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ArangoDB VS Apache Parquet

Compare ArangoDB VS Apache Parquet and see what are their differences

ArangoDB logo ArangoDB

A distributed open-source database with a flexible data model for documents, graphs, and key-values.

Apache Parquet logo Apache Parquet

Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.
  • ArangoDB Landing page
    Landing page //
    2023-01-20
  • Apache Parquet Landing page
    Landing page //
    2022-06-17

ArangoDB features and specs

  • Graph DB

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.

Analysis of ArangoDB

Overall verdict

  • ArangoDB is indeed a good option for those looking for a flexible, feature-rich, and scalable database solution. It caters to modern applications requiring diverse data representations and complex querying capabilities, particularly when graph functionality is vital. However, the right choice depends on specific project requirements and familiarity with ArangoDBโ€™s features and ecosystem.

Why this product is good

  • ArangoDB is a highly versatile database solution known for its multi-model approach, which supports document, key/value, and graph data models. This flexibility allows for complex data structures and enables developers to use the most suitable model for their specific application needs all within a single database. Additionally, ArangoDB offers robust features such as a powerful query language (AQL), scalability, a flexible architecture, and native support for graph analytics, making it suitable for a wide range of use cases.

Recommended for

  • Developers and organizations needing a multi-model database solution
  • Projects requiring complex data analysis, including graph algorithms
  • Applications that can benefit from a flexible, schema-free data structure
  • Teams looking for scalability and horizontal expansion capabilities
  • Environments with diverse data representation needs where maintaining multiple databases is inefficient

ArangoDB videos

ArangoDB and Foxx Framework, deeper dive. WHILT#17

Apache Parquet videos

No Apache Parquet videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to ArangoDB and Apache Parquet)
Databases
83 83%
17% 17
NoSQL Databases
95 95%
5% 5
Big Data
0 0%
100% 100
Graph Databases
100 100%
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare ArangoDB and Apache Parquet

ArangoDB Reviews

9 Best MongoDB alternatives in 2019
ArangoDB is a native multi-model DBMS system. It supports three data models with one database core and a unified query language AQL. Its query language is declarative which helps you to compare different data access patterns by using a single query.
Source: www.guru99.com
Top 15 Free Graph Databases
ArangoDB is a distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions. ArangoDB
ArangoDB vs Neo4j - What you can't do with Neo4j
Scalability needs and ArangoDB ArangoDB is cluster ready for graphs, documents and key/values. ArangoDB is suitable for e.g. recommendation engines, personalization, Knowledge Graphs or other graph-related use cases. ArangoDB provides special features for scale-up (Vertex-centric indices) and scale-out (SmartGraphs).

Apache Parquet Reviews

We have no reviews of Apache Parquet yet.
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Social recommendations and mentions

Based on our record, Apache Parquet should be more popular than ArangoDB. 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.

ArangoDB mentions (6)

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Apache Parquet mentions (25)

  • ๐Ÿ”ฅ 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 isnโ€™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 โ€œData Prep Kitโ€ (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
  • ๐Ÿ”ฌPublic 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

What are some alternatives?

When comparing ArangoDB and Apache Parquet, 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.

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

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

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

neo4j - Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.

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