Gain insights into distributed systems, learn architectural patterns like replication and sharding, and explore Apache Spark and Druid to design and develop effective distributed applications.

data_eng.tar.gz

AWS_ACCESS_KEY_ID

AWS_SECRET_ACCESS_KEY

AWS_DEFAULT_REGION

Apache_Druid

Mapreduce

aws_load_balancer

service_sharding

cqrs

apache_spark

apache_kafka

Gone are the days when monolithic applications were the norm. Most applications that we use today are too complex for that type of construction. Instead, most applications that appear as a single unit are actually built as a collection of inter-operating but independent computational components. Developers are expected to have mastery over a programming language or two. Moreover, they should also know the basics of distributed systems in order to succeed.

In this course, you’ll learn what a distributed system is, followed by the challenges unique to distributed systems. You’ll cover popular architectural patterns for achieving targets like replication and sharding. You’ll finish with a study of two popular distributed systems: Apache Spark and Apache Druid.

With the knowledge gained through this course, you’ll be able to design and develop distributed systems. You’ll be able to make optimal design and architectural choices based on the application requirements.

Introduction to Distributed Systems for Dummies

All the above are actually achieved in partitioning.

Which one is not a benefit of partitioning?

Which one is not a benefit of partitioning?

**(True or False)** In a distributed system, either partitioning or replication can be used, not both.

(True or False) In a distributed system, either partitioning or replication can be used, not both.

Which partitioning technique is not suitable for data that is already skewed over the key?

Which partitioning technique is not suitable for data that is already skewed over the key?

Which of the following partitioning techniques is the most efficient for rebalancing?

Which of the following partitioning techniques is the most efficient for rebalancing?

A virtual node is never lost since it does not exist —big win.

What is the advantage of virtual nodes while partitioning data?

What is the advantage of virtual nodes while partitioning data?

Evaluate your understanding of partitioning and its techniques in distributed systems.

Introduction

What Distributed Systems Achieve for Us

Data in Distributed Systems

Communication Between Nodes

Data Processing in Large Scale

Distributed System Architectural Patterns

Case Study 1: Apache Spark

Case Study 2: Apache Druid

Conclusion

Quick Quiz #7: Partitioning