Data Engineering on Google Cloud Platform
This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.
Google Cloud Platform Training Objectives
This course teaches participants the following skills:
- Design and build data processing systems on Google Cloud Platform
- Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow
- Derive business insights from extremely large datasets using Google BigQuery
- Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML
- Leverage unstructured data using Spark and ML APIs on Cloud Dataproc
- Enable instant insights from streaming data
This class is intended for experienced developers who are responsible for managing big data transformations including:
- Extracting, Loading, Transforming, cleaning, and validating data
- Designing pipelines and architectures for data processing
- Creating and maintaining machine learning and statistical models
- Querying datasets, visualizing query results and creating reports
To get the most of out of this course, participants should have:
- Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience
- Basic proficiency with common query language such as SQL
- Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python
- Familiarity with Machine Learning and/or statistics
Google Cloud Platform Training Course Outline
Module 1: Google Cloud Dataproc Overview
- Creating and managing clusters.
- Leveraging custom machine types and preemptible worker nodes.
- Scaling and deleting Clusters.
- Lab: Creating Hadoop Clusters with Google Cloud Dataproc.
Module 2: Running Dataproc Jobs
- Running Pig and Hive jobs.
- Separation of storage and compute.
- Lab: Running Hadoop and Spark Jobs with Dataproc.
- Lab: Submit and monitor jobs.
Module 3: Integrating Dataproc with Google Cloud Platform
- Customize cluster with initialization actions.
- BigQuery Support.
- Lab: Leveraging Google Cloud Platform Services.
Module 4: Making Sense of Unstructured Data with Google’s Machine Learning APIs
- Google’s Machine Learning APIs.
- Common ML Use Cases.
- Invoking ML APIs.
- Lab: Adding Machine Learning Capabilities to Big Data Analysis.
Module 5: Serverless data analysis with BigQuery
- What is BigQuery.
- Queries and Functions.
- Lab: Writing queries in BigQuery.
- Loading data into BigQuery.
- Exporting data from BigQuery.
- Lab: Loading and exporting data.
- Nested and repeated fields.
- Querying multiple tables.
- Lab: Complex queries.
- Performance and pricing.
Module 6: Serverless, autoscaling data pipelines with Dataflow
- The Beam programming model.
- Data pipelines in Beam Python.
- Data pipelines in Beam Java.
- Lab: Writing a Dataflow pipeline.
- Scalable Big Data processing using Beam.
- Lab: MapReduce in Dataflow.
- Incorporating additional data.
- Lab: Side inputs.
- Handling stream data.
- GCP Reference architecture.
Module 7: Getting started with Machine Learning
- What is machine learning (ML).
- Effective ML: concepts, types.
- ML datasets: generalization.
- Lab: Explore and create ML datasets.
Module 8: Building ML models with Tensorflow
- Getting started with TensorFlow.
- Lab: Using tf.learn.
- TensorFlow graphs and loops + lab.
- Lab: Using low-level TensorFlow + early stopping.
- Monitoring ML training.
- Lab: Charts and graphs of TensorFlow training.
Module 9: Scaling ML models with CloudML
- Why Cloud ML?
- Packaging up a TensorFlow model.
- End-to-end training.
- Lab: Run a ML model locally and on cloud.
Module 10: Feature Engineering
- Creating good features.
- Transforming inputs.
- Synthetic features.
- Preprocessing with Cloud ML.
- Lab: Feature engineering.
Module 11: Architecture of streaming analytics pipelines
- Stream data processing: Challenges.
- Handling variable data volumes.
- Dealing with unordered/late data.
- Lab: Designing streaming pipeline.
Module 12: Ingesting Variable Volumes
- What is Cloud Pub/Sub?
- How it works: Topics and Subscriptions.
- Lab: Simulator.
Module 13: Implementing streaming pipelines
- Challenges in stream processing.
- Handle late data: watermarks, triggers, accumulation.
- Lab: Stream data processing pipeline for live traffic data.
Module 14: Streaming analytics and dashboards
- Streaming analytics: from data to decisions.
- Querying streaming data with BigQuery.
- What is Google Data Studio?
- Lab: build a real-time dashboard to visualize processed data.
Module 15: High throughput and low-latency with Bigtable
- What is Cloud Spanner?
- Designing Bigtable schema.
- Ingesting into Bigtable.
- Lab: streaming into Bigtable.