Software Alternatives & Reviews
Register | Login

Spark Streaming VS Spring Cloud Data Flow

Compare Spark Streaming VS Spring Cloud Data Flow and see what are their differences

Qrvey
Embedded Analytics built exclusively for SaaS applications. featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Spark Streaming logo Spark Streaming

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

Spring Cloud Data Flow logo 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.
  • Spark Streaming Landing page
    Landing page //
    2022-01-10
  • Spring Cloud Data Flow Landing page
    Landing page //
    2023-07-30

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

Spring Cloud Data Flow videos

+ Add

Orchestrate All the Things! with Spring Cloud Data Flow - Eric Bottard & Ilayaperumal Gopinathan

More videos:

  • Review - Demo: Partitioning Batch jobs with Spring Cloud Data Flow & Task
  • Demo - 3 min demo: Spring Cloud Data Flow Metrics

Category Popularity

0-100% (relative to Spark Streaming and Spring Cloud Data Flow)

Spark Streaming

Spring Cloud Data Flow

Stream Processing
83 83%
Stream Processing
17% 17
Big Data
74 74%
Big Data
26% 26
Data Management
78 78%
Data Management
22% 22
Analytics
100 100%
Analytics
0% 0

User comments

Share your experience with using Spark Streaming and Spring Cloud Data Flow. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, Spark Streaming should be more popular than Spring Cloud Data Flow. 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

Spring Cloud Data Flow mentions (1)

  • Dataflow, a self-hosted Observable Notebook Editor
    And a Cloudera project: https://www.cloudera.com/products/cdf.html And an Azure feature: https://docs.microsoft.com/en-us/azure/data-factory/control-flow-execute-data-flow-activity And a Spring feature: https://spring.io/projects/spring-cloud-dataflow. - Source: Hacker News / almost 3 years ago

What are some alternatives?

When comparing Spark Streaming and Spring Cloud Data Flow, you can also consider the following products

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

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

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

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

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.

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

Loading...