In today’s world of exploding big and fast data, developers who want both streaming analytics and ad hoc, OLAP-like analysis have often had to develop complex architectures such as Lambda—a path for fast streaming analytics using NoSQL stores such as Cassandra and HBase with a separate batch path involving HDFS and Parquet. While this approach works, it involves too many moving parts, too many technologies for ops, and too many engineering hours. Helena Edelson and Evan Chan highlight a much simpler approach to combine streaming and ad hoc/batch analysis using what they call the NoLambda stack (Apache Spark/Scala, Mesos, Akka, Cassandra, Kafka), plus FiloDB, a new entrant to the distributed-database world that combines streaming and ad hoc analytics.
- Modern streaming and batch/ad-hoc architectures
- Precise and scalable streaming ingestion using Apache Kafka, Akka, Spark Streaming, Cassandra, and FiloDB
- How a unified streaming + batch stack can lower your TCO
- What FiloDB is and how it enables fast analytics with competitive storage cost
- Use cases involving time series, smart cities, and event data
- Machine learning using Spark MLLib—without the need to export to HDFS
- Combining streaming and historical/ad-hoc data analysis, including efficient longer-time window analysis
Evan’s talk is now available on the Chariot Solutions site.
Tags: akka, big data, frameworks, kafka, spark, stream processing
Location: Salon A
April 11th, 2016
1:30 PM - 2:30 PM