Learn about how Move.com uses BIG Data to support Realtor.com operations and continued innovation.

1. Big Data at
Move
The past, present and future of Big
Data at Move - Realtor.com

2. About me
• Data Warehouse Architect
• Move Inc (realtor.com)
• Pluralsight Author
• Passion for data and technology
Ahmad Alkilani
linkedin.com/in/ahmadalkilani
EASkills.com

3. Topics…
• History of Move’s enterprise data warehouse
• Why Hadoop found a home at Move
• High level architecture
• Where we are now
• Where we’re heading in the future
• Q & A

4. Move Inc.
• Leader in online real estate and operator of realtor.com
• Over 410 million minutes per month on Move websites
• Over 300 million user engagement events per day on realtor.com and
mobile apps
• Connecting consumers and customers requires lots of data

6. growth…
-
1,000,000,000
2,000,000,000
3,000,000,000
4,000,000,000
5,000,000,000
6,000,000,000
7,000,000,000
Raw Events Move Inc. (Realtor.com and Mobile)

7. proactive...
• Transitioned from legacy warehouse and ETLs
• Near real-time collection

8. • Bigger servers
• 8 processors 10 core each
• 2 TB of RAM!
• Solid state drives
• Fusion IO cards
• 10 Terabytes each server
• Worked Great!
• Until we realized we could only store 50 days worth of data!
reactive …
-
1
2
3
4
5
6
7
Billions
Raw Events Move Inc. (Realtor.com and Mobile)

9. • Started with 13 nodes at a fraction of the cost of our SSD monster servers - Cost
• Plan to continue to scale out – Ease of scalability
• Current capacity is ~125 TB – Good starting point
proactive …

10. Big picture…

11. In more details…
Hive over HCatalog
Transferred to HDFS and
then the Hive Warehouse
HDFS
External
Tables against
data in HDFS
Data moves to
Hive
Warehouse
Dynamic
Partition
Inserts
• Partition Pruning
• Snappy Compression
• Dynamic Tables with
Maps and Arrays

12. ETL & Querying Hive…
Hive Warehouse
Aggregates
SQL
Server
(EDW)
Multi-Inserts
Single Pass
Details
Stats

13. ETL & Querying Hive…
Separate files for different keys of a Map
• Resort to MapReduce instead of Hive and use MultipleOutputs class
• Dynamic Partition Inserts again & Hadoop -getmerge

14. Some lessons learned…
• Our ETLs are still expensive
• Putting our data loads and cluster at the mercy of our analysts. Not a very good idea
• Use Queues to guarantee room for ETLs to do their job
• Default queue is for users
• Specialized queue is for ETL
• Keep an eye on the slots available
• Use .hiverc file to automatically control behavior

15. Where we’re headed
• Re-evaluate tool selection
• Talend/Pentaho
• Real-time analytics
• Kafka/Honu/Flume/Storm/StreamInsight
• Hive Geospatial
• Integrating different technologies is OK

16. D3.js with Asp.Net SignalR
Visualizing search activity and active listings in different states

17. Questions?