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Apache Parquet VS MonetDB

Compare Apache Parquet VS MonetDB and see what are their differences

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Apache Parquet logo Apache Parquet

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

MonetDB logo MonetDB

Column-store database
  • Apache Parquet Landing page
    Landing page //
    2022-06-17
  • MonetDB Landing page
    Landing page //
    2023-09-23

Apache Parquet features and specs

  • Columnar Storage
    Apache Parquet uses columnar storage, which allows for efficient retrieval of only the data you need, reducing I/O and improving query performance on large datasets.
  • Compression
    Parquet files support efficient compression and encoding schemes, resulting in significant storage savings and less data to transfer over the network.
  • Compatibility
    It is compatible with the Hadoop ecosystem, including tools like Apache Spark, Hive, and Impala, making it versatile for big data processing.
  • Schema Evolution
    Parquet supports schema evolution, allowing changes to the schema without breaking existing data, which helps in maintaining long-lived data pipelines.
  • Efficient Read Performance for Aggregations
    Due to its columnar layout, Parquet is highly efficient for processing queries that aggregate data across columns, such as SUM and AVERAGE.

Possible disadvantages of Apache Parquet

  • Write Performance
    Writing data to Parquet can be slower compared to row-based formats, particularly for small inserts or updates, due to the overhead of encoding and compression.
  • Complexity in File Management
    Managing and partitioning Parquet files to optimize performance can become complex, particularly as datasets grow in size and complexity.
  • Not Ideal for All Workloads
    Workloads that require frequent row-level updates or involve small queries might be less efficient with Parquet due to its columnar nature.
  • Learning Curve
    The need to understand the nuances of columnar storage, encoding, and compression can pose a learning curve for teams new to Parquet.

MonetDB features and specs

No features have been listed yet.

Apache Parquet videos

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MonetDB videos

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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 Parquet and MonetDB)

Apache Parquet

MonetDB

Databases
80 80%
Databases
20% 20
Big Data
81 81%
Big Data
19% 19
Relational Databases
0 0%
Relational Databases
100% 100
Data Management
100 100%
Data Management
0% 0

User comments

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Social recommendations and mentions

Based on our record, Apache Parquet seems to be more popular. It has been mentiond 25 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 Parquet mentions (25)

  • 🔥 Simulating Course Schedules 600x Faster with Web Workers in CourseCast
    If there was a way to package and compress the Excel spreadsheet in a web-friendly format, then there's nothing stopping us from loading the entire dataset in the browser!1 Sure enough, the Parquet file format was specifically designed for efficient portability. - Source: dev.to / about 1 month ago
  • How to Pitch Your Boss to Adopt Apache Iceberg?
    Iceberg decouples storage from compute. That means your data isn’t trapped inside one proprietary system. Instead, it lives in open file formats (like Apache Parquet) and is managed by an open, vendor-neutral metadata layer (Apache Iceberg). - Source: dev.to / 6 months ago
  • Processing data with “Data Prep Kit” (part 2)
    Data prep kit github repository: https://github.com/data-prep-kit/data-prep-kit?tab=readme-ov-file Quick start guide: https://github.com/data-prep-kit/data-prep-kit/blob/dev/doc/quick-start/contribute-your-own-transform.md Provided samples and examples: https://github.com/data-prep-kit/data-prep-kit/tree/dev/examples Parquet: https://parquet.apache.org/. - Source: dev.to / 6 months ago
  • 🔬Public docker images Trivy scans as duckdb datas on Kaggle
    Deliver nice ready-to-use data as duckdb, parquet and csv. - Source: dev.to / 6 months ago
  • Introducing Promptwright: Synthetic Dataset Generation with Local LLMs
    Push the dataset to hugging face in parquet format. - Source: dev.to / 11 months 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 Parquet and MonetDB, you can also consider the following products

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

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

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

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

DBeaver - DBeaver - Universal Database Manager and SQL Client.

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

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