Martin Heller
Contributing Editor
Martin Heller is a contributing editor and reviewer for InfoWorld. Formerly a web and Windows programming consultant, he developed databases, software, and websites from his office in Andover, Massachusetts, from 1986 to 2010. More recently, he has served as VP of technology and education at Alpha Software and chairman and CEO at Tubifi. Disclosure: He also writes for Hewlett-Packard's TechBeacon marketing website.
How to choose the right database for your application
From performance to programmability, the right database makes all the difference. Here are 12 key questions to guide your selection
What is SQL? The first language of data analysis
SQL is neither the fastest nor the most elegant way to talk to databases, but it is the best way we have. Here’s why
Qubole review: Self-service big data analytics
Cloud-native data platform puts Spark, Presto, Hive, and Airflow at your fingertips, while controlling your cloud spending
Rockset review: Real-time SQL for operational data
One-of-a-kind database for operational analytics analyzes gigabytes to terabytes of recent, real-time, and streaming data in milliseconds
MongoDB vs. MySQL: How to choose
MongoDB and MySQL are the leading open source NoSQL and relational databases, respectively. Which is best for your application?
The best open source software of 2019
InfoWorld recognizes the leading open source projects for software development, cloud computing, data analytics, and machine learning
Visual Studio Code vs. Sublime Text: How to choose
Do you want a maximum-flexible editor with just the right IDE features, or the fastest and most efficient editor you can find? You can have both
Visual Studio Code vs. Visual Studio: How to choose
Deciding between Visual Studio Code and Visual Studio may depend as much on your work style as on the language support and features you need. Here’s how to decide
Snowflake review: A data warehouse made better in the cloud
A fast, no-fuss data warehouse as a service, Snowflake scales dynamically to give you the performance you need exactly when you need it
Semi-supervised learning explained
Using a machine learning model’s own predictions on unlabeled data to add to the labeled data set sometimes improves accuracy, but not always
PyTorch vs. TensorFlow: How to choose
If you actually need a deep learning model, PyTorch and TensorFlow are the two leading options
Automated machine learning or AutoML explained
AutoML frameworks and services eliminate the need for skilled data scientists to build machine learning and deep learning models
Couchbase review: A smart NoSQL database
Flexible, distributed document database offers an easy query language, mobile synch, independently scalable services, and strong consistency within a cluster
The best NoSQL databases
Highly flexible and hugely scalable, NoSQL databases offer a range of data models and consistency options to suit your application
Unsupervised learning explained
Unsupervised learning is used mainly to discover patterns and detect outliers in data today, but could lead to general-purpose AI tomorrow
The best graph databases
These stellar databases combine horizontal scalability with highly efficient engines for storing and analyzing connected data
The best machine learning and deep learning libraries
TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models
The best distributed relational databases
These SQL relational databases offer both horizontal scalability and support for ACID transactions—some on a global scale
TensorFlow 2 review: Easier machine learning
Now more platform than toolkit, TensorFlow has made strides in everything from ease of use to distributed training and deployment
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