- Top KDnuggets tweets, Jul 05-11: 10 Free Must-Read Books for #MachineLearning and #DataScience; Why AI and Machine Learning? - Jul 12, 2017.
Also great overview: Unintuitive properties of #NeuralNetworks; #Apache #Flink vs #Spark: The Strange Loop in #DeepLearning - the coolest idea in #MachineLearning in 20 yrs;
- Apache Flink: The Next Distributed Data Processing Revolution? - Jul 5, 2017.
Will Apache Flink displace Apache Spark as the new champion of Big Data Processing? We compare Spark and Apache Flink performance for batch processing and stream processing.
- KDnuggets™ News 16:n35, Oct 5: Biggest Issues in Data Science; Data Science for IoT: 10 differences - Oct 5, 2016.
Biggest Issues in Data Science; Data Mining vs. Statistics; Data Science for Internet of Things (IoT): 10 Differences; Active Big Data, Data Science Leaders on LinkedIn.
- Beginner’s Guide to Apache Flink – 12 Key Terms, Explained - Oct 4, 2016.
We review 12 core Apache Flink concepts, to better understand what it does and how it works, including streaming engine terminology.
Pages: 1 2
- XGBoost: Implementing the Winningest Kaggle Algorithm in Spark and Flink - Mar 24, 2016.
An overview of XGBoost4J, a JVM-based implementation of XGBoost, one of the most successful recent machine learning algorithms in Kaggle competitions, with distributed support for Spark and Flink.
- Top Big Data Processing Frameworks - Mar 3, 2016.
A discussion of 5 Big Data processing frameworks: Hadoop, Spark, Flink, Storm, and Samza. An overview of each is given and comparative insights are provided, along with links to external resources on particular related topics.
- Getting started with Python and Apache Flink - Nov 13, 2015.
Apache Flink built on top of the distributed streaming dataflow architecture, which helps to crunch massive velocity and volume data sets. With version 1.0 it provided python API, learn how to write a simple Flink application in python.
- Fast Big Data: Apache Flink vs Apache Spark for Streaming Data - Nov 10, 2015.
Real-time stream processing has been gaining momentum in recent past, and major tools which are enabling it are Apache Spark and Apache Flink. Learn with the help of a case study about Data processing, Data Flow, Data management using these tools.
Pages: 1 2
- Top KDnuggets tweets, Aug 04-10: Survival analysis in R – step by step guide - Aug 11, 2015.
Survival analysis in R - step by step guide; Neural Nets, AI and Deep Learning journey to acceptance; Data is Ugly - Tales of Data Cleaning; Apache Flink and the case for #stream processing #BigData #Analytics.
- Apache Flink and the case for stream processing - Aug 7, 2015.
Realtime analytics have been proven challenging in the past, but with new tools it will be possible to setup your pipelines in relative short time. Apache Flink is one of such framework, find out how you can exploit it for your demands.
- Interview: Stefan Groschupf, Datameer on Balancing Accuracy and Simplicity in Analytics - Aug 4, 2015.
We discuss common pain points in Big Data projects, evolution of Datameer technology, department specific solution – Datameer Professional, Datameer 5.0 Smart Execution, tacking over-simplicity and more.
- Exclusive Interview: Matei Zaharia, creator of Apache Spark, on Spark, Hadoop, Flink, and Big Data in 2020 - May 22, 2015.
Apache Spark is one the hottest Big Data technologies in 2015. KDnuggets talks to Matei Zaharia, creator of Apache Spark, about key things to know about it, why it is not a replacement for Hadoop, how it is better than Flink, and vision for Big Data in 2020.