Google Cloud Platform Blog
Product updates, customer stories, and tips and tricks on Google Cloud Platform
BigQuery in Practice - Loading Data Sets that are Terabytes and Beyond
Friday, January 31, 2014
We all know the story of David and Goliath. But did you know that King Saul prepared David for battle by fully arming him? He put a coat of armor and a bronze helmet on him, and gave David his sword. David tried walking around in them but they didn't feel right to him. In the end, he decided to carry five small stones and a sling instead. These were the tools that he used to fight off lions as a shepherd boy. We know what the outcome was. David showed us that picking the right tools and using them well is one of the keys to success.
Let's suppose you are tasked to start a Big Data project. You decide to use
Google BigQuery
because:
Its hosting model allows you to quickly run your data analysis without having to set up a costly computing infrastructure.
The interactive speed allows your analysts to quickly validate hypothesis about their insights.
To get started though, your Goliath is to load multi-terabytes of data into BigQuery. The technical article,
BigQuery in practice - Loading Data Sets that are Terabytes and Beyond
, is intended for IT Professionals and Data Architects who are planning to deploy large data sets to Google BigQuery. When dealing with multi-terabytes to petabytes of data, managing the processing of data such as uploading, failure recovery, cost and quota management becomes paramount.
Just as David showed us the importance of using the right tools effectively, the paper presents various options and considerations to help you to decide on the optimal solution. It follows the common ingestion workflow as depicted in the following diagram and discusses the tools that you can use during each stage - from uploading the data to the Google Cloud Storage, running your Extract Transform and Load (ETL) pipelines, to loading the data into BigQuery.
Scenarios for data ingestion into BigQuery
When dealing with large data sets the correct implementation can mean a savings of hours or days, while an improper design may mean weeks of re-work. David was so successful that King Saul gave him a high rank in the army. Similarly, we are here to help you use Google Cloud Platform successfully so your Big Data project will achieve the same level of success.
- Posted by Wally Yau, Cloud Solutions Architect
Free Trial
GCP Blogs
Big Data & Machine Learning
Kubernetes
GCP Japan Blog
Firebase Blog
Apigee Blog
Popular Posts
Understanding Cloud Pricing
World's largest event dataset now publicly available in BigQuery
A look inside Google’s Data Center Networks
Enter the Andromeda zone - Google Cloud Platform’s latest networking stack
New in Google Cloud Storage: auto-delete, regional buckets and faster uploads
Labels
Announcements
193
Big Data & Machine Learning
134
Compute
271
Containers & Kubernetes
92
CRE
27
Customers
107
Developer Tools & Insights
151
Events
38
Infrastructure
44
Management Tools
87
Networking
43
Open
1
Open Source
135
Partners
102
Pricing
28
Security & Identity
85
Solutions
24
Stackdriver
24
Storage & Databases
164
Weekly Roundups
20
Feed
Subscribe by email
Demonstrate your proficiency to design, build and manage solutions on Google Cloud Platform.
Learn More
Technical questions? Check us out on
Stack Overflow
.
Subscribe to
our monthly newsletter
.
Google
on
Follow @googlecloud
Follow
Follow