Spark Streaming Vs Kafka Streams

Mahesh Chand Kandpal contrasts Kafka Streams with Spark Streaming:

The low latency and an easy-to-use event time support also apply to Kafka Streams. It is a rather focused library, and it’s very well-suited for certain types of tasks. That’s also why some of its design can be so optimized for how Kafka works. You don’t need to set up any kind of special Kafka Streams cluster, and there is no cluster manager. And if you need to do a simple Kafka topic-to-topic transformation, count elements by key, enrich a stream with data from another topic, or run an aggregation or only real-time processing — Kafka Streams is for you.

If event time is not relevant and latencies in the seconds range are acceptable, Spark is the first choice. It is stable and almost any type of system can be easily integrated. In addition it comes with every Hadoop distribution. Furthermore, the code used for batch applications can also be used for the streaming applications as the API is the same.

Read on for more analysis.

Related Posts

Hooking SQL Server to Kafka

Niels Berglund has an interesting scenario for us: We see how the procedure in Code Snippet 2 takes relevant gameplay details and inserts them into the dbo.tb_GamePlay table. In our scenario, we want to stream the individual gameplay events, but we cannot alter the services which generate the gameplay. We instead decide to generate the event from the database […]

Read More

Notebooks in Azure Databricks

Brad Llewellyn takes us through Azure Databricks notebooks: Azure Databricks Notebooks support four programming languages, Python, Scala, SQL and R.  However, selecting a language in this drop-down doesn’t limit us to only using that language.  Instead, it makes the default language of the notebook.  Every code block in the notebook is run independently and we […]

Read More

Categories

June 2017
MTWTFSS
« May Jul »
 1234
567891011
12131415161718
19202122232425
2627282930