Data Engineering with Google Cloud Platform, Virtual PDT

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<H2><SPAN>Data Engineering with Google Cloud Platform</SPAN></H2>
<H4><SPAN>(4 days)</SPAN></H4>
<P> </P>
<P>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.</P>
<H4><SPAN>Objectives</SPAN></H4>
<P>This course teaches participants the following skills:</P>
<UL>
<LI>Design and build data processing systems on Google Cloud Platform</LI>
<LI>Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow</LI>
<LI>Derive business insights from extremely large datasets using Google BigQuery</LI>
<LI>Train, evaluate, and predict using machine learning models using Tensorflow and Cloud ML</LI>
<LI>Leverage unstructured data using Spark and ML APIs on Cloud Dataproc</LI>
<LI>Enable instant insights from streaming data</LI>
</UL>
<H4><SPAN>Audience</SPAN></H4>
<P>This class is intended for experienced developers who are responsible for managing big data transformations including:</P>
<UL>
<LI>Extracting, Loading, Transforming, cleaning, and validating data</LI>
<LI>Designing pipelines and architectures for data processing</LI>
<LI>Creating and maintaining machine learning and statistical models</LI>
<LI>Querying datasets, visualizing query results, and creating reports</LI>
</UL>
<H4><SPAN>Prerequisites</SPAN></H4>
<P>To get the most of out of this course, participants should have:</P>
<UL>
<LI>Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience</LI>
<LI>Basic proficiency with common query language such as SQL</LI>
<LI>Experience with data modeling, extract, transform, load activities</LI>
<LI>Developing applications using a common programming language such as Python</LI>
<LI>Familiarity with Machine Learning and/or statistics</LI>
</UL>
<HR>
<H3><SPAN>Course Outline</SPAN></H3>
<H4><SPAN>Day 1: <STRONG>Modernizing Data Lakes and Data Warehouses with GCP</STRONG></SPAN></H4>
<H4>Module 1: Introduction to Data Engineering</H4>
<UL>
<LI>Explore the role of a data engineer</LI>
<LI>Analyze data engineering challenges</LI>
<LI>Intro to BigQuery</LI>
<LI>Data Lakes and Data Warehouses</LI>
<LI>Demo: Federated Queries with BigQuery</LI>
<LI>Transactional Databases vs Data Warehouses</LI>
<LI>Website Demo: Finding PII in your dataset with DLP API</LI>
<LI>Partner effectively with other data teams</LI>
<LI>Manage data access and governance</LI>
<LI>Build production-ready pipelines</LI>
<LI>Review GCP customer case study</LI>
<LI>Lab: Analyzing Data with BigQuery</LI>
</UL>
<H4>Module 2: Building a Data Lake</H4>
<UL>
<LI>Introduction to Data Lakes</LI>
<LI>Data Storage and ETL options on GCP</LI>
<LI>Building a Data Lake using Cloud Storage</LI>
<LI>Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions</LI>
<LI>Securing Cloud Storage</LI>
<LI>Storing All Sorts of Data Types</LI>
<LI>Video Demo: Running federated queries on Parquet and ORC files in BigQuery</LI>
<LI>Cloud SQL as a relational Data Lake</LI>
<LI>Lab: Loading Taxi Data into Cloud SQL</LI>
</UL>
<H4>Module 3: Building a Data Warehouse</H4>
<UL>
<LI>The modern data warehouse</LI>
<LI>Intro to BigQuery</LI>
<LI>Demo: Query TB+ of data in seconds</LI>
<LI>Getting Started</LI>
<LI>Loading Data</LI>
<LI>Video Demo: Querying Cloud SQL from BigQuery</LI>
<LI>Lab: Loading Data with Console and CLI</LI>
<LI>Exploring Schemas</LI>
<LI>Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA</LI>
<LI>Schema Design</LI>
<LI>Nested and Repeated Fields</LI>
<LI>Demo: Nested and repeated fields in BigQuery</LI>
<LI>Lab: ARRAYs and STRUCTs</LI>
<LI>Optimizing with Partitioning and Clustering</LI>
<LI>Demo: Partitioned and Clustered Tables in BigQuery</LI>
<LI>Preview: Transforming Batch and Streaming Data</LI>
</UL>
<H4><SPAN>Day 2: <STRONG>Batch Processing of Data with Spark and Hadoop on GCP</STRONG></SPAN></H4>
<H4>Module 1 – Introduction to Building Batch Data Pipelines</H4>
<UL>
<LI>EL, ELT, ETL</LI>
<LI>Quality considerations</LI>
<LI>How to carry out operations in BigQuery</LI>
<LI>Demo: ELT to improve data quality in BigQuery</LI>
<LI>Shortcomings</LI>
<LI>ETL to solve data quality issues</LI>
