optimising ecommerce with machine learning & game theory — cassandra, elasticsearch and spark

Optimising ExperiencesMachine learning, Cassandra,

Elasticsearch and Spark

Joe Chittenden-VealJamie Turner

You won’t have heard of us but you will have used us!

5.5bn, 1500tps, 15m

ExperienceUltimate differentiator

Hard to scaleHard to maintain

Expensive

Tin v SkinCost

CapacityConsistency

ContextCoverageCompassion

TriggarInternal projectTraditional stack

Small dataExternal potential

Sensors

Games

Interventions

Problem

Volume Velocity Variety

Options

CouchDB, Riak, Redis, Hbase, CouchBase, Neo4j, Dynamo, XAP, Aerospike, BigTable,

Keyspace, LevelDB, Accumulo…

MySQLMongoDBCassandra

Research.NET friendly?Test Test TestAsk Ask Ask

We help organisations become more data driven through data science and the adoption of new generation big data

technologies rapidly and at low risk.

Discover, Develop, Deliver, Train, Support

PotentialWhy build the

games manually when we can use

ML?

Why not blend Cassandra’s speed with Elasticsearch?

Why not use Spark and Spark

Streaming over Hadoop?

SolutionCassandra

ElasticsearchSpark.NET

Final thoughtsWe’ve been spoilt

Real engineering choicesBig impact

Thanks for listening!