BigQuery Overview Typical Uses Project Hierarchy Access Control and Security Datasets and Tables Tools Demos

1. BigQuery Basics

2. Who? Why?
BigQuery Basics
Ido Green
Developer Advocate
plus.google.com/greenido
greenido.wordpress.com

3. Topics we cover
● BigQuery Overview
● Typical Uses
● Project Hierarchy
○ Access Control and Security
○ Datasets and Tables
● Tools
● Demos
BigQuery Basics

4. ● MapReduce based analysis can be slow for ad-hoc queries
● Managing data centers and tuning software takes time & money
● Analytics tools should be services
How does BigQuery fit in the analytics landscape?
BigQuery Basics

5. Why BigQuery?
● Generate big data reports require expensive servers
and skilled database administrators
● Interacting with big data has been expensive, slow and
inefficient
● BigQuery changes all that
○ Reducing time and expense to query data
BigQuery Basics

6. What's BigQuery?
● Service for interactive analysis of massive datasets (TBs)
○ Query billions of rows: seconds to write, seconds to return
○ Uses a SQL-style query syntax
○ It's a service, accessed by a RESTful API
● Reliable and secure
○ Replicated across multiple sites
○ Secured through Access Control Lists
● Scalable
○ Store hundreds of terabytes
○ Pay only for what you use
● Fast (really)
○ Run ad hoc queries on multi-terabyte data sets in seconds
BigQuery Basics

7. Analyzing Large Amount of Data
.....at high speed
BigQuery Basics
demobigquery.appspot.com

8. Uses

9. Typical Uses
Analyzing query results using a visualization library such as Google
Charts Tools API
BigQuery Basics

10. Typical Uses
Another way to analyze query results with Google Spreadsheets
○ greenido.wordpress.com/2013/12/16/big-query-and-google-spreadsheet-intergration/
○ greenido.wordpress.com/2013/07/24/big-query-power-with-javascript/
BigQuery Basics

11. BigQuery Use Cases
● Log Analysis - Making sense of computer generated records
● Retailer - Using data to forecast product sales
● Ads Targeting - Targeting proper customer sections
● Sensor Data - Collect and visualize ambient data
● Data Mashup - Query terabytes of heterogeneous data
BigQuery Basics

12. Some Customer Case Studies
Uses BigQuery to hone ad targeting
and gain insights into their business
Dashboards using BigQuery to
analyze booking and inventory data
Use BigQuery to provide their
customers ways to expand game
engagement and find new channels for
monetization
Used BigQuery, App Engine and the
Visualizaton API to build a business
intelligence solution
BigQuery Basics

13. BigQuery Basic Technical Details

14. Project Hierarchy
● Project
○ All data in BigQuery belongs inside a project
○ Set of users, APIs, authentication, billing information and ACL
● Dataset
○ Holds one or more tables
○ Lowest access control unit (to which ACLs are applied)
● Table
○ Row-column structure that contains actual data
● Job
○ Used to start
potentially long
running queries
BigQuery Basics

15. Datasets and Tables
Table name is represented as
follows:
● Current Project
<dataset>.<table
name>
● Different Project
<project>:<dataset>.<table>
e.g. publicdata:samples.wikipedia
BigQuery Basics

16. Schema Example
● Demographics about names occurrence table schema
name:string,gender:string,count:integer
BigQuery Basics

17. Data Types
● String
○ UTF-8 encoded, <64kB
● Integer
○ 64 bit signed
● Float
● Boolean
○ "true" or "false", case insensitive
● Timestamp
○ String format
■ YYYY-MM-DD HH:MM:SS[.sssss] [+/-][HH:MM]
○ Numeric format (seconds from UNIX epoch)
■ 1234567890, 1.234567890123456E9
(*) Max row size: 64kB
Date type is supported as timestamp
BigQuery Basics

18. Data Format
BigQuery supports the following format for loading data:
1. Comma Separated Values (CSV)
2. JSON
a. BigQuery can load data faster, if your data con
embedded newlines.
b. Supports nested/repeated data fields
BigQuery Basics

19. Loading data with
repeated and nested
fields is supported by
JSON data format only
Repeated and Nested Fields
BigQuery Basics
[
{
"fields": [
{
"mode": "nullable",
"name": "country",
"type": "string"
},
{
"mode": "nullable",
"name": "city",
"type": "string"
}
],
"mode": "repeated",
"name": "location",
"type": "record"
},
...........
[
{
"fields": [
{
"mode": "nullable",
"name": "country",
"type": "string"
},
{
"mode": "nullable",
"name": "city",
"type": "string"
}
],
"mode": "repeated",
"name": "location",
"type": "record"
},
...........
Schema
example

20. Accessing BigQuery
● BigQuery Web browser
○ Imports/exports data, runs
queries
● bq command line tool
○ Performs operations from
the command line
● Service API
○ RESTful API to access
BigQuery programmatically
○ Requires authorization by
OAuth2
○ Google client libraries for
Python, Java, JavaScript,
PHP, ...
○
BigQuery Basics

21. Third-party Tools
BigQuery Basics
Visualization and Business Intelligence
ETL tools for loading data into BigQuery

22. Example of Visualization Tools
Using commercial visualization tools to graph the query results
BigQuery Basics

23. Loading Data Using the Web Browser
● Upload from local disk or from Cloud Storage
● Start the Web browser
● Select Dataset
● Create table and follow the wizard steps
BigQuery Basics

24. "bq load" command
Syntax
● If not specified, the default file format is CSV (comma separated values)
● The files can also use newline delimited JSON format
● Schema
○ Either a filename or a comma-separated list of column_name:datatype
pairs that describe the file format.
● Data source may be on local machine or on Cloud Storage
Loading Data Using bq Tool
BigQuery Basics
bq load [--source_format=NEWLINE_DELIMITED_JSON|CSV]
destination_table data_source_uri table_schema

25. ● 1,000 import jobs per table per day
● 10,000 import jobs per project per day
● File size (for both CSV and JSON)
○ 1GB for compressed file
○ 1TB for uncompressed
■ 4GB for uncompressed CSV with newlines in strings
● 10,000 files per import job
● 1TB per import job
Load Limitations
BigQuery Basics

26. CSV/JSON must be split into chunks less than 1TB
● "split" command with --line-bytes option
● Split to smaller files
○ Easier error recovery
○ To smaller data unit (day, month instead of year)
● Uploading to Cloud Storage is recommended
Best Practices
Cloud Storage BigQuery
BigQuery Basics

27. ● Split Tables by Dates
○ Minimize cost of data scanned
○ Minimize query time
● Upload Multiple Files to Cloud Storage
○ Allows parallel upload into BigQuery
● Denormalize your data
Best Practices
BigQuery Basics

28. Google I/O Data Sensing
● Start the BigQuery Web browser
● Click on Display Project in the project chooser dialog window
● Enter data-sensing-lab when prompted
● In the dataset data-sensing-lab:io_sensor_data, select the table
moscone_io13
● In the New Query box, enter the following query:
SELECT * FROM [data-sensing-lab:io_sensor_data.moscone_io13] LIMIT 10
● Click Run Query button
● Scroll to see relevant results
BigQuery Basics

29. Data Structure
● Define table schema when creating table
● Data is stored in per-column structure
● Each column is handled separately and only combined when
necessary
Advantage of this data structure:
● No need to set index in advance
● Load only the relevant Columns
BigQuery Basics

30. Questions?
BigQuery Basics
Thank you!