VoxxedDays Bucharest 2017 - Powering interactive data analysis with Google BigQuery

Powering Interactive Data Analysis
with Google BigQuery
Márton Kodok / @martonkodok
Google Developer Expert at REEA
March 2017 - Bucharest, Romania

● Geek. Hiker. Do-er.
● Among the Top3 romanians on Stackoverflow
● Google Developer Expert on Cloud technologies
● Crafting Web/Mobile backends at REEA.net
● BigQuery and database engine expert
● Active in mentoring
Twitter: @martonkodok
StackOverflow: pentium10
Slideshare: martonkodok
GitHub: pentium10
Powering Interactive Data Analysis with Google BigQuery @martonkodok
About me

Agenda
The
Challenge
Powering interactive
Data Analysis/Reporting system
Architecture
Overview
Strategy &
Tricks
Winning
Solution

❏ Need backend/database to STORE, QUERY, EXTRACT data
❏ Deep analytics - large, multi-source, complex, unstructured
❏ Be real time
❏ Terabyte scale
❏ Cost effective
❏ Run Ad-Hoc reports - as the occasion requires
❏ Without Developer - interactive
❏ Minimal engineering efforts
❏ Support streaming - data is generated on a continual basis
❏ Withstand #BlackFriday
❏ Simple Query language (prefered SQL / Javascript)
The Challenge

“We can't solve problems by
using the same kind of
thinking we used when we
created them”
-Albert Einstein
The Challenge

Legacy Business Reporting System
Web
Mobile
Web Server
Database
SQL
Cached
Platform Services
CMS/Framework
Report & Share
Business Analysis
Scheduled
Tasks
Batch Processing
Compute Engine
Multiple Instances

Web
Mobile
Web Server
Database
SQL
Cached
Platform Services
CMS/Framework
Report & Share
Business Analysis
Scheduled
Tasks
Batch Processing
Compute Engine
Multiple Instances
BehindtheScenes:
DaysToInsights

Legacy Business Reporting System
Web
Mobile
Web Server
Database
SQL
Cached
Platform Services
CMS/Framework
Report & Share
Business Analysis
Scheduled
Tasks
Batch Processing
Compute Engine
Multiple Instances
Minutes
to kick in
Hours to Run
Batch Processing
Hours to Clean and
Aggregate
DAYS TO
INSIGHTS

● Terabyte scalable storage
● Real-time row ingestion
● Ask sophisticated queries
● Query-performance
● Low-maintenance
● Cost effective
● Wire them up easily
Goal: Store everything accessible by SQL immediately.
Desired system/platform Equipment strategy
* people still required
Engines:
● MongoDB, Riak, Redis
● ELK Stack (Elasticsearch-Logstash-Kibana)
● Cassandra, Hive, Hadoop...
● Amazon Athena, Google BigQuery...
In-House Hosted Managed

● Analytics-as-a-Service - Data Warehouse in the Cloud
● Fully-Managed by Google (US or EU zone)
● Scales into Petabytes
● Ridiculously fast
● Decent pricing (queries $5/TB, storage: $20/TB cold: $10/TB) *Feb 2017
● Open Interfaces (Web UI, BQ command line tool, REST, ODBC)
● SQL 2011 + Javascript UDF (User Defined Functions)
● Familiar DB Structure (table, views, record, nested, JSON)
● Integrates with Google Sheets + Google Cloud Storage + Pub/Sub connectors
● Client libraries available in YFL (your favorite languages)
What is BigQuery?

● Columnar storage (max 10 000 columns in table)
● Rich SQL 2011: JSON, IP, Math, RegExp, Window functions
● Data types: String, Integer, Float, Boolean, Timestamp,
Record, Nested, Struct, Array.
● Append-only tables prefered (DML syntax available)
● Batch load file size limits: 5TB (CSV or JSON)
● User Defined Functions in SQL or Javascript
● Day partitioned tables
● ACL - row level locking (individual or group based)
BigQuery: Convenience of SQL

* 1 Petabyte storage, 10 TB inserts, 100 TB queries => $22000
Queries Storage Ingestion
➔ 1 TB per month free
➔ 5 USD per TB
➔ only pay for the columns you use
in your query
➔ 20 USD per TB frequently accessed
data
➔ 10 USD per TB long term storage
90 days
➔ Batch load free (CSV/JSON)
➔ Exporting free
➔ Table copy free
➔ Streaming 50 USD per TB
Estimate 1
- Storage 5 TB
- Streaming Inserts 1 TB
- Queries 3 TB
Monthly total: $165
Estimate 2
- Storage 25 TB
- Streaming Inserts 1 TB
- Queries 50 TB
Monthly total: $788
BigQuery Costs - February 2017

