of a CRM Use Case

Posted in Analytics, Big Data, BPM, Business Intelligence, Cloud, ESB, In Memory, Social Network on December 3rd, 2014 by admin

Today, humans have to interpret large sets of different data to make a decision. Using gut feeling is nothing but gambling. Therefore, big data analytics is getting more and more important every year to make better decisions. However, just doing big data analytics is not enough. In many use cases, systematic and monitored human interactions are as important to get best outcomes.

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Comparison of Stream Processing and Streaming Analytics Alternatives (Apache Storm, Spark, IBM InfoSphere Streams, TIBCO StreamBase, Software AG Apama)

Posted in Analytics, Big Data, Business Intelligence, Hadoop on September 10th, 2014 by Kai Wähner

The demand for stream processing is increasing a lot these days. Frameworks (Apache Storm, Spark) and products (e.g. IBM InfoSphere Streams, TIBCO StreamBase, Software AG Apama) for stream processing and streaming analytics are getting a lot of attention these days. The reason is that often processing big volumes of data is not enough. Data has to be processed fast, so that a firm can react to changing business conditions in real time. This is required for trading, fraud detection, system monitoring, and many other examples. A “too late architecture” cannot realize these use cases.

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“Hadoop and Data Warehouse (DWH) – Friends, Enemies or Profiteers? What about Real Time?” – Slides (including TIBCO Examples) from JAX 2014 Online

Posted in Analytics, Big Data, Business Intelligence, Cloud, ESB, Hadoop on May 13th, 2014 by Kai Wähner

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.

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