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
What's 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.
Actual course outline may vary depending on offering center. Contact your sales representative for more information.
Who is it For?
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
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