Blog about big data processing and data-driven investments
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 moreThe structural tracking error minimization (STEM) approach produces stable tracking portfolios out-of-sample in the crucial investment period. Full version:
Quantitative Finance
It looks like, that separation between two infrastructure layers is increasing.
Read more
Main purpose of using microservices architecture is to increase velocity of development and reduce system complexity.
Read more
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.
Read more
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.
Read more
Tuning spark parameters is not a trivial task. In this short post I will explain how to tune some of the important parameters.
Read more