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Read moreCategory: DATA PROCESSING
We will discuss here solutions for data processing and distributed computing, like HDFS, spark, and kubernetes.
Authentication and authorization in Hadoop cluster
Here we explain concepts behind activation of security in Hadoop cluster.
Read moreMeta data service and schema registry in data lake
Maintaining data description is useful feature. There are some ideas, how to implement this.
Read moreORM (object-relational mapping) analog for data in data lake
We start saving data in HDFS using avro format. In previous post we have discussed about forward and backward compatibility of avro schemas. How to use this concept?
Read moreRunning Apache Zeppelin in K8s cluster and integration with YARN cluster
Useful way for implementing CI/CD pipeline is to pack code as docker and run in K8s cluster. One very practical application for data analytics is notebook based tool Apache Zeppelin. Every business department requires own configuration for zeppelin. Hence, there is an idea to create docker containers for every department and run in k8s cluster.
Read moreBook notes – Microservice patterns: with examples in java
Two infrastructure layers for distributed systems
It looks like, that separation between two infrastructure layers is increasing.
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Why contracts are important in data intensive applications with microservices
Main purpose of using microservices architecture is to increase velocity of development and reduce system complexity.
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Book notes – Kubernetes: Up and Running: Dive into the Future of Infrastructure
Hybrid cloud architecture for data lake applications
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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