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Hashquery VS DuckDB

Compare Hashquery VS DuckDB and see what are their differences

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Hashquery logo Hashquery

A Python framework for defining and querying BI models in your data warehouse.

DuckDB logo DuckDB

DuckDB is an in-process SQL OLAP database management system
Not present
  • DuckDB Landing page
    Landing page //
    2023-06-18

Hashquery features and specs

No features have been listed yet.

DuckDB features and specs

  • Lightweight
    DuckDB is a lightweight database that is easy to install and use without requiring a separate server process.
  • In-Memory Processing
    It supports efficient in-memory execution, which makes it suitable for analytical queries that require quick data processing.
  • Columnar Storage
    DuckDB uses a columnar storage format that optimizes for analytical workloads by improving read performance for large datasets.
  • Integration with Data Science Tools
    The database integrates well with popular data science tools and libraries such as Pandas, R, and Jupyter Notebooks.
  • SQL Support
    DuckDB offers full support for SQL, allowing users to leverage their existing SQL knowledge without having to learn new query languages.
  • Open Source
    DuckDB is open-source, enabling users to inspect the code, contribute to its development, and use it without licensing costs.

Possible disadvantages of DuckDB

  • Limited Scalability
    DuckDB is optimized for single-node operations, which may not be suitable for scaling out to large, distributed data workloads.
  • Relatively New
    As a newer database system, DuckDB might lack some features and optimizations found in more mature database systems.
  • Lack of Advanced Features
    DuckDB may not support some advanced database management features like complex transactions and user permissions found in other database systems.
  • Community and Support
    Being a less mature project, it might not have as large a community or extensive documentation and support as other established database systems.
  • Limited Distributed Processing
    DuckDB currently focuses more on local data processing and may not be the best choice for applications needing distributed computing capabilities.

Hashquery videos

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DuckDB videos

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DuckDB An Embeddable Analytical Database

More videos:

  • Review - DuckDB: Hi-performance SQL queries on pandas dataframe (Python)
  • Review - DuckDB An Embeddable Analytical Database

Category Popularity

0-100% (relative to Hashquery and DuckDB)

Hashquery

DuckDB

Big Data
20 20%
Big Data
80% 80
Databases
15 15%
Databases
85% 85
Data Warehousing
100 100%
Data Warehousing
0% 0
Relational Databases
13 13%
Relational Databases
87% 87

User comments

Share your experience with using Hashquery and DuckDB. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, DuckDB seems to be more popular. It has been mentiond 37 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.

Hashquery mentions (0)

We have not tracked any mentions of Hashquery yet. Tracking of Hashquery recommendations started around May 2025.

DuckDB mentions (37)

  • From OLTP to OLAP: Streaming Databases into MotherDuck with Estuary
    DuckDB is an open source analytical database designed with a clear goal: to make complex queries fast and simple without heavy infrastructure. Instead of being a traditional client-server database, DuckDB is embedded. It runs inside the host process, which reduces overhead and makes it easy to integrate directly into applications, notebooks, or scripts. Several features stand out:. - Source: dev.to / 8 days ago
  • DuckDB + Iceberg: The ultimate synergy
    Apache Iceberg and DuckDB have established themselves as key players in data architecture landscape. With DuckDB 1.4's native support for Iceberg writes, combined with Apache Polaris and MinIO, this promising stack offers efficiency, scalability, and flexibility. - Source: dev.to / 14 days ago
  • DuckDB on AWS Lambda: The Easy Way with Layers
    It seemed like the perfect opportunity to explore DuckDB, an in-process analytical database known for its efficiency and simplicity. - Source: dev.to / 23 days ago
  • From Go to Rust: Supercharging Our ClickHouse UDFs with Alloy
    While our Go-based implementation has served us well, we've been exploring whether Rust—with its rapidly maturing Ethereum ecosystem—could take us even further. The potential benefits are compelling: better performance, enhanced safety, and improved portability that could make it easier to bring these UDFs to other analytical engines like DataFusion or DuckDB. - Source: dev.to / 3 months ago
  • Show HN: TextQuery – Query CSV, JSON, XLSX Files with SQL
    Have you seen duckdb? https://duckdb.org/ It's basically what you're building, but more low-level. Really cool, to be honest -- serves the same market too. Do you have any significant differentiator, other than charts? - Source: Hacker News / 5 months ago
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What are some alternatives?

When comparing Hashquery and DuckDB, you can also consider the following products

Apache Pinot - Apache Pinot is a real-time distributed OLAP datastore, built to deliver scalable real-time analytics with low latency.

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

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

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

Amazon Redshift - Learn about Amazon Redshift cloud data warehouse.

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

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