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Spark Streaming VS TIBCO BusinessEvents

Compare Spark Streaming VS TIBCO BusinessEvents and see what are their differences

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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.

TIBCO BusinessEvents logo TIBCO BusinessEvents

In the emerging digital world, billions of people, systems, and devices will interact in real-time, suggesting new and disruptive competitive advantages.
  • Spark Streaming Landing page
    Landing page //
    2022-01-10
  • TIBCO BusinessEvents Landing page
    Landing page //
    2023-01-19

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

TIBCO BusinessEvents videos

No TIBCO BusinessEvents videos yet. You could help us improve this page by suggesting one.

+ Add video

Category Popularity

0-100% (relative to Spark Streaming and TIBCO BusinessEvents)

Spark Streaming

TIBCO BusinessEvents

Stream Processing
83 83%
Stream Processing
17% 17
Big Data
80 80%
Big Data
20% 20
Data Management
83 83%
Data Management
17% 17
Analytics
79 79%
Analytics
21% 21

User comments

Share your experience with using Spark Streaming and TIBCO BusinessEvents. 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

TIBCO BusinessEvents mentions (0)

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

What are some alternatives?

When comparing Spark Streaming and TIBCO BusinessEvents, 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.

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.

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