Course topics: Hadoop architecture, Data ingestion into Big Data systems and ETL, Distributed processing MapReduce Framework, Apache Hive, HBase, Hadoop Application Testing, Spark Core processing RDD, Spark SQL, SparkMLlib modeling Big Data with Spark, Stream Processing, Frameworks and Spark Streaming, Improving Spark Performance, Data Processing with PySpark

Course webpage

Course topics: Relational Model, Distributed transactions, Scale cube, Relational Model Versus Document Model, Data modeling, sharding, replication, consistency guarantees, Batch and Stream Processing

Course webpage

Course topics: Network Communication and Remote Procedure Calls, Failures, Concurrency & Timing, Replication, Consensus & Consistency, Availability & Consensus Algorithms, General Recommendations for Building Distributed Systems.

Course webpage.

A Data Warehouse is a foundation and core component of any data analytics, business intelligence solution. Its building and design significantly differ from the classic transactional OLTP database and requires a specific set of skills.

So the goal of this course is first to give a birds-eye view of the different Data warehouse architectures and high-level approaches and then go into the depth of how to design and model the target storage according to the industry best practices as well as how to bring the data from the disparate source into it in a most efficient and reliable way.

Course webpage.

In the world of data, distributed system, and real-time communication, it is common to hear “stream of data”. But do you know how to work with data streams? How to process data within a stream? Doing that fast and, of course, keep your code readable? If not, that the course is for you!

In the course, we will learn a functional approach for data stream processing. We will start from the basics of functional programming and end with applying FP for building real data pipelines.

Course webpage.