Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.
Comparison: Data Preparation vs. Inline Data Wrangling in Machine Learning and Deep Learning ProjectsPosted 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.
In November 2016, I am at Big Data Spain in Madrid for the first time. A great conference with many awesome speakers and sessions about very hot topics such as Apache Hadoop, Spark Spark, Streaming Processing / Streaming Analytics and Machine Learning. If you are interested in big data, then this conference is for you! My two talks:
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.
Comparison Of Log Analytics for Distributed Microservices – Open Source Frameworks, SaaS and Enterprise ProductsPosted in Analytics, Big Data, Business Intelligence, Cloud, Hadoop, Microservices, SOA 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.
I want to share the slide of my session about comparing open source frameworks, SaaS and Enterprise products regarding log analytics for distributed microservices:
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:
In March 2016, I had a talk at Voxxed Zurich about “How to Apply Machine Learning and Big Data Analytics to Real Time Processing”.
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.
In February 2016, I presented a brand new talk at OOP in Munich: “Comparison of Frameworks and Tools for Big Data Log Analytics and IT Operations Analytics”. The focus of the talk is to discuss different open source frameworks, SaaS cloud offerings and enterprise products for analyzing big masses of distributed log events. This topic is getting much more traction these days with the emerging architecture concept of Microservices.
- Log Analytics enables IT Operations Analytics for Machine Data
- Correlation of Events is the Key for Added Business Value
- Log Management is complementary to other Big Data Components
I was invited to speak at Microservices Meetup Dublin this week. I updated my slide deck “Microservices – Death of the ESB?” … The meetup was fully booked with a waiting list; around 120 attendees came to Gild‘s office. (see attached link).
If you have not seen the slide deck last year, you should definitely take a look at this updated version with more recent information. I also incorporated valuable information from discussions with attendees in 2015’s sessions about this topic.
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.