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Overview

Overall experience with Apache Spark

Director of Finance
<50M USD, Banking
FAVORABLE

“Efficient for Large Datasets But Faces Issues with Python Performance and Storage”

5.0
Feb 14, 2026
its excellent for big data analysis and easy to use and manage
There are no reviews in this category.
CRITICAL

Key Insights

A Snapshot of What Matters - Based on Validated User Reviews

Peer Discussions

Recommended Gartner Insights

  • Market Guide for Event Stream Processing

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Logo of Apache Spark

Apache Spark

byApache Software Foundation
in Event Stream Processing
4.5

About Company

Company Details

Updated 9th August 2024
Head office location
United States

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Reviewer Insights for: Apache Spark
Deciding Factors: Apache Spark Vs. Market Average
Performance of Apache Spark Across Market Features

Apache Spark Likes & Dislikes

Like

deployment , less support needed , easy to use, open source

Like

The use of module via easy set of programs. a programer need not write large codes to operate and use of sql like commands is also supported.

Like

Some of the features of Apache Spark are- 1. It is easily compatible with SQL makes it accessible to users having very less or no programming knowledge. It works with various formats like JSON, Parquet etc. 2. Its in-memory database allows this software to process large volume data. Its processing speed may reach to Petabytes sometimes. 3. It allows users to track real time data and do react to any specific changes done instantly. It is widely accepted by financial industry to operate real time trading and identify gaps instantly.

Dislike

No native file storage, python program is running slow

Dislike

Nothing as of now

Dislike

1. Foremost drawback of this software is its high memory consumption. It heavily consumes RAM to provide high speed data processing. But this could lead to major memory consumption and needs additional hardware investment. 2. Apache Spark's processing speed becomes slow when it works on multiple small files. This makes it vulnerable for small scale industry with small and multiple datasets. 3. Fetching data from different sources might affect on data accuracy and data quality which may result to inaccurate analysis result.

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Apache Spark Reviews and Ratings

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  • Director of Finance
    <50M USD
    Banking
    Review Source

    Efficient for Large Datasets But Faces Issues with Python Performance and Storage

    5.0
    Feb 14, 2026
    its excellent for big data analysis and easy to use and manage
  • IT Associate
    10B+ USD
    Banking
    Review Source

    Efficient Python Module Handles Huge Data with Ease

    5.0
    Feb 4, 2025
    I used this via a python module. It's great for handling large amounts of data with efficiency. The module also provides easy coding options and optimization.
  • DATA ANALYTICS MANAGER
    50M-1B USD
    Finance (non-banking)
    Review Source

    Processing Large Datasets using Apache Spark

    5.0
    Nov 8, 2023
    Apache spark is a unified engine software made for large scale data analytics powered by Apache Software Foundation. Its flexible option allows this software to work on multiple language and execute Data Analytics and Machine Learning tasks.
  • DATA AND ANALYTICS MANAGER
    50M-1B USD
    Retail
    Review Source

    it is flexible, scalable, fault tolerant and difficult to use at early stage

    4.0
    Oct 31, 2023
    The main reason for my rating is very very convenient in handling big data and big scale data, and the functionality it offers which is flexibility, scalability using multiple data source is commendable and recommendable, one of the projects which i was using apache spark is for dynamic pricing algorithm with multiple statistical approaches with big data handled very easily there.
  • Manager of IT Services
    1B-10B USD
    Consumer Goods
    Review Source

    Review on Apache Spark

    4.0
    Oct 17, 2023
    As we are handling more than 100 gb data on daily basis, I found spark framework as best solution to accomplish the business need. As spark is know for its speed and performance. It also supports both dataframe API and SQL queries in mutiple languages like scala, python, R and java.
...
Showing Result 1-5 of 47

4.5

(47 Ratings)

Rating Distribution

5 Star
38%
4 Star
62%
3 Star
0%
2 Star
0%
1 Star
0%
Why ratings and reviews count differ?

Customer Experience

Evaluation & Contracting

4.4

Integration & Deployment

4.5

Service & Support

4.4

Product Capabilities

4.6