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Qubole VS Apache Parquet

Compare Qubole VS Apache Parquet and see what are their differences

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Qubole logo Qubole

Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

Apache Parquet logo Apache Parquet

Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.
  • Qubole Landing page
    Landing page //
    2023-06-22
  • Apache Parquet Landing page
    Landing page //
    2022-06-17

Qubole videos

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Fast and Cost Effective Machine Learning Deployment with S3, Qubole, and Spark

More videos:

  • Review - Migrating Big Data to the Cloud: WANdisco, GigaOM and Qubole
  • Review - Democratizing Data with Qubole

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 Qubole and Apache Parquet)

Qubole

Apache Parquet

Data Dashboard
86 86%
Data Dashboard
14% 14
Databases
0 0%
Databases
100% 100
Big Data
58 58%
Big Data
42% 42
Data Warehousing
100 100%
Data Warehousing
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 19 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.

Qubole mentions (0)

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

Apache Parquet mentions (19)

  • [D] Is there other better data format for LLM to generate structured data?
    The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's delta.io over json. Source: 5 months ago
  • Demystifying Apache Arrow
    Apache Parquet (Parquet for short), which nowadays is an industry standard to store columnar data on disk. It compress the data with high efficiency and provides fast read and write speeds. As written in the Arrow documentation, "Arrow is an ideal in-memory transport layer for data that is being read or written with Parquet files". - Source: dev.to / 12 months ago
  • Parquet: more than just "Turbo CSV"
    Googling that suggests this page: https://parquet.apache.org/. Source: about 1 year ago
  • Beginner question about transformation
    You should also consider distribution of data because in a company that has machine learning workflows, the same data may need to go through different workflows using different technologies and stored in something other than a data warehouse, e.g. Feature engineering in Spark and loaded/stored in binary format such as Parquet in a data lake/object store. Source: about 1 year ago
  • Pandas Free Online Tutorial In Python — Learn Pandas Basics In 5 Lessons!
    This section will teach you how to read and write data to and from a variety of file types, including CSV, Excel, SQL, HTML, Parquet, JSON etc. You’ll also learn how to manipulate data from other sources, such as databases and web sites. Source: about 1 year ago
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What are some alternatives?

When comparing Qubole and Apache Parquet, you can also consider the following products

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

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

MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming

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

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

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

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