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Review: Kinetica analyzes billions of rows in real time
GPU database is not only hugely scalable, but integrates graph analysis, location intelligence, and machine learning with standard SQL
Pub/sub messaging: Apache Kafka vs. Apache Pulsar
Apache Kafka set the bar for large-scale distributed messaging, but Apache Pulsar has some neat tricks of its own
TigerGraph review: A graph database designed for deep analytics
Highly parallelized and horizontally scalable, TigerGraph shines for use cases that require multi-hop analytic queries
SQL tutorial: Learn SQL on MariaDB
Follow this step-by-step guide to install MariaDB, load data, connect to a database, and execute SQL queries including table joins
AWS piles on the machine learning services
Hosted TensorFlow, pay-as-you-go inference serving, reinforcement learning, and automatic data labeling come to the Amazon cloud
Machine learning lessons: 5 companies share their mistakes
Machine learning can also go terribly wrong, making you regret that enthusiastic rush to adopt
Tutorial: Spark application architecture and clusters
Learn how Spark components work together and how Spark applications run on standalone and YARN clusters
How to use Knime for data science
Free, open-source Knime allows you to visually assemble data processing “nodes” into machine learning, deep learning, and other analytics workflows
7 cloud services to ease machine learning
Build, train, tune, and deploy machine learning and deep learning models in these end-to-end machine learning clouds
Built for realtime: Big data messaging with Apache Kafka, Part 1
Apache Kafka scales horizontally and offers much higher throughput than some traditional messaging systems. Get started with installation, then build your first Kafka messaging system
Real-time data processing with data streaming: new tools for a new era
Real-time data streaming is still early in its adoption, but over the next few years organizations with successful rollouts will gain a competitive advantage
Machine learning: How to create a recommendation engine
In this excerpt from the book “Pragmatic AI,” learn how to code recommendation engines based on machine learning in AWS, Azure, and Google Cloud
Why there are no shortcuts to machine learning
As long as companies understand that good data science takes time in an enterprise, and give these people room to learn and grow, they won’t need shortcuts
Julia tutorial: Get started with the Julia language
Want the convenience of a dynamic language and the performance of a compiled statically typed language? Try Julia
3 big data platforms look beyond Hadoop
Learn how the Cloudera, Hortonworks, and MapR data platforms are evolving to meet the demands for real-time analytics and machine learning
What’s new in Apache Spark? Low-latency streaming and Kubernetes
Continuous processing and native Kubernetes support in Apache Spark 2.3 spell the end for micro-batching and Hadoop
How to choose the right NoSQL database
NoSQL databases vary in architecture and function, so you need to pick the type that is best for the desired task
Cython tutorial: How to speed up Python
How to use Cython and its Python-to-C compiler to give your Python applications a rocket boost
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