This course introduces how to build robust, scalable, real-time big data systems using a variety of Apache Spark's APIs, including the Streaming, DataFrame, SQL, and DataSources APIs, integrated with Apache Kafka, HDFS and Apache Cassandra.

Google Bigtable is a sophisticated NoSQL offering on the Google Cloud Platform with extremely low latencies. By the end of this course, you'll understand why Bigtable is much more powerful offering than HBase, with linear scaling of your data.

Dataproc is Google’s managed Hadoop offering on the cloud. This course teaches you how the separation of storage and compute allows you to utilize clusters more efficiently purely for processing data and not for storage.

Dataflow represents a fundamentally different approach to Big Data processing than computing engines such as Spark. Dataflow is serverless and fully-managed, meaning that provisioning resources and scaling can be transparent to the data architect.

Get started with Big Data fundamentals. Learn the key concepts, tools, and technologies used to analyze and manage large datasets in modern data ecosystems.

This course introduces you to essential concepts in handling vast datasets, equipping you with the ability to implement an effective data solution.

Learn the fundamentals of working with big data with PySpark.

Learn about the architecture and management of big data systems.

This course brings any Big Data newbie up to speed on one of the most widely used open-source platform to build data pipelines - Apache Kafka.

Learn how to schedule and manage Hadoop jobs using Apache Oozie. Explore its workflow automation capabilities to optimize big data processing.

Explore advanced topics in big data technologies, architectures, and analytical methods.

Learn how to apply software solutions for different systems and Big Data needs to your IoT designs.