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Apache Kudu VS Apache Parquet

Compare Apache Kudu VS Apache Parquet and see what are their differences

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Apache Kudu logo Apache Kudu

Apache Kudu is Hadoop's storage layer to enable fast analytics on fast data.

Apache Parquet logo Apache Parquet

Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.
  • Apache Kudu Landing page
    Landing page //
    2021-09-26
  • Apache Parquet Landing page
    Landing page //
    2022-06-17

Apache Kudu

Categories
  • Business & Commerce
  • Data Dashboard
  • Office & Productivity
  • Technical Computing
Website kudu.apache.org
Details $-
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Apache Parquet

Categories
  • Databases
  • Big Data
  • Relational Databases
  • NoSQL Databases
Website parquet.apache.org
Details $
Edit details

Apache Kudu videos

+ Add

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

Apache Parquet videos

No Apache Parquet videos yet. You could help us improve this page by suggesting one.

+ Add video

Category Popularity

0-100% (relative to Apache Kudu and Apache Parquet)

Apache Kudu

Apache Parquet

Technical Computing
100 100%
Technical Computing
0% 0
Databases
0 0%
Databases
100% 100
Office & Productivity
100 100%
Office & Productivity
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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Social recommendations and mentions

Based on our record, Apache Parquet seems to be more popular. It has been mentiond 19 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 Kudu mentions (0)

We have not tracked any mentions of Apache Kudu yet. Tracking of Apache Kudu recommendations started around Mar 2021.

Apache Parquet mentions (19)

  • [D] Is there other better data format for LLM to generate structured data?
    The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's delta.io over json. Source: 4 months ago
  • Demystifying Apache Arrow
    Apache Parquet (Parquet for short), which nowadays is an industry standard to store columnar data on disk. It compress the data with high efficiency and provides fast read and write speeds. As written in the Arrow documentation, "Arrow is an ideal in-memory transport layer for data that is being read or written with Parquet files". - Source: dev.to / 11 months ago
  • Parquet: more than just "Turbo CSV"
    Googling that suggests this page: https://parquet.apache.org/. Source: about 1 year ago
  • Beginner question about transformation
    You should also consider distribution of data because in a company that has machine learning workflows, the same data may need to go through different workflows using different technologies and stored in something other than a data warehouse, e.g. Feature engineering in Spark and loaded/stored in binary format such as Parquet in a data lake/object store. Source: about 1 year ago
  • Pandas Free Online Tutorial In Python — Learn Pandas Basics In 5 Lessons!
    This section will teach you how to read and write data to and from a variety of file types, including CSV, Excel, SQL, HTML, Parquet, JSON etc. You’ll also learn how to manipulate data from other sources, such as databases and web sites. Source: about 1 year ago
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What are some alternatives?

When comparing Apache Kudu and Apache Parquet, you can also consider the following products

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

Apache ORC - Apache ORC is a columnar storage for Hadoop workloads.

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.

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

Apache Arrow - Apache Arrow is a cross-language development platform for in-memory data.

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