Data Engineering on Google Cloud Platform

$2,099.00 $1,799.00
gcp-fundamentals-v2
Data Engineering on Google Cloud Platform - 1WorldTraining

This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform.

Course Provider: Organization

Editor's Rating:
3.9

Overview

This four-day instructor-led class provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform.

Course Description

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.

Duration

4 days

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

Delivery Method

Instructor-led, Instructor-led online

Audience

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

Prerequisites

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

  • 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 Features

  • Lectures 0
  • Quizzes 0
  • Duration 50 hours
  • Skill level All levels
  • Language English
  • Students 0
  • Assessments Yes
  • 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. 0

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  • 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. 0

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  • Module 3: Integrating Dataproc with Google Cloud Platform Customize cluster with initialization actions. BigQuery Support. Lab: Leveraging Google Cloud Platform Services. 0

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  • 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. 0

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  • 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. 0

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  • 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. 0

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  • Module 10: Feature Engineering Creating good features. Transforming inputs. Synthetic features. Preprocessing with Cloud ML. Lab: Feature engineering. 0

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  • Module 11: Architecture of streaming analytics pipelines Stream data processing: Challenges. Handling variable data volumes. Dealing with unordered/late data. Lab: Designing streaming pipeline. 0

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  • Module 12: Ingesting Variable Volumes What is Cloud Pub/Sub? How it works: Topics and Subscriptions. Lab: Simulator. 0

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  • Module 13: Implementing streaming pipelines Challenges in stream processing. Handle late data: watermarks, triggers, accumulation. Lab: Stream data processing pipeline for live traffic data. 0

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  • 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. 0

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  • Module 15: High throughput and low-latency with Bigtable What is Cloud Spanner? Designing Bigtable schema. Ingesting into Bigtable. Lab: streaming into Bigtable. 0

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1WorldTraining.com has a team of Google, Amazon, IBM & Azure cloud technologies trainers, who have worked as trainers in MNCs and hold many cloud technologies certifications such as:

Google Cloud Certified Professional - Cloud Architect
Google Cloud Certified Professional - Data Engineer
Foundations of IBM DevOps V1
IBM Certified Cloud Architect V5
IBM Certified Solution Advisor V2
IBM Certified Solution Advisor - SoftLayer V1
IBM Certified Application Developer - Cloud Platform V1
IBM Certified Application Developer - Cloud Platform V2
IBM Certified Application Developer - Watson V3 Certification
AWS Certified Solutions Architect – Professional (Amazon Web Services)
Microsoft Azure Administrator
Microsoft Azure Architect Design
Developing Solutions for Microsoft Azure
Microsoft Azure DevOps Solutions
Configuring and Operating a Hybrid Cloud with Microsoft Azure Stack

Our trainers have successfully delivered trainings in Cloud Computing for multiple platforms (Google, AWS, IBM & Azure). SDA Cloud team has consulting and training expertise in Data Engineering languages such as BigQuery, BigTable, DataProc, Hadoop, Cloudant, Machine learning languages/platforms such as IBM Watson, TensorFlow, DevOps languages such as Kubernetes , Docker , Jenkins , Spinnaker, IBM Cloud Garage methods, Microservices such as Kubernetes and Docker and Google kubernetes Engine.

$2,099.00 $1,799.00

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