For those who feel they are relatively competent in Google BigQuery I would encourage you to join Google Cloud and Kaggle to really up your ante in Google BigQuery. Your task is to predict congestion, based on an aggregate measure of stopping distance and waiting times, at intersections in 4 major US cities: Atlanta, Boston, Chicago & Philadelphia.
Here is the official announcement.
For this competition it is focused on Machine Learning.
To sign up follow the link.
When you sign up make sure you select the “Rules” and accept otherwise the submit button is not highlighted.
The competition details. You’ll find a dataset from Geotab as your starting point. We’re excited to partner with Geotab, which provides a variety of aggregate datasets gathered from commercial vehicle telematics devices.
The dataset for this competition includes aggregate stopped vehicle information and intersection wait times, gathered from trip logging metrics from vehicles like semi-trucks. The data have been grouped by intersection, month, hour of day, direction driven through the intersection, and whether the day was on a weekend or not.
Your task is to predict congestion based on an aggregate measure of stopping distance and waiting times at intersections in four major U.S. cities: Atlanta, Boston, Chicago and Philadelphia.
The Prizes are Google Cloud Platform Credits. Yep, I know not exactly going to pay for Christmas or whatever holiday you celebrate. Its the recognition right?
Information regarding prizes posted on the contest website.
BIGQUERY ML AWARDS: $10,000 GCP CREDIT COUPONS
1. BQML MODELS BUILT IN SQL
- First Prize: $3,000 IN GCP CREDITS
- Second Prize: $1,000 IN GCP CREDITS
- Third Prize: $1,000 IN GCP CREDITS
2. BQML MODELS BUILT IN TENSORFLOW
- First Prize: $3,000 IN GCP CREDITS
- Second Prize: $1,000 IN GCP CREDITS
- Third Prize: $1,000 IN GCP CREDITS
WINNER LICENSE TYPE: No License Required
DATA ACCESS AND USE: Competition Use Only
EXTERNAL DATA: Publicly, freely available external data is permitted, if posted to the official competition forum prior to the Entry Deadline
Competitions are open to residents of the United States and worldwide, except that if you are a resident of Crimea, Cuba, Iran, Syria, North Korea, Sudan, or are subject to U.S. export controls or sanctions, you may not enter the Competition. Other local rules and regulations may apply to you, so please check your local laws to ensure that you are eligible to participate in skills-based competitions.
Thinking of taking the Google Cloud Developer Certification. Check out my practice questions here on Udemy
Designing a Cloud Bigtable schema is very different than designing a schema for a relational database. Cloud Bigtable reads rows atomically and you need to limit the total amount of data that you store in a single row.
What are two best practices when designing your BigQuery data schema? (Select Two)
a. Store a maximum of 10 MB in a single cell and 100 MB in a single row.
b. For performance Cloud Bigtable queries use the row key, a row key prefix, or a row range to retrieve the data.
c.Use JavaScript user-defined functions
d.For queries that join data from multiple tables, optimize your join patterns. Start with the smallest table
Correct Answers are A and B.
Quick Note..
Query Service vs Data Warehouse
•BigQuery is what you use when you have collected a large amount of data, and need to ask questions about it. (It’s Serverless)
•BigTable is a database which is designed to be the foundation for a large, scalable application.
•Use BigQuery when you have collected a large amount of data, and then need to ask questions about it (Query).
•Use BigTable when you are making any kind of app that needs to read and write data, and scale is a potential issue.(Warehouse)
Know how to ensure performance (reduce latency) in queries, know how to connect to services, load data from various sources, validate Query
So what are you waiting for. Join in now.
Joe Holbrook, The Cloud Tech Guy – MyBlockchainExperts
