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Spark Streaming VS Azure Stream Analytics

Compare Spark Streaming VS Azure Stream Analytics and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

Spark Streaming logo Spark Streaming

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

Azure Stream Analytics logo Azure Stream Analytics

Azure Stream Analytics offers real-time stream processing in the cloud.
  • Spark Streaming Landing page
    Landing page //
    2022-01-10
  • Azure Stream Analytics Landing page
    Landing page //
    2023-01-21

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

Azure Stream Analytics videos

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Azure Stream Analytics

More videos:

  • Review - Real-time Analytics with Azure Stream Analytics
  • Demo - Introduction to Azure Stream Analytics + Demo

Category Popularity

0-100% (relative to Spark Streaming and Azure Stream Analytics)

Spark Streaming

Azure Stream Analytics

Stream Processing
54 54%
Stream Processing
46% 46
Data Management
60 60%
Data Management
40% 40
Big Data
62 62%
Big Data
38% 38
Analytics
49 49%
Analytics
51% 51

User comments

Share your experience with using Spark Streaming and Azure Stream Analytics. 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

Azure Stream Analytics mentions (0)

We have not tracked any mentions of Azure Stream Analytics yet. Tracking of Azure Stream Analytics recommendations started around Mar 2021.

What are some alternatives?

When comparing Spark Streaming and Azure Stream Analytics, 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.

PieSync - Seamless two-way sync between your CRM, marketing apps and Google in no time

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.

Kibana - Easily visualize data pushed into Elasticsearch from Logstash, es-hadoop or 3rd party technologies...

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