Comparing Spark Streaming, Flink, And Kafka Streams

Shivangi Gupta contrasts three streaming technologies:

Flink and Spark are in-memory databases that do not persist their data to storage. They can write their data to permanent storage, but the whole point of streaming is to keep it in memory, to analyze current data. All of this lets programmers write big data programs with streaming data. They can take data in whatever format it is in, join different sets, reduce it to key-value pairs (map), and then run calculations on adjacent pairs to produce some final calculated value. They also can plug these data items into machine learning algorithms to make some projection (predictive models) or discover patterns (classification models).

Streaming has become the product-level battleground in the Hadoop ecosystem, and it’s interesting to see the different approaches that different groups have taken.

Related Posts

It’s All ETL (Or ELT) In The End

Robin Moffatt notes that ETL (and ELT) doesn’t go away in a streaming world: In the past we used ETL techniques purely within the data-warehousing and analytic space. But, if one considers why and what ETL is doing, it is actually a lot more applicable as a broader concept. Extract: Data is available from a source system Transform: We […]

Read More

Flint: Time Series With Spark

Li Jin and Kevin Rasmussen cover the concepts of Flint, a time-series library built on Apache Spark: Time series analysis has two components: time series manipulation and time series modeling. Time series manipulation is the process of manipulating and transforming data into features for training a model. Time series manipulation is used for tasks like data […]

Read More

Categories

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