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Apache Parquet VS QuickBI

Compare Apache Parquet VS QuickBI 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.

QuickBI logo QuickBI

Export data from over 300 sources to a data warehouse and analyze it with a reporting tool of your choice. Quick and easy setup.
  • Apache Parquet Landing page
    Landing page //
    2022-06-17
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Apache Parquet videos

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Category Popularity

0-100% (relative to Apache Parquet and QuickBI)

Apache Parquet

QuickBI

Databases
100 100%
Databases
0% 0
Data Integration
0 0%
Data Integration
100% 100
Big Data
100 100%
Big Data
0% 0
Data Management
0 0%
Data Management
100% 100

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.

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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QuickBI mentions (0)

We have not tracked any mentions of QuickBI yet. Tracking of QuickBI recommendations started around Feb 2024.

What are some alternatives?

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

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

Supermetrics - Supermetrics condenses all the major vectors of data relevant to a user's marketing campaigns and helps them make sense of it all.

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

Airbyte - Replicate data in minutes with prebuilt & custom connectors

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

Fivetran - Fivetran offers companies a data connector for extracting data from many different cloud and database sources.

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