Software Alternatives, Accelerators & Startups

Glew.io VS Apache Kudu

Compare Glew.io VS Apache Kudu and see what are their differences

Glew.io logo Glew.io

Generate more revenue, cultivate loyal customers, and optimize product strategy with our advanced ecommerce analytics software. Start your free trial today!

Apache Kudu logo Apache Kudu

Apache Kudu is Hadoop's storage layer to enable fast analytics on fast data.
  • Glew.io Landing page
    Landing page //
    2022-07-27
  • Apache Kudu Landing page
    Landing page //
    2021-09-26

Glew.io 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

0-100% (relative to Glew.io and Apache Kudu)
eCommerce
100 100%
0% 0
Technical Computing
0 0%
100% 100
Analytics
100 100%
0% 0
Office & Productivity
0 0%
100% 100

User comments

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What are some alternatives?

When comparing Glew.io and Apache Kudu, you can also consider the following products

Databox - Databox is Business Analytics platform that helps companies deliver insights and analytics anytime and anywhere.

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

Polar Analytics - Your #1 Analytics for Ecommerce — Centralize Ecommerce data and create custom reports + metrics without coding. Try it free.

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.

Triple Whale - Triple Whale helps ecommerce brands make better decisions with better data.

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.