graph computation model articles

Articles tagged with graph computation model. There are 4 article(s) corresponding to the tag graph computation model. If you don't find what you're looking for, please check related tags: Apache Spark 2.4.0 features, Apache Spark data sources, AWS EC2, Big Data patterns implemented, Cerberus + PySpark, Change Data Capture, completable future, custom state store, custom state store, data patterns.

Check out my new course on Data Engineering!

Are you a data scientist who wants to extend his data engineering skills? Or a software engineer who wants to work with Big Data? If not, maybe a BI developer who wants to evolve to engineering position? My course will help you to achieve your goal! Join the class →

Chaos in streaming graph processing

Some time ago I wrote a post about the graph data processing with streams. That article was based on X-Stream framework proposed by the searchers of EPFL research institute. At this occasion, I also mentioned the existence of newer alternative for X-Stream, adapted for distributed workloads, called Chaos. I voluntary omitted the explanation of Chaos in the previous post. Putting it aside of X-Stream would introduce too many new concepts. But now, after some weeks of graph processing discoveries, I would like to return to the successor of X-Stream and present it more in details. Continue Reading →

Streaming and graph processing

Use cases of streaming surprise me more and more. In my recent research about graph processing in Big Data era I found a paper presenting the graph framework working on vertices and edges directly from a stream. In case you've missed that paper I'll try to present this idea to you. Continue Reading →

Vertex-centric graph processing

Graph data processing, even though seems to be less popular than streaming or files processing, is an important member of data-oriented systems. And as its "colleagues", it also has some different processing logics. The first described in this blog is called vertex-centric. Continue Reading →