Category: DATA PROCESSING

Big data technologies nowadays are very mature. Typically you use HDFS, or another distributed file systems, like S3, for storing data, Spark as a processor engine, and YARN as a resource manager. Next steps, wich you probably would like to achieve, are implement CI/CD (continuous integration and delivery) and move workload on demand in cloud.
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Remote submit is a powerful feature of Apache Spark. Why it is needed? For example, you can experiment with different versions of Spark, independent of what you have in the cluster. Or if you have no direct access to cluster you can start your spark jobs remotely.
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Tuning spark parameters is not a trivial task. In this short post I will explain how to tune some of the important parameters.
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You can buy this book from amazon.de.
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You can buy this book from amazon.de.
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I think, an idea to stream data back from HDFS into some streaming component, like Kafka, is coming from concept of Enterprise Service Bus (ESB). But after some thoughts, I have come to conclusion, that this concept is not useful in Big Data world.
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Microservices approach gains recently popularity. Some time ago service oriented architecture (SOA) approach was very popular. But what is the difference?

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We have discussed before the format for clean and derived data in data lakes. One of the popular formats for this goal is an avro format. We will talk here why it is needed and how to achieve backward and forward compatibility by designing avro schemas.
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If you would like to turn on basic authentication for mondrian cubes from excel you need to implement steps below.
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