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Data Engineering on Google Cloud Platform

( Duration: 4 Days )

This Data Engineering on Google Cloud Platform training course provides introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, you will learn 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.

By attending Data Engineering on Google Cloud Platform workshop, delegates will learn 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

  • Attend a training on Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM) 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



Google Cloud Dataproc Overview

  • Creating and managing clusters
  • Leveraging custom machine types and preemptible worker nodes
  • Scaling and deleting Clusters

Running Dataproc Jobs

  • Running Pig and Hive jobs
  • Separation of storage and compute

Integrating Dataproc with Google Cloud Platform

  • Customize cluster with initialization actions
  • BigQuery Support

Making Sense of Unstructured Data with Google’s Machine Learning APIs

  • Google’s Machine Learning APIs
  • Common ML Use Cases
  • Invoking ML APIs

Serverless data analysis with BigQuery

  • What is BigQuery
  • Queries and Functions
  • Loading data into BigQuery
  • Exporting data from BigQuery
  • Nested and repeated fields
  • Querying multiple tables
  • Performance and pricing

Serverless, autoscaling data pipelines with Dataflow

  • The Beam programming model
  • Data pipelines in Beam Python
  • Data pipelines in Beam Java
  • Scalable Big Data processing using Beam
  • Incorporating additional data
  • Handling stream data
  • GCP Reference architecture

Getting started with Machine Learning

  • What is machine learning (ML)
  • Effective ML: concepts, types
  • ML datasets: generalization

Building ML models with Tensorflow

  • Getting started with TensorFlow
  • TensorFlow graphs and loops + lab
  • Monitoring ML training

Scaling ML models with CloudML

  • Why Cloud ML?
  • Packaging up a TensorFlow model
  • End-to-end training

Feature Engineering

  • Creating good features
  • Transforming inputs
  • Synthetic features
  • Preprocessing with Cloud ML

Architecture of streaming analytics pipelines

  • Stream data processing: Challenges
  • Handling variable data volumes
  • Dealing with unordered/late data

Ingesting Variable Volumes

  • What is Cloud Pub/Sub?
  • How it works: Topics and Subscriptions

Implementing streaming pipelines

  • Challenges in stream processing
  • Handle late data: watermarks, triggers, accumulation

Streaming analytics and dashboards

  • Streaming analytics: from data to decisions
  • Querying streaming data with BigQuery
  • What is Google Data Studio?

High throughput and low-latency with Bigtable

  • What is Cloud Spanner?
  • Designing Bigtable schema
  • Ingesting into Bigtable

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 6,000 various courses on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

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