Analytics
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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
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?
Review: Elasticsearch 7 soars with SQL, search optimizations
Across-the-board upgrade beefs up query capabilities, boosts cluster performance, and simplifies cluster configuration
Is Java the next COBOL?
60 years later, billions of lines of COBOL live on. Could Java, SQL, or Python last as long?
Deep learning frameworks: PyTorch vs. TensorFlow
If you actually need a deep learning model, PyTorch and TensorFlow are both good choices
10 Splunk alternatives for log analysis
Splunk may be the most famous way to make sense of mass quantities of log data, but it is far from the only player around
The best graph databases
These stellar databases combine horizontal scalability with highly efficient engines for storing and analyzing connected data
The best distributed NoSQL databases
Highly flexible and hugely scalable, NoSQL databases offer a range of data models and consistency options to suit your application
5 machine learning tools to ease software development
AI-driven development tools that provide code auto-completion, code vulnerability detection, and even cutting-edge code generation
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
10 new tricks your old database can do
You might be surprised by the powerful “NoSQL” features lurking in your old Oracle, SQL Server, MySQL, or PostgreSQL database
Hadoop runs out of gas
As big data customers flee complexity and embrace the cloud, Hadoop vendors are sputtering
Machine learning algorithms explained
Machine learning uses algorithms to turn a data set into a predictive model. Which algorithm works best depends on the problem
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
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