As a warm-up to Spark Summit West in San Francisco (June 6-8), we’ve added a new project to Cloudera Labs that makes building Spark Streaming pipelines considerably easier.
Spark Streaming is the go-to engine for stream processing in the Cloudera stack. It allows developers to build stream data pipelines that harness the rich Spark API for parallel processing, expressive transformations, fault tolerance, and exactly-once processing. But it requires a programmer to write code,
Learn how to read FIX message files directly with Hive, create a view to simplify user queries, and use a flattened Apache Parquet table to enable fast user queries with Impala.
The Financial Information eXchange (FIX) protocol is used widely by the financial services industry to communicate various trading-related activities. Each FIX message is a record that represents an action by a financial party, such as a new order or an execution report.
Did you know that using the Crunch API is a powerful option for doing time-series analysis?
Apache Crunch is a Java library for building data pipelines on top of Apache Hadoop. (The Crunch project was originally founded by Cloudera data scientist Josh Wills.) Developers can spend more time focused on their use case by using the Crunch API to handle common tasks such as joining data sets and chaining jobs together in a pipeline.