For over 10 years, there has been an intense focus by companies to extract business value from their data. Out of this activity, a role called the data scientist emerged. However, it quickly became obvious that a majority of a data scientist’s time was spent on data preparation or moving analytical models into production environments. Thus, the data engineer has emerged as a highly desirable and indispensable member of an analytics project team. The course covers the data analytics life cycle, the role of the data engineering, and the skill set of a successful data engineer. 

With the rapid growth in the volume of data, organizations continue to be challenged with how to transact and later analyze this data in a timely manner. This course covers traditional data warehousing with SQL and newer approaches such as data lakes and NoSQL tools. Provide a mixture of theory and practical considerations, the on-demand course includes recorded lab exercise demonstrations of the Greenplum Database, Redis, Apache Cassandra, and Apache CouchDB. 

This course introduces data governance and data management approaches applicable to a wide range of use cases. Focusing on the various governance roles and models, this course prepares the learner to implement and maintain a successful data governance program. Additionally, the course covers several Apache tools, Atlas, Ranger, and Knox, that enable the management and control of an organization’s datasets in Big Data environments such as Hadoop. The on-demand course includes recorded lab exercise demonstrations of these Apache tools. 

Due to the proliferation of smart devices and the growing need for real-time analysis, the legacy batch processing approaches are insufficient to support modern applications such as credit card fraud detection, cybersecurity protection, and automobile navigation. This course introduces such use cases and the approaches to process streaming data generated by the Internet of Things (IoT). To process and analyze IoT data, this course covers several Apache tools: Storm, Kafka, Spark Streaming, and Flink. Additionally, Pravega, a new storage paradigm, is covered as well as emerging IoT projects, Project Nautilus and EdgeX Foundry. The on-demand course includes recorded lab exercise demonstrations of the Apache tools. 

A key responsibility of a data engineer is to automate the movement and transformation of data from source systems to data repositories such as data lakes. This course covers the fundamentals of Python programming and how to construct data pipelines. This on-demand course includes recorded lab exercise demonstrations of basic Python programming techniques and how Python is used to process and transform data. 

As data volumes continue to grow, more and more demands are placed on data warehousing resources to not only host an organization’s data, but to also merge and prepare the data for end-user consumption. This course covers how off-loading the Extract-Transform-Load (ETL) processes from a data warehouse can improve delivery times and enable the inclusion of new sources of data. This on-demand course includes recorded lab exercise demonstrations of several Apache Hadoop ecosystem tools: Hadoop Distributed File System (HDFS), Spark, Flume, Sqoop, and Oozie.