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Apache Parquet VS 9 Spokes

Compare Apache Parquet VS 9 Spokes and see what are their differences

Apache Parquet logo Apache Parquet

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

9 Spokes logo 9 Spokes

9 Spokes is a free data dashboard that connects your apps to identify powerful insights to deliver your business KPI's.
  • Apache Parquet Landing page
    Landing page //
    2022-06-17
  • 9 Spokes Landing page
    Landing page //
    2023-10-04

Apache Parquet videos

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9 Spokes videos

9 Spokes and Gigride - case study

More videos:

  • Review - 9 Spokes - How It Works
  • Review - Winning with large enterprise customers, the learnings – 9 Spokes

Category Popularity

0-100% (relative to Apache Parquet and 9 Spokes)
Databases
100 100%
0% 0
Monitoring Tools
0 0%
100% 100
Big Data
100 100%
0% 0
Application Performance Monitoring

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: 6 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 / about 1 year 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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9 Spokes mentions (0)

We have not tracked any mentions of 9 Spokes yet. Tracking of 9 Spokes recommendations started around Mar 2021.

What are some alternatives?

When comparing Apache Parquet and 9 Spokes, you can also consider the following products

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

LightStep - We deliver insights that put organizations back in control of their complex software apps.

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

Pepperdata - Pepperdata's software runs on existing Hadoop clusters to give operators predictability, capacity, and visibility for their Hadoop jobs.

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

Epsagon - Track costs and fix your serverless application.