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Apache Apex VS Spark Streaming

Compare Apache Apex VS Spark Streaming and see what are their differences

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Apache Apex logo Apache Apex

Apache Apex is an enterprise-grade unified stream and batch processing engine.

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
  • Apache Apex Landing page
    Landing page //
    2021-09-30
  • Spark Streaming Landing page
    Landing page //
    2022-01-10

Apache Apex videos

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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 Apache Apex and Spark Streaming)

Apache Apex

Spark Streaming

Big Data
22 22%
Big Data
78% 78
Stream Processing
0 0%
Stream Processing
100% 100
Data Warehousing
100 100%
Data Warehousing
0% 0
Data Management
0 0%
Data Management
100% 100

User comments

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Social recommendations and mentions

Based on our record, Spark Streaming should be more popular than Apache Apex. 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.

Apache Apex mentions (1)

  • Spark for beginners - and you
    Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 2 years ago

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 Apache Apex and Spark Streaming, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

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 Storm - Apache Storm is a free and open source distributed realtime computation system.

Snowplow - Snowplow is an enterprise-strength event analytics platform.

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