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@PatrickMcFadin
Patrick McFadin

Chief Evangelist, DataStax
Spark and Cassandra: An amazing
Apache love story
1
Store a ton of data Analyze a ton of data
Community Response?
Cassandra
Only DC
Cassandra
Only DC
Cassandra
+ Spark DC
Spark
Jobs
Cassandra
Only DC
Cassandra
+ Spark DC
Spark
Jobs
Spark
Streaming
Worker
Worker
Worker Worker
Analytics WorkloadTransactional Workload
DataStax Enterprise
DataStax Enterprise
• 10T of high frequency event data daily
• Constant increasing volume
“The web server that powers the interface can query both
datacenters, depending on which the user is closest to,”
“A small set of signals tend to double every eight months. So
we needed a model that can scale linearly.”
- Arun Jayandra, Microsoft
REST	API
O365 Event	Hub
Ingestion	
Worker
(Azure	worker	role	
using	DataStax C#	
driver)
C* Analytics
REST	API
O365
Kafka
C*/
Spark	
Streaming
Analytics
G4	– Local	SSD
Kafka: G4	– Data	Disk
ZooKeeper: A7	– Data	Disk
PaaS	Small
G4	– Local	SSD
Cluster	1:
Cluster	2:
20k – 50k events/sec
200k+ events/sec
Data Protection
• Maximilian Schrems v Data Protection Commissioner
• No longer OK to ship EU data to US under “Safe
Harbour”
Product_Catalog RF=3
Product_Catalog RF=3 Customer_Data RF=3
Customer_Data RF=0
Product_Catalog RF=3
Customer_Data RF=3
• 300k customers
• Report on energy usage
• Predict boiler failure
“We’re dealing largely with time series data, and Spark is 10 to 100
times quicker as it is operating on data in-memory…Cassandra
delivers what we need today and if you look at the Internet of Things
space; that is what is really useful right now.” - Jim Anning, British Gas
Hive Active Heating™
Cassandra
Only DC
Cassandra
+ Spark DC
Spark
Jobs
Spark
Streaming
Home Data Center
Hive Active Heating™
Store a ton of data Analyze a ton of data
Thank you!

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