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SQream VS Apache Arrow

Compare SQream VS Apache Arrow and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

SQream logo SQream

SQream empowers organizations to analyze the full scope of their Massive Data, from terabytes to petabytes, to achieve critical insights which were previously unattainable.

Apache Arrow logo Apache Arrow

Apache Arrow is a cross-language development platform for in-memory data.
  • SQream Landing page
    Landing page //
    2023-09-17

SQream is a data analytics acceleration platform built especially for massive data - from terabytes to petabytes. SQream takes queries down from days to hours and hours to minutes. The SQream platform provides the ability to analyze more data, faster, with multiple dimensions and cuts data preparation significantly by enabling ad-hoc querying on raw data. Leading global organizations in telecommunications, healthcare, ad-tech, retail and more rely on SQream to achieve critical business insights and potentially valuable BI across their massive data stores.

  • Apache Arrow Landing page
    Landing page //
    2021-10-03

SQream 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.

SQream videos

+ Add

SQream DB v2020.1 - Product review and demo

More videos:

  • Review - Introducing SQream DB - The GPU-accelerated data warehouse for massive data
  • Review - SQream DB, GPU-accelerated data warehouse

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 SQream and Apache Arrow)

SQream

Apache Arrow

Data Management
100 100%
Data Management
0% 0
Databases
0 0%
Databases
100% 100
Big Data
30 30%
Big Data
70% 70
NoSQL Databases
0 0%
NoSQL Databases
100% 100

User comments

Share your experience with using SQream 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 a lot more popular than SQream. While we know about 38 links to Apache Arrow, we've tracked only 1 mention of SQream. 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.

SQream mentions (1)

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 / 7 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 / about 1 year ago
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What are some alternatives?

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

Panoply - Panoply is a smart cloud data warehouse

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

Impala - Impala is a modern, open source, distributed SQL query engine for Apache Hadoop.

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

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

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

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