</UL>
<H4>Module 2 – Executing Spark on Cloud Dataproc</H4>
<UL>
<LI>The Hadoop ecosystem</LI>
<LI>Running Hadoop on Cloud Dataproc</LI>
<LI>GCS instead of HDFS</LI>
<LI>Optimizing Dataproc</LI>
<LI>Lab: Running Apache Spark jobs on Cloud Dataproc</LI>
</UL>
<H4>Module 3 – Serverless Data Processing with Cloud Dataflow</H4>
<UL>
<LI>Cloud Dataflow</LI>
<LI>Why customers value Dataflow</LI>
<LI>Dataflow Pipelines</LI>
<LI>Lab: A Simple Dataflow Pipeline (Python/Java)</LI>
<LI>Lab: MapReduce in Dataflow (Python/Java)</LI>
<LI>Lab: Side Inputs (Python/Java)</LI>
<LI>Dataflow Templates</LI>
<LI>Dataflow SQL</LI>
</UL>
<H4>Module 4: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer</H4>
<UL>
<LI>Building Batch Data Pipelines visually with Cloud Data Fusion
<UL>
<LI>Components</LI>
<LI>UI Overview</LI>
<LI>Building a Pipeline</LI>
<LI>Exploring Data using Wrangler</LI>
</UL>
</LI>
<LI>Lab: Building and executing a pipeline graph in Cloud Data Fusion</LI>
<LI>Orchestrating work between GCP services with Cloud Composer
<UL>
<LI>Apache Airflow Environment</LI>
<LI>DAGs and Operators</LI>
<LI>Workflow Scheduling</LI>
</UL>
</LI>
<LI>Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery
<UL>
<LI>Monitoring and Logging</LI>
</UL>
</LI>
<LI>Lab: An Introduction to Cloud Composer</LI>
</UL>
<H4><SPAN>Day 3: <STRONG>Building Resilient Streaming Analytics Systems on GCP</STRONG></SPAN></H4>
<H4>Module 1: Introduction to Processing Streaming Data</H4>
<UL>
<LI>Processing Streaming Data</LI>
</UL>
<H4>Module 2: Serverless Messaging with Cloud Pub/Sub</H4>
<UL>
<LI>Cloud Pub/Sub</LI>
<LI>Lab: Publish Streaming Data into Pub/Sub</LI>
</UL>
<H4>Module 3: Cloud Dataflow Streaming Features</H4>
<UL>
<LI>Cloud Pub/Sub</LI>
<LI>Lab: Publish Streaming Data into Pub/Sub</LI>
</UL>
<H4>Module 4: High-Throughput BigQuery and Bigtable Streaming Features</H4>
<UL>
<LI>BigQuery Streaming Features</LI>
<LI>Lab: Streaming Analytics and Dashboards</LI>
<LI>Cloud Bigtable</LI>
<LI>Lab: Streaming Data Pipelines into Bigtable</LI>
</UL>
<H4>Module 5: Advanced BigQuery Functionality and Performance</H4>
<UL>
<LI>Analytic Window Functions</LI>
<LI>Using With Clauses</LI>
<LI>GIS Functions</LI>
<LI>Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz</LI>
<LI>Performance Considerations</LI>
<LI>Lab: Optimizing your BigQuery Queries for Performance</LI>
<LI>Optional Lab: Creating Date-Partitioned Tables in BigQuery</LI>
</UL>
<H4><SPAN>Day 4: <STRONG>Smart Analytics, Machine Learning and AI on GCP</STRONG></SPAN></H4>
<H4>Module 1: Introduction to Analytics and AI</H4>
<UL>
<LI>What is AI?</LI>
<LI>From Ad-hoc Data Analysis to Data Driven Decisions</LI>
<LI>Options for ML models on GCP</LI>
</UL>
<H4>Module 2: Prebuilt ML model APIs for Unstructured Data</H4>
<UL>
<LI>Unstructured Data is Hard</LI>
<LI>ML APIs for Enriching Data</LI>
<LI>Lab: Using the Natural Language API to Classify Unstructured Text</LI>
</UL>
<H4>Module 3: <STRONG>Big Data Analytics with Cloud AI Platform Notebooks</STRONG></H4>
<UL>
<LI>What’s a Notebook</LI>
<LI>BigQuery Magic and Ties to Pandas</LI>
<LI>Lab: BigQuery in Jupyter Labs on AI Platform</LI>
</UL>
<H4>Module 4: Production ML Pipelines with Kubeflow</H4>
<UL>
<LI>Ways to do ML on GCP</LI>
<LI>Kubeflow</LI>
<LI>AI Hub</LI>
<LI>Lab: Running AI models on Kubeflow</LI>
</UL>
<H4>Module 5: Custom Model building with SQL in BigQuery ML</H4>
<UL>
<LI>BigQuery ML for Quick Model Building</LI>
<LI>Demo: Train a model with BigQuery ML to predict NYC taxi fares</LI>
<LI>Supported Models</LI>
<LI>Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML</LI>
<LI>Lab Option 2: Movie Recommendations in BigQuery ML</LI>
</UL>
<H4>Module 6: Custom Model building with Cloud AutoML</H4>
<UL>
<LI>Why Auto ML?</LI>
<LI>Auto ML Vision</LI>
<LI>Auto ML NLP</LI>
<LI>Auto ML Tables</LI>
</UL>
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Data Engineering with Google Cloud Platform, Virtual PDT

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Data Engineering with Google Cloud Platform, Virtual PDT

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