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BlueData VS Apache Arrow

Compare BlueData VS Apache Arrow and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

BlueData logo BlueData

BlueData's software platform makes it easier, faster and more cost-effective for organizations to deploy Big Data infrastructure on-premises.

Apache Arrow logo Apache Arrow

Apache Arrow is a cross-language development platform for in-memory data.
  • BlueData Landing page
    Landing page //
    2023-06-13
  • Apache Arrow Landing page
    Landing page //
    2021-10-03

BlueData features and specs

No features have been listed yet.

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.

BlueData videos

+ Add

HPE BlueData Software Demo | AI, Machine Learning and Data Science

More videos:

  • Review - Kumar Sreekanti, BlueData - #BigDataSV 2016 - #theCUBE

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 BlueData and Apache Arrow)

BlueData

Apache Arrow

Data Dashboard
100 100%
Data Dashboard
0% 0
Databases
0 0%
Databases
100% 100
Big Data
38 38%
Big Data
62% 62
Data Management
100 100%
Data Management
0% 0

User comments

Share your experience with using BlueData and Apache Arrow. For example, how are they different and which one is better?
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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.

BlueData mentions (0)

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

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
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What are some alternatives?

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

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

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

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

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

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