Batch and Streaming Pipelines on Google Cloud

In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.

In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.

This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow. Prerequisites: The Serverless Data Processing with Dataflow course series builds on the concepts covered in the Data Engineering specialization. We recommend the following prerequisite courses: (i)Building batch data pipelines on Google Cloud : covers core Dataflow principles (ii)Building Resilient Streaming Analytics Systems on Google Cloud : covers streaming basics concepts like windowing, triggers, and watermarks >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<

In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.

This is a self-paced lab that takes place in the Google Cloud console. In addition to batch pipelines, Data Fusion also allows you to create real-time pipelines, that can process events as they are generated. Currently, realtime pipelines execute using Apache Spark Streaming on Cloud Dataproc clusters. In this lab, you will learn how to build a streaming pipeline using Data Fusion.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you’ll be working with Wrangler directives which are used by the Wrangler plugin, the “Swiss Army Knife” of plugins in the Data Fusion platform, so that your transformations are encapsulated in one place and we can group transformation tasks into manageable blocks.

This is a self-paced lab that takes place in the Google Cloud console. This lab will teach you how to use the Pipeline Studio in Cloud Data Fusion to build an ETL pipeline. Pipeline Studio exposes the building blocks and built-in plugins for you to build your batch pipeline, one node at a time. You will also use the Wrangler plugin to build and apply transformations to your data that goes through the pipeline.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to create a Data Fusion instance and deploy a sample pipeline

This is a self-paced lab that takes place in the Google Cloud console. This quickstart shows you how to use Dataflow to read messages published to a Pub/Sub topic, window (or group) the messages by timestamp, and Write the messages to Cloud Storage.

This is a self-paced lab that takes place in the Google Cloud console. This page shows you how to create a streaming pipeline using a Google-Provided Cloud Dataflow template.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will set up your Python development environment, get the Cloud Dataflow SDK for Python, and run an example pipeline using the Google Cloud Platform Console.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you create a streaming data pipeline with Kafka providing you a hands-on look at the Kafka Streams API. You will run a Java application that uses the Kafka Streams library by showcasing a simple end-to-end data pipeline powered by Apache.