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EVENT DETAILS |
Data Engineering on Google Cloud Platform (4 days)
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 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
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 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 Machine Learning and/or statistics
Course Outline Day 1:Modernizing Data Lakes and Data Warehouses with GCP Module 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
Module 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
Module 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 with Console and CLI 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: ARRAYs and STRUCTs Optimizing with Partitioning and Clustering Demo: Partitioned and Clustered Tables in BigQuery Preview: Transforming Batch and Streaming Data
Day 2:Batch Processing of Data with Spark and Hadoop on GCP Module 1 - 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
Module 2 - 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
Module 3 - 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
Module 4: 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
Day 3:Building Resilient Streaming Analytics Systems on GCP Module 1: Introduction to Processing Streaming Data
Processing Streaming Data
Module 2: Serverless Messaging with Cloud Pub/Sub
Cloud Pub/Sub Lab: Publish Streaming Data into Pub/Sub
Module 3: Cloud Dataflow Streaming Features
Cloud Pub/Sub Lab: Publish Streaming Data into Pub/Sub
Module 4: High-Throughput BigQuery and Bigtable Streaming Features
BigQuery Streaming Features Lab: Streaming Analytics and Dashboards Cloud Bigtable Lab: Streaming Data Pipelines into Bigtable
Module 5: 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
Day 4:Smart Analytics, Machine Learning and AI on GCP Module 1: Introduction to Analytics and AI
What is AI? From Ad-hoc Data Analysis to Data Driven Decisions Options for ML models on GCP
Module 2: 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
Module 3:Big Data Analytics with Cloud AI Platform Notebooks
What's a Notebook BigQuery Magic and Ties to Pandas Lab: BigQuery in Jupyter Labs on AI Platform
Module 4: Production ML Pipelines with Kubeflow
Ways to do ML on GCP Kubeflow AI Hub Lab: Running AI models on Kubeflow
Module 5: 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
Module 6: Custom Model building with Cloud AutoML
Why Auto ML? Auto ML Vision Auto ML NLP Auto ML Tables
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