In die Kategorie Analytics fallen sowohl die klassischen Data-driven-Business / BI-Themen (Data Warehouse, ETL, Reporting, Dashboards) als auch die neueren Trends in diesem Umfeld: Big Data, Data Science & Deep Learning und Search-based Applications.

Wir verstehen uns als Spezialist für anspruchsvolle Aufgaben in den Bereichen Data Management und Analytics, die unter Zeitdruck gelöst werden müssen und für die oftmals in den Unternehmen keine eigenen Fachleute verfügbar sind:

  • die Modellierung hochkomplexer Cubes,
  • die Integration heterogener Datenquellen,
  • der effiziente Umgang mit sehr großen Datenvolumina (Big Data),
  • die wissenschaftliche Analyse dieser Daten-Pools (Data Science) und
  • der Einsatz von innovativen Suchtechnologien im Unternehmenskontext.

Data Science in Production: Packaging, Versioning and Continuous Integration

2018-02-07T14:53:36+00:00

Here's what changes when your data science project grows from a proof of concept. How do you deploy your model, how can updates be rolled out, ...?

A common pattern in most data science projects I participated in is that it’s all fun and games until someone wants to put it into production. From that point in time

Data Science in Production: Packaging, Versioning and Continuous Integration 2018-02-07T14:53:36+00:00

Network Anomaly Detection: Online vs. Offline Machine Learning

2018-09-25T11:06:05+00:00

In this part of our network anomaly detection blogpost series we want to compare two basically different styles of learning.

In this part of our network anomaly detection series we want to compare two basically different styles of learning. The very first post introduced the simple k-means 

Network Anomaly Detection: Online vs. Offline Machine Learning 2018-09-25T11:06:05+00:00

Sport-Tracking mit Elasticsearch [Meetup]

2018-02-07T14:54:40+00:00

In diesem Mittschnitt unseres Meetups zeigt Tracking Fan Wolfgang, wie er die Daten seiner Garmin Watch selbst mit Elasticsearch ausgewertet hat.

In diesem Mittschnitt unseres Meetups in Karlsruhe zeigt Wolfgang, ein begeisterter Triathlet und Tracking Fan, wie er die Daten seiner Garmin Watch selbst mit Elasti

Sport-Tracking mit Elasticsearch [Meetup] 2018-02-07T14:54:40+00:00

Real-time detection of anomalies in computer networks with methods of machine learning: Stop the (data)-thief!

2019-02-15T12:54:16+00:00

This blog post describes some basic concepts and shows a prototypical architecture for network anomaly detection in real-time.

This blog post shows some results and concepts of a master’s thesis here at inovex. It describes some basic concepts and shows a prototypical architecture for detecti

Real-time detection of anomalies in computer networks with methods of machine learning: Stop the (data)-thief! 2019-02-15T12:54:16+00:00

Powering a Data Hub at Otto Group BI with Schedoscope

2017-11-27T15:30:20+00:00

In order to build data services or advanced machine learning models, organizations must integrate large amounts of information from diverse sources.

In order to build data services or advanced machine learning models, organizations must integrate large amounts of information from diverse sources. As a central plac

Powering a Data Hub at Otto Group BI with Schedoscope 2017-11-27T15:30:20+00:00

Causal Inference and Propensity Score Methods

2017-11-27T15:30:21+00:00

In supervised learning, correlation is crucial to predict the target variable with the help of the feature variables. But what good is causation?

In the field of machine learning and particularly in supervised learning, correlation is crucial to predict the target variable with the help of the feature variables

Causal Inference and Propensity Score Methods 2017-11-27T15:30:21+00:00

24/7 Spark Streaming on YARN in Production

2019-01-15T11:05:23+00:00

We have been running Spark Streaming on Apache Hadoop™ YARN in production for close to a year now. This is what we learned.

At a large client in the German food retailing industry, we have been running Spark Streaming on Apache Hadoop™ YARN in production for close to a year now. Overall, S

24/7 Spark Streaming on YARN in Production 2019-01-15T11:05:23+00:00

Hive UDFs and UDAFs with Python

2019-01-23T11:31:13+00:00

In this post we focus on how to write sophisticated User Defined (Aggregated) Functions (UD(A)Fs) for Apache Hive in Python.

Sometimes the analytical power of built-in Hive functions is just not enough. In this case it is possible to write hand-tailored User-Defined Functions (UDFs) for tra

Hive UDFs and UDAFs with Python 2019-01-23T11:31:13+00:00

Elk on Docker (-Compose)

2017-11-27T15:30:26+00:00

This article will show you how to run an ELK on Docker using Docker Compose so you can run it on your Docker infrastructure or test it on your local system.

The ELK/Elastic stack is a common open source solution for collecting and analyzing log data from distributed systems. This article will show you how to run an ELK on

Elk on Docker (-Compose) 2017-11-27T15:30:26+00:00

Death of an ELK?

2017-11-27T15:30:27+00:00

This article will guide you through updating the ELK stack (Elastic Search, Logstash Kibiana) from version 1.x to 2.x.

This article will guide you through updating the ELK stack from version 1.x to 2.x, taking into account the correct order of its components Elasticsearch, Logstash an

Death of an ELK? 2017-11-27T15:30:27+00:00

Cloud Wars: Datenvisualisierung [Teil 5]

2019-01-15T10:35:37+00:00

In diesem Artikel untersuchen wir die Methoden, die Amazon Web Services (AWS), Azure und Google Cloud zur Visualisierung von Daten anbieten.

In diesem Artikel untersuchen wir die Methoden, die AWS, Azure und Google Cloud zur Visualisierung von Daten anbieten. Abschließend ziehen wir ein Gesamtfazit unserer

Cloud Wars: Datenvisualisierung [Teil 5] 2019-01-15T10:35:37+00:00
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