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Apache Arrow VS MonetDB

Compare Apache Arrow VS MonetDB and see what are their differences

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Apache Arrow logo Apache Arrow

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

MonetDB logo MonetDB

Column-store database
  • Apache Arrow Landing page
    Landing page //
    2021-10-03
  • MonetDB Landing page
    Landing page //
    2023-09-23

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.

MonetDB features and specs

No features have been listed yet.

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)

MonetDB videos

+ Add

MonetDB on Azure - Deployment and First Query

More videos:

  • Review - My uninformed attempt at running open source high speed monetDB
  • Review - DB2 — Chapter 03 — Video #10 — Column storage in MonetDB, NSM vs. DSM, positional BAT "joins"

Category Popularity

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

Apache Arrow

MonetDB

Databases
83 83%
Databases
17% 17
Big Data
75 75%
Big Data
25% 25
Relational Databases
0 0%
Relational Databases
100% 100
NoSQL Databases
100 100%
NoSQL Databases
0% 0

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 40 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 Arrow mentions (40)

  • Show HN: Typed-arrow – compile‑time Arrow schemas for Rust
    I had no idea what Arrow is: https://arrow.apache.org or arrow-rs: https://github.com/apache/arrow-rs. - Source: Hacker News / about 2 months ago
  • Show HN: Pontoon, an open-source data export platform
    - Open source: Pontoon is free to use by anyone Under the hood, we use Apache Arrow (https://arrow.apache.org/) to move data between sources and destinations. Arrow is very performant - we wanted to use a library that could handle the scale of moving millions of records per minute. In the shorter-term, there are several improvements we want to make, like:. - Source: Hacker News / 2 months ago
  • 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 / 10 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 / 11 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 / over 1 year ago
View more

MonetDB mentions (0)

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

What are some alternatives?

When comparing Apache Arrow and MonetDB, you can also consider the following products

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

ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.

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

DuckDB - DuckDB is an in-process SQL OLAP database management system

DBeaver - DBeaver - Universal Database Manager and SQL Client.

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

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