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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Machine Learning Applied to Microservices

Posted in Analytics, Big Data, Business Intelligence, Cloud, Docker, Hadoop, Microservices, Middleware on October 20th, 2016 by Kai Wähner

I had two sessions at O’Reilly Software Architecture Conference in London in October 2016. It is the first #OReillySACon in London. A very good organized conference with plenty of great speakers and sessions. I can really recommend this conference and its siblings in other cities such as San Francisco or New York if you want to learn about good software architectures and new concepts, best practices and technologies. Some of the hot topics this year besides microservices are DevOps, serverless architectures and big data analytics respectively machine learning.

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Characteristics of a Good Visual Analytics and Data Discovery Tool

Posted in Analytics, Big Data, Business Intelligence, Hadoop on July 28th, 2016 by Kai Wähner

Visual Analytics and Data Discovery allow analysis of big data sets to find insights and valuable information. This is much more than just classical Business Intelligence (BI). See this article for more details and motivation: “Using Visual Analytics to Make Better Decisions: the Death Pill Example“. Let’s take a look at important characteristics to choose the right tool for your use cases.

Visual Analytics Tool Comparison and Evaluation

Several tools are available on the market for Visual Analytics and Data Discovery. Three of the most well known options are Tableau, Qlik and TIBCO Spotfire. Use the following list to compare and evaluate different tools to make the right decision for your project:

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Streaming Analytics with Analytic Models (R, Spark MLlib, H20, PMML)

Posted in Analytics, Big Data, Business Intelligence, Hadoop, In Memory, NoSQL on March 3rd, 2016 by Kai Wähner

In March 2016, I had a talk at Voxxed Zurich about “How to Apply Machine Learning and Big Data Analytics to Real Time Processing”.

Kai_Waehner_at_Voxxed_Zurich

Finding Insights with R, H20, Apache Spark MLlib, PMML and TIBCO Spotfire

Big Data” is currently a big hype. Large amounts of historical data are stored in Hadoop or other platforms. Business Intelligence tools and statistical computing are used to draw new knowledge and to find patterns from this data, for example for promotions, cross-selling or fraud detection. The key challenge is how these findings can be integrated from historical data into new transactions in real time to make customers happy, increase revenue or prevent fraud.

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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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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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Fundamentals of Stream Processing (IBM InfoSphere Streams, TIBCO StreamBase, Apache Storm) – Book Review

Posted in Analytics, Big Data, Hadoop on July 1st, 2014 by Kai Wähner

Internet of things, cloud and mobile are the major drivers for stream processing. Use cases are network monitoring, intelligent surveillance, but also less technical things such as inventory management or fraud detection. The book helps a lot to get a basic understanding about history, concepts and patterns of the stream processing paradigm.

“Fundamentals of Stream Processing: Application Design, Systems, and Analytics” (www.amazon.com/Fundamentals-Stream-Processing-Application-Analytics/dp/1107015545) is one of only few books available about stream processing. Published in 2014 by Cambridge University Press. Authors are Henrique C. M. Andrade (JP Morgan, New York), Bugra Gedik (Bilkent University, Turkey), Deepak S. Turaga (IBM Thomas J. Watson Research Center, New York).

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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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