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Become a GCP Data Engineer
Become a Professional Data Engineer on GCP. Design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. This course is part of Google’s Data Engineering track that leads to the Professional Data Engineer certificate.
This 4-day training offers a combination of presentations, demos, and hands-on labs. You will learn how to design data processing systems, build end-to-end data pipelines, analyze data, and carry out Machine Learning.
Clients we've helped
What you'll learn
For everyone familiar with SQL, ETL, and a programming language
- Design and build data processing system on GCP
- Analyze data using GCP services
- Train and deploy machine learning models
- How to deal with structured, unstructured, and streaming data
The schedule
Google Cloud Dataproc Overview
- Creating and managing clusters
- Leverage custom machine types and preemptible worker nodes
- Scaling and deleting Clusters
- Lab: Creating Hadoop Clusters with Google Cloud Dataproc
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
Integrating Dataproc with Google Cloud Platform
- Customize clusters with initialization actions
- BigQuery Support
- Lab: Leveraging Google Cloud Platform Services
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
Serverless Data Analysis With Big Query
- 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
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
Getting Started With Machine Learning
- What is machine learning (ML)
- Effective ML: concepts, types
- ML datasets: generalization
- Lab: Explore and create ML datasets
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
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
Feature Engineering
- Creating good features
- Transforming inputs
- Synthetic features
- Preprocessing with Cloud ML
- Lab: Feature engineering
Architecture of Streaming Analytics Pipelines
- Stream data processing: Challenges
- Handling variable data volumes
- Dealing with unordered/late data
- Lab: Designing streaming pipeline
Ingesting Variable Volumes
- What is Cloud Pub/Sub?
- How it works: Topics and Subscriptions
- Lab: Simulator
Implementing Streaming Pipelines
- Challenges in stream processing
- Handle late data: watermarks, triggers, accumulation
- Lab: Stream data processing pipeline for live traffic data
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
High Throughput and Low-Latency With Bigtable
- What is Cloud Spanner?
- Designing Bigtable schema
- Ingesting into Bigtable
- Lab: streaming into Bigtable
learning journey
Data Engineering Learning Journey
Daniel van der Ende
Data Gandalf
Structured, to-the-point, good combination of theory and practical examples, very knowledgeable trainer who can explain concepts very well
It was a hands-on and tangible course. We could apply what we learned in a matter of minutes. The trainer did a great job of answering ad-hoc questions that complemented the material. We appreciated the fact that we could apply what we were taught directly to our company.
I liked every aspect of this training and would like to thank the trainers. They did an excellent job of explaining how to use Spark for data science. This is the fourth GoDataDriven training I’ve followed. All were great, but this was the best one so far.
Climbing a steep Python and Machine Learning curve in three days. This would have taken me months on my own.