Date post: | 16-Apr-2017 |
Category: |
Data & Analytics |
Upload: | pca-predict-formerly-postcode-anywhere |
View: | 1,190 times |
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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!