Data Engineering on Google Cloud Platform Course Overview

Enroll for the 4-day Data Engineering on Google Cloud Platform Training course from Koenig Solutions.  In this course you will learn about introduction to designing and building data processing systems on Google Cloud Platform. 

Through a blend of hands-on labs and interactive lectures, you 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.

Target Audience:

This class is intended for experienced developers who are responsible for managing big data transformations.

Learning Objectives:

After completing this course, you will be able to:

  • 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 is a Rare Course and it can be take up to 3 weeks to arrange the training.

The 1-on-1 Advantage

Methodology

Flexible Dates

  • • Choose Start Date
  • • Reschedule After Booking
  • • Weekend / Evening Option

4-Hour Sessions

You will learn:

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
  • Running Pig and Hive jobs.
  • Separation of storage and compute.
  • Lab: Running Hadoop and Spark Jobs with Dataproc.
  • Lab: Submit and monitor jobs.
  • Customize cluster with initialization actions.
  • BigQuery Support.
  • Lab: Leveraging Google Cloud Platform Services.
  • Google’s Machine Learning APIs
  • Common ML Use Cases
  • Invoking ML APIs
  • Lab: Adding Machine Learning Capabilities to Big Data Analysis
  • 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.
  • 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
  • What is machine learning (ML).
  • Effective ML: concepts, types.
  • ML datasets: generalization
  • Lab: Explore and create ML datasets.
  • 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.
  • Why Cloud ML?
  • Packaging up a TensorFlow model.
  • End-to-end training
  • Lab: Run a ML model locally and on cloud
  • Creating good features.
  • Transforming inputs.
  • Synthetic features
  • Preprocessing with Cloud ML.
  • Lab: Feature engineering.
  • Stream data processing: Challenges.
  • Handling variable data volumes.
  • Dealing with unordered/late data.
  • Lab: Designing streaming pipeline
  • What is Cloud Pub/Sub?
  • How it works: Topics and Subscriptions
  • Lab: Simulator.
  • Challenges in stream processing.
  • Handle late data: watermarks, triggers, accumulation
  • Lab: Stream data processing pipeline for live traffic data.
  • 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.
  • What is Cloud Spanner?
  • Designing Bigtable schema
  • Ingesting into Bigtable
  • Lab: streaming into Bigtable.
Live Online Training (Duration : 32 Hours) Fee On Request
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

4 Hours
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
Date On Request
Course Prerequisites
  • Completed Google Cloud Fundamentals: Big Data & 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 as Python
  • Familiarity with Machine Learning and/or statistics
 

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