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Azure Stream Analytics VS Amazon EMR

Compare Azure Stream Analytics VS Amazon EMR and see what are their differences

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

Azure Stream Analytics logo Azure Stream Analytics

Azure Stream Analytics offers real-time stream processing in the cloud.

Amazon EMR logo Amazon EMR

Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
  • Azure Stream Analytics Landing page
    Landing page //
    2023-01-21
  • Amazon EMR Landing page
    Landing page //
    2023-04-02

Azure Stream Analytics videos

+ Add

Azure Stream Analytics

More videos:

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

Amazon EMR videos

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Amazon EMR Masterclass

More videos:

  • Review - Deep Dive into What’s New in Amazon EMR - AWS Online Tech Talks
  • Tutorial - How to use Apache Hive and DynamoDB using Amazon EMR

Category Popularity

0-100% (relative to Azure Stream Analytics and Amazon EMR)

Azure Stream Analytics

Amazon EMR

Stream Processing
100 100%
Stream Processing
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
Data Management
100 100%
Data Management
0% 0
Big Data
12 12%
Big Data
88% 88

User comments

Share your experience with using Azure Stream Analytics and Amazon EMR. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Amazon EMR seems to be more popular. It has been mentiond 10 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.

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.

Amazon EMR mentions (10)

  • 5 Best Practices For Data Integration To Boost ROI And Efficiency
    There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
  • What compute service i should use? Advice for a duck-tape kind of guy
    I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
  • Processing a large text file containing millions of records.
    This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago
  • How to use Spark and Pandas to prepare big data
    Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
  • Beginner building a Hadoop cluster
    Check out https://aws.amazon.com/emr/. Source: about 2 years ago
View more

What are some alternatives?

When comparing Azure Stream Analytics and Amazon EMR, you can also consider the following products

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

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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.

Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost

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