Data Science on the Google Cloud Platform, 2nd Edition
by Valliappa Lakshmanan
Data Science on the Google Cloud Platform, 2nd Edition
- 1. Making Better Decisions Based on Data
- Many Similar Decisions
- The Role of Data Scientists
- Scrappy Environment
- Full Stack Cloud Data Scientists
- Collaboration
- Target audience for the book
- Best Practices
- Simple to Complex Solutions
- Cloud Computing
- Serverless
- A Probabilistic Decision
- Probabilistic Approach
- Probability Density Function
- Cumulative Distribution Function
- Data and Tools
- Getting Started with the Code
- Summary
- 2. Ingesting Data into the Cloud
- Airline On-Time Performance Data
- Knowability
- Training–Serving Skew
- Downloading Data
- Hub and Spoke Architecture
- Dataset Fields
- Separation of Compute and Storage
- Scaling Up
- Scaling Out with Sharded Data
- Scaling out with Data in Situ
- Ingesting Data
- Reverse Engineering a Web Form
- Dataset Download
- Exploration and Cleanup
- Uploading Data to Google Cloud Storage
- Loading Data into Google BigQuery
- Advantages of a Serverless Columnar Database
- Staging on Cloud Storage
- Access Control
- Ingesting CSV Files
- Partitioning
- Scheduling Monthly Downloads
- Ingesting in Python
- Cloud Run
- Securing Cloud Run
- Deploying and Invoking Cloud Run
- Scheduling Cloud Run
- Summary
- Code Break
- 3. Creating Compelling Dashboards
- Explain Your Model with Dashboards
- Why Build a Dashboard First?
- Accuracy, Honesty, and Good Design
- Loading Data into Cloud SQL
- Create a Google Cloud SQL Instance
- Create Table of Data
- Interacting with the database
- Querying Using BigQuery
- Schema Exploration
- Using Preview
- Using Table Explorer
- Creating BigQuery View
- Building Our First Model
- Contingency Table
- Threshold Optimization
- Building a Dashboard
- Getting Started with Data Studio
- Creating Charts
- Adding End-User Controls
- Showing Proportions with a Pie Chart
- Explaining a Contingency Table
- Summary
- 4. Streaming Data: Publication and Ingest with Pub/Sub and Dataflow
- Designing the Event Feed
- Transformations Needed
- Architecture
- Getting airport information
- Sharing data
- Time Correction
- Apache Beam/Cloud Dataflow
- Parsing Airports Data
- Adding Time Zone Information
- Converting Times to UTC
- Correcting Dates
- Creating Events
- Reading and Writing to the Cloud
- Running the Pipeline in the Cloud
- Publishing an Event Stream to Cloud Pub/Sub
- Speed-up Factor
- Get Records to Publish
- Iterating Through Records
- Building a Batch of Events
- Publishing a Batch of Events
- Real-Time Stream Processing
- Streaming in Dataflow
- Windowing a pipeline
- Streaming aggregation
- Using Event Timestamps
- Executing the Stream Processing
- Analyzing Streaming Data in BigQuery
- Real-Time Dashboard
- Summary
-
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.