There is a pattern in microservices architecture: Command and Query Responsibility Segregation (CQRS). This pattern helps to design multi-purpose data lake.Read more
Maintaining data description is useful feature. There are some ideas, how to implement this.Read more
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 more
Main purpose of using microservices architecture is to increase velocity of development and reduce system complexity.
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.
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.
You may think, that there is no need to structure data in HDFS. You can systemize it in the future. But I think this is a wrong way. We should always keep in mind: there is no free lunch. Therefore it is better to make desicions at the beginning.