In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX).

The Apache Beam unified model allows us to process batch as well as streaming data using the same API. Several execution backends such as Google Cloud Dataflow, Apache Spark, and Apache Flink are compatible with Beam.

Streaming analytics can be difficult to implement. This course will teach you to model real-time data processing with Spark Structured Streaming.

In this session, Jason Maude from Starling Bank answers questions posed by Big Data LDN about why it is possible for challenger banks to be on a level playing field with big banks due to advances in technology, modern team structures and processes.

The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud Platform in technical detail.

This course teaches you how to modernize data architecture for innovation, which will help you as a data professional to better plan out and improve your organization’s data architecture to accommodate the growing need to embrace innovation.

This course covers different ways to alter document data in Couchbase using the N1QL query language. It explores the queries used to add new data, modify existing data, and remove unwanted documents.

The structure of document-oriented databases, such as MongoDB, introduce benefits and challenges when working with data. This course explores the create, update, and delete portion of CRUD from the perspective of a MongoDB administrator or developer.

Mona discusses how creativity plays a role when illustrating sensitive data and so many more unique insights.

This course will teach you you the essential skills and tools required to manage MongoDB effectively. It covers MongoDB administration tools, syntax, and management principles addressing everything from installation to configuration, backups, monitoring, scaling out, security, and performance tuning.

Learn how to monitor and manage data engineering workloads with Apache Spark in Azure Synapse Analytics.

This course covers different ways to monitor a Couchbase cluster - from gathering diagnostic data from logs and real-time statistics, to the analysis of running and completed N1QL queries. These are performed using the Couchbase UI, CLI and REST API.