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Spark Streaming VS StreamSets Data Collector

Compare Spark Streaming VS StreamSets Data Collector and see what are their differences

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Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.

StreamSets Data Collector logo StreamSets Data Collector

The StreamSets Data Collector (SDC) is used to build, test and execute dataflow pipelines for data lake and multi-cloud data movement plus cybersecurity, IoT and customer 360 applications.
  • Spark Streaming Landing page
    Landing page //
    2022-01-10
  • StreamSets Data Collector Landing page
    Landing page //
    2023-10-20

Spark Streaming videos

+ Add

Spark Streaming Vs Kafka Streams || Which is The Best for Stream Processing?

More videos:

  • Tutorial - Spark Streaming Vs Structured Streaming Comparison | Big Data Hadoop Tutorial

StreamSets Data Collector videos

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Data Pipeline Preview with StreamSets Data Collector

Category Popularity

0-100% (relative to Spark Streaming and StreamSets Data Collector)

Spark Streaming

StreamSets Data Collector

Stream Processing
78 78%
Stream Processing
22% 22
Data Management
78 78%
Data Management
22% 22
Big Data
75 75%
Big Data
25% 25
Analytics
79 79%
Analytics
21% 21

User comments

Share your experience with using Spark Streaming and StreamSets Data Collector. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Spark Streaming seems to be more popular. It has been mentiond 3 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Spark Streaming mentions (3)

  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago
  • Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
    Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / over 1 year ago
  • Spark for beginners - and you
    Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / over 2 years ago

StreamSets Data Collector mentions (0)

We have not tracked any mentions of StreamSets Data Collector yet. Tracking of StreamSets Data Collector recommendations started around Mar 2021.

What are some alternatives?

When comparing Spark Streaming and StreamSets Data Collector, you can also consider the following products

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Spring Cloud Data Flow - Spring Cloud Data Flow is a platform capable of stream and batch data pipelines having the tools to create delicate topologies.

Leo Platform - Leo enables teams to innovate faster by providing visibility and control for data streams.

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