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Upsolver VS Apache Storm

Compare Upsolver VS Apache Storm and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Upsolver logo Upsolver

Upsolver is a robust Data Lake Platform that simplifies big & streaming data integration, management and preparation on premise (HDFS) or in the cloud (AWS, Azure, GCP).

Apache Storm logo Apache Storm

Apache Storm is a free and open source distributed realtime computation system.
  • Upsolver Landing page
    Landing page //
    2023-08-06
  • Apache Storm Landing page
    Landing page //
    2019-03-11

Upsolver videos

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Apache Storm videos

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Apache Storm Tutorial For Beginners | Apache Storm Training | Apache Storm Example | Edureka

More videos:

  • Review - Developing Java Streaming Applications with Apache Storm
  • Review - Atom Text Editor Option - Real-Time Analytics with Apache Storm

Category Popularity

0-100% (relative to Upsolver and Apache Storm)

Upsolver

Apache Storm

Business & Commerce
100 100%
Business & Commerce
0% 0
Big Data
0 0%
Big Data
100% 100
Online Services
100 100%
Online Services
0% 0
Stream Processing
0 0%
Stream Processing
100% 100

User comments

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

These are some of the external sources and on-site user reviews we've used to compare Upsolver and Apache Storm

Upsolver Reviews

Top 10 AWS ETL Tools and How to Choose the Best One | Visual Flow
In this way, Upsolver removes the complexity of Big Data and Real-Time projects and reduces their use time from several weeks or months to several hours. With the latest Volcano technology, this tool queries the entire data lake in less than a millisecond and stores 10x the amount of data in RAM.
Source: visual-flow.com

Apache Storm Reviews

Top 15 Kafka Alternatives Popular In 2021
Apache Storm is a recognized, distributed, open-source real-time computational system. It is free, simple to use, and helps in easily and accurately processing multiple data streams in real-time. Because of its simplicity, it can be utilized with any programming language and that is one reason it is a developer’s preferred choice. It is fast, scalable, and integrates well...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Storm is an open-source distributed real-time computational system for processing data streams. Similar to what Hadoop does for batch processing, Apache Storm does for unbounded streams of data in a reliable manner. Built by Twitter, Apache Storm specifically aims at the transformation of data streams. Storm has many use cases like real-time analytics, online machine...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Apache Storm seems to be a lot more popular than Upsolver. While we know about 11 links to Apache Storm, we've tracked only 1 mention of Upsolver. 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.

Upsolver mentions (1)

  • Anyone Used Dremio?
    Most of the pains of using query engines over object storage are in the ongoing management of files (partitioning, compression, merging many small files into fewer larger files) Cloud data lakes are tremendously valuable when it comes to exploratory and ad-hoc data analysis. If you really require sub-second queries on structured data, you're better off with a data warehouse. I'm not totally clear on your use... Source: almost 3 years ago

Apache Storm mentions (11)

  • Data Engineering and DataOps: A Beginner's Guide to Building Data Solutions and Solving Real-World Challenges
    There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 1 year ago
  • Real Time Data Infra Stack
    Although this article lists a lot of targets for technical selection, there are definitely others that I haven't listed, which may be either outdated, less-used options such as Apache Storm or out of my radar from the beginning, like JAVA ecosystem. - Source: dev.to / over 1 year ago
  • In One Minute : Hadoop
    Storm, a system for real-time and stream processing. - Source: dev.to / over 1 year ago
  • Elon Musk reportedly wants to fire 75% of Twitter’s employees
    Google has scaled well and has helped others scale, Twitter has always been behind by years. I think the only thing they did well was Twitter Storm, now taken up by Apache Foundation. Source: over 1 year ago
  • Spark for beginners - and you
    Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 2 years ago
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What are some alternatives?

When comparing Upsolver and Apache Storm, you can also consider the following products

Kylo - Kylo is an end-to-end data lake management software that provides data from many sources in an automated fashion and optimizes it.

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

IRI Voracity - IRI Voracity is an automated data management platform that helps you extract, transform and load (ETL) your data lake to any data warehouse or cloud.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Mozart Data - The easiest way for teams to build a Modern Data Stack

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

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