Data Engineering on Google Cloud

    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

    In this course, participants will learn the following skills:
    • Draw and create data processing systems on Google Cloud Platform.
    • Batch and Data Streaming Processing, implementing automatically scaled data channels in Cloud Dataflow.
    • Get business insights from extremely large data sets with Google BigQuery.
    • Train, evaluate and predict with machine learning models using TensorFlow and Cloud ML.
    • Leverage unstructured data with Spark and machine learning APIs in Cloud Dataproc.
    • Provide instant information on data transmission.

    Audience

    This class is for experienced developers responsible for managing Big Data transformations, including:
    • extract, load, transform, clean and validate data;
    • design pipelines and architectures for data processing;
    • create and maintain machine learning models and statistical models; query data sets, view query results, and create reports.

    Prerequisites

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

    • Completed Google Cloud Fundamentals Big Data and 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 basic statistics

    Duration

    32 hours (4 days)

    Investment

    Check the next open public class in our enrollment page. If you are interested in a private training class for your company, contact-us.
    Data Engineering on Google Cloud dependencies with other courses and certifications
    Data Engineering on Google Cloud dependencies with other courses and certifications

    Course Outline

    The course includes presentations, demonstrations, and hands-on labs.
    • 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.
    • 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.
    • 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.
    • 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.
    • The Hadoop ecosystem.
    • Running Hadoop on Cloud Dataproc.
    • GCS instead of HDFS.
    • Optimizing Dataproc.
    • Lab: Running Apache Spark jobs on Cloud Dataproc.
    • 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.
    • 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.
    • Processing Streaming Data.
    • Cloud Pub/Sub.
    • Lab: Publish Streaming Data into Pub/Sub.
    • Cloud Dataflow Streaming Features.
    • Lab: Streaming Data Pipelines.
    • BigQuery Streaming Features.
    • Lab: Streaming Analytics and Dashboards.
    • Cloud Bigtable.
    • Lab: Streaming Data Pipelines into Bigtable.
    • 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.
    • What is AI?.
    • From Ad-hoc Data Analysis to Data Driven Decisions.
    • Options for ML models on GCP.
    • Unstructured Data is Hard.
    • ML APIs for Enriching Data.
    • Lab: Using the Natural Language API to Classify Unstructured Text.
    • Whats a Notebook.
    • BigQuery Magic and Ties to Pandas.
    • Lab: BigQuery in Jupyter Labs on AI Platform.
    • Ways to do ML on GCP.
    • Kubeflow.
    • AI Hub.
    • Lab: Running AI models on Kubeflow.
    • 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.
    • Why Auto ML?
    • Auto ML Vision.
    • Auto ML NLP.
    • Auto ML Tables.
    Check our schedule and enroll