Architecting for The Cloud
BigQuery
On-Premises Servers
Pipelines
ETL
Engine
Event Sourcing
Frontend
Platform Services
Metrics / Logs/
Streaming

Access to Insights without Developer support
Analytics Backend
BigQuery
On-Premises Servers
Pipelines
ETL
Engine
Event Sourcing
Frontend
Platform Services
Metrics / Logs/
Streaming
Development
Team
Data Analysts
Report & Share
Business Analysis
Tools
Tableau
QlikView
Data Studio
Internal
Dashboard
Database
SQL

Data Pipeline Integration
Analytics Backend
BigQuery
On-Premises Servers
Pipelines ETL
Database
SQL
Standard
Devices
HTTPS
Ingest
Events
Monitoring
Logging
FluentD
Cloud
Storage
Report & Share
Business Analysis
Firebase
archive
Load
Export
Replay
Application
ServersServers

<filter frontend.user.*>
@type record_transformer
enable_ruby
remove_keys host
<record>
bq {"insert_id":"${uid}","host":"${host}","created":"${time.to_i}"}
</record>
</filter>
<match frontend.user.*>
@type copy
<store>
@type forest
subtype file
<template>
path /tank/storage/${tag}.*.log
time_slice_format %Y%m%d
time_slice_wait 10m
</template>
</store>
<store>
@type bigquery
method insert
...
</store>
</match>
….bigquery section continued….
auth_method json_key
json_key /etc/td-agent/keys/key-31da042be48c.json
project project_id
dataset dataset_name
time_field timestamp
time_slice_format %Y%m%d
table user$%{time_slice}
ignore_unknown_values
schema_path /etc/td-agent/schema/user_login.json
1
2
3
4

● On data that it is difficult to process/analyze using traditional databases
● On exploring unstructured data
● Not a replacement to traditional DBs, but it compliments the system
● Applying Javascript UDF on columnar storage to resolve complex tasks
(natural lang. processing)
● On streams (form wizard ...)
● On IoT streams
● Major strength is handling Large datasets
Where to use BigQuery?

1. Select or create a Cloud Platform project.
https://cloud.google.com/bigquery/
2. Enable billing for your project.
3. BigQuery is automatically enabled in new projects.
To activate BigQuery in a pre-existing project, go to Enable the BigQuery API.
Quickstart Using the Web UI

Go to the BigQuery web UI.
https://bigquery.cloud.google.com/
Query a public dataset

Romanian stations that record the most days of snow

Mentions of RO politicians since ‘16 Nov in GDELT articles

● Funnel Analysis
Achievements

Funnel analysis: Time on upsell pages

Example HITS chain:
● article1 -> page2 -> page3 -> page4 -> orderpage1 -> thankyoupage1
● page1 -> article2-> page3 -> orderpage2 -> ...
Attribute credit to first article visited on purchase

● Funnel Analysis
● Email URL click heatmap
Achievements

Email URL clicks heat-map

● Funnel Analysis
● Email URL click heatmap
● Email Health Dashboard (SPAM, ISP deferral, content
A/B split tests, trends or low open rate campaigns)
● Advanced segmentation (all raw data stored)
● Behavioral analytics - engaged users etc...
Achievements Continued

● no provisioning/deploy
● no running out of resources
● no more focus on large scale execution plan
● no need to re-implement tricky concepts
(time windows / join streams)
● pay only the columns we have in your queries
● run raw ad-hoc queries (either by analysts/sales or Devs)
● no more throwing away-, expiring-, aggregating old data.
Our benefits

● No manual sharding
● No capacity guessing
● No idle resources
● No maintenance windows
● No manual scaling
● No file mgmt
BigQuery: Serverless Data Warehouse

BigQuery: Sample projects to try out 1

BigQuery: Sample projects to try out 2

HttpArchive - multiple JS frameworks

HttpArchive - multiple jQuery versions

Easily Build Custom Reports and Dashboards

Training courses for Google Cloud Platform now on Coursera!

Questions?
Thank you.
Slides available on: slideshare.net/martonkodok

VoxxedDays Bucharest 2017 - Powering interactive data analysis with Google BigQuery

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VoxxedDays Bucharest 2017 - Powering interactive data analysis with Google BigQuery