Sparkier, faster, more: Graph databases, and Neo4j, are moving on
New players, new features, platforms, and ticking boxes. Let's talk graph with Neo4j.
Read More →Towards a unifying data theory and practice: Combining operations, analytics, and streaming
We've heard the one data platform to rule them all story before. Could it be this time it's actually true? New Pivotal-backed and Spark-compatible open source solution SnappyData promises so, and we take the opportunity to look inside and around for whys, hows, and options.
Read More →In-memory architecture + streaming data = Digital Twins?
The Digital Twin is a buzzword in its prime time infancy. We take a look at what it is and how it relates to modern data architectures, examining ScaleOut's adoption and implementation and comparing with other alternatives.
Read More →Kafka gets SQL with KSQL
Apache Kafka is a key component in data pipeline architectures when it comes to ingesting data. Confluent, the commercial entity behind Kafka, wants to leverage this position to become a platform for the enterprise and today is announcing a milestone on the road to ubiquity: SQL.
Read More →Kafka: The story so far
Hard problems at scale, the future of application development, and building an open source business. If any of that is of interest, or if you want to know about Kafka, real-time data, and streaming APIs in the cloud and beyond, Jay Kreps has some thoughts to share.
Read More →Streaming hot: Real-time big data architecture matters
What is streaming in big data processing, why you should care, and what are your options to make this work for you?
Read More →Big Data, Crystal Balls and Looking Glasses: Reviewing 2016, predicting 2017
End-of-year reviews are boring — and everyone does them. Predictions are boring — and they are hard. Of course, this is different — because big data.
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