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Apache Kudu VS Apache Arrow

Compare Apache Kudu VS Apache Arrow 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 Arrow logo Apache Arrow

Apache Arrow is a cross-language development platform for in-memory data.
  • Apache Kudu Landing page
    Landing page //
    2021-09-26
  • Apache Arrow Landing page
    Landing page //
    2021-10-03

Apache Kudu features and specs

  • Fast Analytics on Fresh Data
    Kudu is designed for fast analytical processing on up-to-date data. It allows for efficient columnar storage which enables quick read and write capabilities suitable for real-time analytics.
  • Hybrid Workloads
    Supports hybrid workloads of both analytical and transactional processing, making it versatile for use cases that require both types of operations.
  • Seamless Integration
    Integrates well with the Apache ecosystem, particularly with Apache Hadoop, Apache Impala, and Apache Spark, enabling a cohesive environment for data processing and management.
  • Fine-grained Updates
    Allows for efficient updates to individual columns and rows, which is useful for applications that require frequent updates alongside analytic capabilities.
  • Schema Evolution
    Supports schema evolution, which allows for adding, dropping, and renaming columns without costly table rewrites.

Possible disadvantages of Apache Kudu

  • Complexity in Installation and Configuration
    The setup and configuration of Kudu can be complex, requiring a good understanding of its architecture and dependencies.
  • Limited SQL Support
    While Kudu is optimized for analytical tasks, its SQL capabilities are limited compared to some traditional RDBMS systems, which might require additional tools for more complex queries.
  • Community and Ecosystem
    Although growing, the community and ecosystem around Kudu are smaller compared to more established systems, which may result in less available resources and third-party tools.
  • Memory Intensive
    Kudu can be memory-intensive, which might require more hardware resources compared to other systems, especially as data volumes grow.
  • Write Performance Limitations
    While Kudu offers fast reads, its write performance can be slower compared to systems specifically optimized for high-speed transactional processing.

Apache Arrow features and specs

  • In-Memory Columnar Format
    Apache Arrow stores data in a columnar format in memory which allows for efficient data processing and analytics by enabling operations on entire columns at a time.
  • Language Agnostic
    Arrow provides libraries in multiple languages such as C++, Java, Python, R, and more, facilitating cross-language development and enabling data interchange between ecosystems.
  • Interoperability
    Arrow's ability to act as a data transfer protocol allows easy interoperability between different systems or applications without the need for serialization or deserialization.
  • Performance
    Designed for high performance, Arrow can handle large data volumes efficiently due to its zero-copy reads and SIMD (Single Instruction, Multiple Data) operations.
  • Ecosystem Integration
    Arrow integrates well with various data processing systems like Apache Spark, Pandas, and more, making it a versatile choice for data applications.

Possible disadvantages of Apache Arrow

  • Complexity
    The use of Apache Arrow can introduce additional complexity, especially for smaller projects or those which do not require high-performance data interchange.
  • Learning Curve
    Getting accustomed to Apache Arrow can take time due to its unique in-memory format and APIs, especially for developers who are new to columnar data processing.
  • Memory Usage
    While Arrow excels in speed and performance, the memory consumption can be higher compared to row-based storage formats, potentially becoming a bottleneck.
  • Maturity
    Although rapidly evolving, some Arrow components or language implementations may not be as mature or feature-complete, potentially leading to limitations in certain use cases.
  • Integration Challenges
    While Arrow aims for broad compatibility, integrating it into existing systems may require substantial effort, affecting development timelines.

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 Arrow videos

+ Add

Wes McKinney - Apache Arrow: Leveling Up the Data Science Stack

More videos:

  • Review - "Apache Arrow and the Future of Data Frames" with Wes McKinney
  • Review - Apache Arrow Flight: Accelerating Columnar Dataset Transport (Wes McKinney, Ursa Labs)

Category Popularity

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

Apache Kudu

Apache Arrow

Office & Productivity
100 100%
Office & Productivity
0% 0
Databases
0 0%
Databases
100% 100
Technical Computing
100 100%
Technical Computing
0% 0
NoSQL Databases
0 0%
NoSQL Databases
100% 100

User comments

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Social recommendations and mentions

Based on our record, Apache Arrow seems to be more popular. It has been mentiond 38 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 Arrow mentions (38)

  • Unlocking DuckDB from Anywhere - A Guide to Remote Access with Apache Arrow and Flight RPC (gRPC)
    Apache Arrow : It contains a set of technologies that enable big data systems to process and move data fast. - Source: dev.to / 5 months ago
  • Using Polars in Rust for high-performance data analysis
    One of the main selling points of Polars over similar solutions such as Pandas is performance. Polars is written in highly optimized Rust and uses the Apache Arrow container format. - Source: dev.to / 6 months ago
  • Kotlin DataFrame ❤️ Arrow
    Kotlin DataFrame v0.14 comes with improvements for reading Apache Arrow format, especially loading a DataFrame from any ArrowReader. This improvement can be used to easily load results from analytical databases (such as DuckDB, ClickHouse) directly into Kotlin DataFrame. - Source: dev.to / about 1 year ago
  • Shades of Open Source - Understanding The Many Meanings of "Open"
    It's this kind of certainty that underscores the vital role of the Apache Software Foundation (ASF). Many first encounter Apache through its pioneering project, the open-source web server framework that remains ubiquitous in web operations today. The ASF was initially created to hold the intellectual property and assets of the Apache project, and it has since evolved into a cornerstone for open-source projects... - Source: dev.to / 11 months ago
  • Arrow Flight SQL in Apache Doris for 10X faster data transfer
    Apache Doris 2.1 has a data transmission channel built on Arrow Flight SQL. (Apache Arrow is a software development platform designed for high data movement efficiency across systems and languages, and the Arrow format aims for high-performance, lossless data exchange.) It allows high-speed, large-scale data reading from Doris via SQL in various mainstream programming languages. For target clients that also... - Source: dev.to / 12 months ago
View more

What are some alternatives?

When comparing Apache Kudu and Apache Arrow, 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.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

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 Ignite - high-performance, integrated and distributed in-memory platform for computing and transacting on...

IBM Cloud Pak for Data - Move to cloud faster with IBM Cloud Paks running on Red Hat OpenShift – fully integrated, open, containerized and secure solutions certified by IBM.

Apache Parquet - Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.

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