Virtual Live Instructor
Free Training Materials
Convenient Scheduling
Course Description:
The Data Engineering on Google Cloud Platform course is a 4-day course that was designed to help students learn how to design and build data processing systems on the Google Cloud Platform (GCP).
This course provides students with 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, students 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.
Topics covered in the course include:
Target Student: This course is ideal for experienced developers that are responsible for managing big data transformations including:
Prerequisites:
Course Outline
Section 1: Google Cloud Dataproc Overview
Section 2: Running Dataproc Jobs
Section 3: Integrating Dataproc with Google Cloud Platform
Section 4: Making Sense of Unstructured Data with Google’s Machine Learning APIs
Section 5: Serverless data analysis with BigQuery
Section 6: Serverless, autoscaling data pipelines with Dataflow
Section 7: Getting started with Machine Learning
Section 8: Building ML models with Tensorflow
Section 9: Scaling ML models with CloudML
Section 10: Feature Engineering
Section 11: Architecture of streaming analytics pipelines
Section 12: Ingesting Variable Volumes
Section 13: Implementing streaming pipelines
Section 14: Streaming analytics and dashboards
Section 15: High throughput and low-latency with Bigtable
Lab 1: Creating Hadoop Clusters with Google Cloud Dataproc.
Lab 2: Running Hadoop and Spark Jobs with Dataproc.
Lab 3: Submit and monitor jobs.
Lab 4: Leveraging Google Cloud Platform Services.
Lab 5: Adding Machine Learning Capabilities to Big Data Analysis.
Lab 6: Writing queries in BigQuery.
Lab 7: Loading and exporting data.
Lab 8: Complex queries.
Lab 9: Writing a Dataflow pipeline.
Lab 10: MapReduce in Dataflow.
Lab 11: Side inputs.
Lab 12: Explore and create ML datasets.
Lab 13: Using tf.learn.
Lab 14: Using low-level TensorFlow + early stopping.
Lab 15: Charts and graphs of TensorFlow training.
Lab 16: Run a ML model locally and on cloud.
Lab 17: Feature engineering.
Lab 18: Designing streaming pipeline.
Lab 19: Simulator.
Lab 20: Stream data processing pipeline for live traffic data.
Lab 21: build a real-time dashboard to visualize processed data.
Lab 22: streaming into Bigtable.
Topics covered in the course include:
Target Student: This course is ideal for:
This was the class I needed.
The instructor Jeff took his time and made sure we understood each topic before moving to the next. He answered all of our questions, and I don't know about the rest of the students, but was very pleased with this experience.
I finally understand how to use Excel.
-Amanda T (Yale New Haven Hospital).
Great class!
We were able to cover a lot of information in one day without getting overwhelmed.
-Maria R (Microsoft).
Instructor led training is a cost effective and convenient learning platform for busy professionals. Most courses are available at over 300 locations nationwide and Online.
The classes are taught via the RCI method by professionally certified instructors, and are usually limited to 12 or less students. Each student receives a training manual and practice problems, along with a free course retake. Click here to learn more about Instructor Led Training
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