A lot of players on the market have built successful MapReduce workflows to daily process terabytes of historical data. But who wants to wait 24h to get updated analytics?
With this post, we start a series that will provide a guide on building a fault-tolerant, scalable, microservice-based solution with Apache Ignite In-Memory Data Fabric.
This article covers how to prevent big data failures in predictive analytics by diving into strategies for proper implementation, common mistakes, proper big data structuring, and more.
In this post, we will dive into the consumer side of this application ecosystem, which means looking closely at Kafka consumer group monitoring. Read on to find out more.
We argue that recommendations and search are two sides of the same coin. Both rank content for a user based on “relevance.” The only difference is whether a keyword query is provided. Read this article to find out more.
If you run a Swarm Mode cluster or even one Docker engine, you've likely wondered how to keep track of all that's happening. Read for tips on monitoring your containers.
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