Spark Streaming On Azure Databricks

Tristan Robinson shows us how to run Spark Streaming within Azure Databricks:

Real-time stream processing is becoming more prevalent on modern day data platforms, and with a myriad of processing technologies out there, where do you begin? Stream processing involves the consumption of messages from either queue/files, doing some processing in the middle (querying, filtering, aggregation) and then forwarding the result to a sink – all with a minimal latency. This is in direct contrast to batch processing which usually occurs on an hourly or daily basis. Often is this the case, both of these will need to be combined to create a new data set.

In terms of options for real-time stream processing on Azure you have the following:

  • Azure Stream Analytics

  • Spark Streaming / Storm on HDInsight

  • Spark Streaming on Databricks

  • Azure Functions

Click through for more.

Related Posts

Machine Learning and Delta Lake

Brenner Heintz and Denny Lee walk us through solving data engineering problems with Delta Lake: As a result, companies tend to have a lot of raw, unstructured data that they’ve collected from various sources sitting stagnant in data lakes. Without a way to reliably combine historical data with real-time streaming data, and add structure to […]

Read More

Cloudera Stream Processing

Dinesh Chandrasekhar announces the new iteration of Cloudera’s streaming data processor: Cloudera Stream Processing (CSP) is a product within the Cloudera DataFlow platform that packs Kafka along with some key streaming components that empower enterprises to handle some of the most complex and sophisticated streaming use cases. CSP provides advanced messaging, real-time processing and analytics on […]

Read More

Categories

October 2018
MTWTFSS
« Sep Nov »
1234567
891011121314
15161718192021
22232425262728
293031