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Google Cloud Certified – Professional Data Engineer (Bundle)

Google Cloud Certified – Professional Data Engineer (Bundle)

Duration

5 Days

This track includes :

Google Cloud Platform Fundamentals: Big Data & Machine Learning – 1 Day

Data Engineering on Google Cloud Platform – 4 Days

Description

Google Cloud Platform Fundamentals: Big Data & Machine Learning

This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.

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.

Objectives

Google Cloud Platform Fundamentals: Big Data & Machine Learning

This course teaches participants the following skills:

  • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.
  • Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.
  • Employ BigQuery and Cloud Datalab to carry out interactive data analysis.
  • Train and use a neural network using TensorFlow.
  • Employ ML APIs.
  • Choose between different data processing products on the Google Cloud Platform.

Data Engineering on Google Cloud Platform

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

Audience

This course is intended for the following Participants:

Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform.

  • Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports.
  • Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists

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

Prerequisites

To get the most out of this course, participants should:

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.

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

Course Outline

Google Cloud Platform Fundamentals: Big Data & Machine Learning

Module 1:

Introducing Google Cloud Platform Google Platform Fundamentals Overview. Google Cloud Platform Big Data Products.

Module 2:

Compute and Storage Fundamentals CPUs on demand (Compute Engine). A global filesystem (Cloud Storage). CloudShell. Lab: Set up a Ingest-Transform-Publish data processing pipeline.

Module 3:

Data Analytics on the Cloud Stepping-stones to the cloud. Cloud SQL: your SQL database on the cloud. Lab: Importing data into CloudSQL and running queries. Spark on Dataproc. Lab: Machine Learning Recommendations with Spark on Dataproc.

Module 4:

Scaling Data Analysis Fast random access. Datalab. BigQuery. Lab: Build machine learning dataset. www.cloudassistsvcs.com

Module 5:

Machine Learning Machine Learning with TensorFlow. Lab: Carry out ML with TensorFlow Pre-built models for common needs. Lab: Employ ML APIs.

Module 6:

Data Processing Architectures Message-oriented architectures with Pub/Sub. Creating pipelines with Dataflow. Reference architecture for real-time and batch data processing.

Module 7:

Summary Why GCP? Where to go from here Additional Resources

Data Engineering on Google Cloud Platform

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. www.cloudassistsvcs.com
  • 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 www.cloudassistsvcs.com

  • 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

Certification

Google Cloud Certified – Professional Data Engineer

Schedule

Course Title Days Price Promo Price Feb Mar Apr May  Jun Register
Google Cloud Certified – Professional Data Engineer

Google Cloud Platform Fundamentals: Big Data & Machine Learning (1 Day)

Data Engineering on Google Cloud Platform (4 Days)

(Include Certification Exam Voucher)

5 $3750 $3500 17,18,19,20,21 23,2425,26,27 26,27,28,29,30 25,26,27,28,29 22,23,24,25,26 Register

*Weekend Class

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