Understand current approaches to designing data warehouses and using them in heterogeneous enterprise information systems.

This training provides an overview of modern methods for data storage, including key-value stores, document-oriented and database management systems, distributed data storage and processing systems.

This training focuses on the key concepts and methods for data processing applications development using Apache Hadoop.

This training is aimed at developers and covers the full stack of technical features, architecture and performance tuning. Apache Hive supports analysis of large datasets stored in Hadoop's HDFS and compatible file systems and it provides an SQL-like language with schema on read and transparently converts queries to map/reduce.

We’ll look at the RDD-based framework for automated distributed code execution, and companion projects in different paradigms: Spark SQL, Spark Streaming, MLLib, Spark ML, GraphX.

The Advanced Spark for Developers Course will help trainees get a proper understanding of the internal structure and functioning of Apache Spark – Spark Core (RDD), Spark SQL and Spark Streaming.

A basic practical training in machine learning that covers the entire cycle of building a solution – from initial data capture (“.xlsx file”), through building a model, to explaining data and outcomes specifics to the end customer.

An intro training on Apache Kafka, the open-source distributed event streaming platform. We’ll look at the architectural features of Kafka that enable high-performance data delivery.

Oriented towards python programmers or ML practitioners who want to understand the RL framework in detail.