Software Alternatives & Reviews

SAS Solutions for Hadoop VS Apache Kudu

Compare SAS Solutions for Hadoop VS Apache Kudu and see what are their differences

SAS Solutions for Hadoop logo SAS Solutions for Hadoop

SAS Solutions for Hadoop is a product suite for companies managing large amounts of data on Hadoop.

Apache Kudu logo Apache Kudu

Apache Kudu is Hadoop's storage layer to enable fast analytics on fast data.
  • SAS Solutions for Hadoop Landing page
    Landing page //
    2023-09-18
  • Apache Kudu Landing page
    Landing page //
    2021-09-26

SAS Solutions for Hadoop videos

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Apache Kudu videos

Apache Kudu and Spark SQL for Fast Analytics on Fast Data (Mike Percy)

More videos:

  • Review - Apache Kudu (Incubating): New Hadoop Storage for Fast Analytics on Fast Data
  • Review - Apache Kudu: Fast Analytics on Fast Data | DataEngConf SF '16

Category Popularity

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Big Data Infrastructure
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Technical Computing
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100% 100
Big Data
100 100%
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Office & Productivity
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100% 100

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What are some alternatives?

When comparing SAS Solutions for Hadoop and Apache Kudu, you can also consider the following products

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Azure Databricks - Azure Databricks is a fast, easy, and collaborative Apache Spark-based big data analytics service designed for data science and data engineering.

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

MyAnalytics - MyAnalytics, now rebranded to Microsoft Viva Insights, is a customizable suite of tools that integrates with Office 365 to drive employee engagement and increase productivity.

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

ATLAS.ti - ATLAS.ti is a powerful workbench for the qualitative analysis of large bodies of textual, graphical, audio and video data. It offers a variety of sophisticated tools for accomplishing the tasks associated with any systematic approach to "soft" data.