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Radicalbit VS Spark Streaming

Compare Radicalbit VS Spark Streaming and see what are their differences

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Radicalbit logo Radicalbit

Event Stream Processing

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
  • Radicalbit Landing page
    Landing page //
    2023-10-08
  • Spark Streaming Landing page
    Landing page //
    2022-01-10

Radicalbit videos

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Data intensive applications with Apache Flink - Simone Robutti, Radicalbit

Spark Streaming videos

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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

Category Popularity

0-100% (relative to Radicalbit and Spark Streaming)

Radicalbit

Spark Streaming

Big Data
20 20%
Big Data
80% 80
Stream Processing
17 17%
Stream Processing
83% 83
Data Management
20 20%
Data Management
80% 80

User comments

Share your experience with using Radicalbit and Spark Streaming. 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.

Radicalbit mentions (0)

We have not tracked any mentions of Radicalbit yet. Tracking of Radicalbit recommendations started around Mar 2021.

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

What are some alternatives?

When comparing Radicalbit and Spark Streaming, 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.

Striim - Striim provides an end-to-end, real-time data integration and streaming analytics platform.

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.

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