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BlueData VS Apache Parquet

Compare BlueData VS Apache Parquet and see what are their differences

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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 Parquet logo Apache Parquet

Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.
  • BlueData Landing page
    Landing page //
    2023-06-13
  • Apache Parquet Landing page
    Landing page //
    2022-06-17

BlueData features and specs

No features have been listed yet.

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.

BlueData videos

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

More videos:

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

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 BlueData and Apache Parquet)

BlueData

Apache Parquet

Data Dashboard
100 100%
Data Dashboard
0% 0
Databases
0 0%
Databases
100% 100
Big Data
31 31%
Big Data
69% 69
Data Management
47 47%
Data Management
53% 53

User comments

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

BlueData mentions (0)

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

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

What are some alternatives?

When comparing BlueData and Apache Parquet, 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.

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

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

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

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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