Data Engineering on Google Cloud Platform

Price
$2,495.00 USD

Duration
4 Days

 

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.

Course Objectives

  • Design and build data processing systems on Google Cloud Platform.
  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc.
  • 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.
  • Enable instant insights from streaming data

    • Who Should Attend?

  • 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
    • Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
    • Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
    • Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
    • Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
    • Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
    • Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

    Course Prerequisites

  • Completed Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM) 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 basic statistics

    • Agenda

    1 - Introduction to Data Engineering

    • Explore the role of a data engineer.
    • Analyze data engineering challenges.
    • Intro to BigQuery.
    • Data Lakes and Data Warehouses.
    • Demo: Federated Queries with BigQuery.
    • Transactional Databases vs Data Warehouses.
    • Website Demo: Finding PII in your dataset with DLP API.
    • Partner effectively with other data teams.
    • Manage data access and governance.
    • Build production-ready pipelines.
    • Review GCP customer case study.
    • Lab: Analyzing Data with BigQuery.

    2 - Building a Data Lake

    • Introduction to Data Lakes.
    • Data Storage and ETL options on GCP.
    • Building a Data Lake using Cloud Storage.
    • Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
    • Securing Cloud Storage.
    • Storing All Sorts of Data Types.
    • Video Demo: Running federated queries on Parquet and ORC files in BigQuery.
    • Cloud SQL as a relational Data Lake.
    • Lab: Loading Taxi Data into Cloud SQL.

    3 - Building a Data Warehouse

    • The modern data warehouse.
    • Intro to BigQuery.
    • Demo: Query TB+ of data in seconds.
    • Getting Started.
    • Loading Data.
    • Video Demo: Querying Cloud SQL from BigQuery.
    • Lab: Loading Data into BigQuery.
    • Exploring Schemas.
    • Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
    • Schema Design.
    • Nested and Repeated Fields.
    • Demo: Nested and repeated fields in BigQuery.
    • Lab: Working with JSON and Array data in BigQuery.
    • Optimizing with Partitioning and Clustering.
    • Demo: Partitioned and Clustered Tables in BigQuery.
    • Preview: Transforming Batch and Streaming Data.

    4 - Introduction to Building Batch Data Pipelines,

    • EL, ELT, ETL.
    • Quality considerations.
    • How to carry out operations in BigQuery.
    • Demo: ELT to improve data quality in BigQuery.
    • Shortcomings.
    • ETL to solve data quality issues.

    5 - Executing Spark on Cloud Dataproc

    • The Hadoop ecosystem.
    • Running Hadoop on Cloud Dataproc.
    • GCS instead of HDFS.
    • Optimizing Dataproc.
    • Lab: Running Apache Spark jobs on Cloud Dataproc.

    6 - Serverless Data Processing with Cloud Dataflow

    • Cloud Dataflow.
    • Why customers value Dataflow.
    • Dataflow Pipelines.
    • Lab: A Simple Dataflow Pipeline (Python/Java).
    • Lab: MapReduce in Dataflow (Python/Java).
    • Lab: Side Inputs (Python/Java).
    • Dataflow Templates.
    • Dataflow SQL.

    7 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

    • Building Batch Data Pipelines visually with Cloud Data Fusion.
    • Components.
    • UI Overview.
    • Building a Pipeline.
    • Exploring Data using Wrangler.
    • Lab: Building and executing a pipeline graph in Cloud Data Fusion.
    • Orchestrating work between GCP services with Cloud Composer.
    • Apache Airflow Environment.
    • DAGs and Operators.
    • Workflow Scheduling.
    • Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.
    • Monitoring and Logging.
    • Lab: An Introduction to Cloud Composer.

    8 - Introduction to Processing Streaming Data

    • Processing Streaming Data.

    9 - Serverless Messaging with Cloud Pub/Sub

    • Cloud Pub/Sub.
    • Lab: Publish Streaming Data into Pub/Sub.

    10 - Cloud Dataflow Streaming Features

    • Cloud Dataflow Streaming Features.
    • Lab: Streaming Data Pipelines.

    11 - High-Throughput BigQuery and Bigtable Streaming Features

    • BigQuery Streaming Features.
    • Lab: Streaming Analytics and Dashboards.
    • Cloud Bigtable.
    • Lab: Streaming Data Pipelines into Bigtable.

    12 - Advanced BigQuery Functionality and Performance

    • Analytic Window Functions.
    • Using With Clauses.
    • GIS Functions.
    • Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.
    • Performance Considerations.
    • Lab: Optimizing your BigQuery Queries for Performance.
    • Optional Lab: Creating Date-Partitioned Tables in BigQuery.

    13 - Introduction to Analytics and AI

    • What is AI?.
    • From Ad-hoc Data Analysis to Data Driven Decisions.
    • Options for ML models on GCP.

    14 - Prebuilt ML model APIs for Unstructured Data

    • Unstructured Data is Hard.
    • ML APIs for Enriching Data.
    • Lab: Using the Natural Language API to Classify Unstructured Text.

    15 - Big Data Analytics with Cloud AI Platform Notebooks

    • Whats a Notebook.
    • BigQuery Magic and Ties to Pandas.
    • Lab: BigQuery in Jupyter Labs on AI Platform.

    16 - Production ML Pipelines with Kubeflow

    • Ways to do ML on GCP.
    • Kubeflow.
    • AI Hub.
    • Lab: Running AI models on Kubeflow.

    17 - Custom Model building with SQL in BigQuery ML

    • BigQuery ML for Quick Model Building.
    • Demo: Train a model with BigQuery ML to predict NYC taxi fares.
    • Supported Models.
    • Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.
    • Lab Option 2: Movie Recommendations in BigQuery ML.

    18 - Custom Model building with Cloud AutoML

    • Why Auto ML?
    • Auto ML Vision.
    • Auto ML NLP.
    • Auto ML Tables.
     

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