101 Google BigQuery from SQL data analytics to creating machine learning models with BigQuery
In this workshop, we’ll first introduce BigQuery. You’ll then learn how to load and manage data on petabyte-scale, and get hands-on experience creating optimized queries with partitioning, clustering, and materialized views and writing complex queries. Then we dive into BigQuery ML.Models are trained and accessed in BigQuery using SQL — a language developers and data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
The course is designed for developers, digital marketers, data analysts and engineers who are looking to answer complex business questions using large datasets and are familiar with SQL language.
What to expect:
Our workshop is relaxed and informal, covering a lot of ground in a fun and stimulating way. We will be working in Google Cloud Platform, and Qwiklabs datasets using an internet browser, and a simple text/code editor. It will help if you have experience with Google Analytics, APIs, interface, and some familiarity with SQL. There will be publicly available datasets available like Wikipedia, Reddit, Stackoverflow or Github for query exercises.
- Getting to know Google Cloud Platform (GCP)
- BigQuery setup and administration
- Management and access control
- Creating datasets and tables
- Creating simple queries using BigQuery
- Understanding and fixing errors
- Writing functions
- An introduction to Nested Queries
Coding and Beyond:
- BigQuery Connectors
- Loading data to BigQuery
- Streaming Data to Bigquery
- Schema and data transfer overview
- Serverless components
- Cloud Functions - lambda gateway to cloud
- Migrating on premise data to BigQuery
Efficient and optimization techniques:
- Working with partitioned tables
- Working with clustered tables
- Working with materialized views
- Visualizing BigQuery Data using DataStudio
- Introduction to BigQuery ML
- Working with models
- Review existing models
- Build, train, eval and predict, your own scalable ML using SQL
Hands-on examples with BigQuery ML:
- Predict with Linear regression
- Run multiclass logistic regression for classification
- Creating a K-Means clustering model
- Recommend products for up sell using Matrix-Factorization model
- Import and run custom TensorFlow models for prediction in BigQuery
Final and architecture:
- Exporting a BigQuery ML model for online prediction
- Optimizing large scale ingestion of analytics events and logs
- Data processing pipeline
- Working with encryption keys
Questions and Answers.
Márton is a Google Developer Expert(GDE) senior software architect at REEA.net . A romanian hero on StackOverflow with 175k reputation points. He led the implementation of complex and distributed systems serving millions of users for companies like FreeLogoServices, LogoMix, WaterSmart, Ausschreibungsdienste and many more.
Active contributor for open-source solutions like Beanstalkd console, and Riak admin interface. Expert in databases and systems like Google BigQuery, Elasticsearch, Redis.
Full speaker bio: https://kodokmarton.com/speaker/