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Confluent VS Upsolver

Compare Confluent VS Upsolver and see what are their differences

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

Confluent logo Confluent

Confluent offers a real-time data platform built around Apache Kafka.

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).
  • Confluent Landing page
    Landing page //
    2023-10-22
  • Upsolver Landing page
    Landing page //
    2023-08-06

Confluent videos

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1. Intro | Monitoring Kafka in Confluent Control Center

More videos:

  • Review - Jason Gustafson, Confluent: Revisiting Exactly One Semantics (EOS) | Bay Area Apache Kafka® Meetup
  • Review - CLEARER SKIN AFTER 1 USE‼️| Ancient Cosmetics Update✨| CONFLUENT & RETICULATED PAPILLOMATOSIS CURE?😩

Upsolver videos

No Upsolver videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Confluent and Upsolver)

Confluent

Upsolver

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

User comments

Share your experience with using Confluent and Upsolver. 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 Confluent and Upsolver

Confluent Reviews

We have no reviews of Confluent yet.
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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

Social recommendations and mentions

Upsolver might be a bit more popular than Confluent. We know about 1 link to it since March 2021 and only 1 link to Confluent. 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.

Confluent mentions (1)

  • Spring Boot Event Streaming with Kafka
    We’re going to setup a Kafka cluster using confluent.io, create a producer and consumer as well as enhance our behavior driven tests to include the new interface. We’re going to update our helm chart so that the updates are seamless to Kubernetes and we’re going to leverage our observability stack to propagate the traces in the published messages. Source: over 2 years ago

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

What are some alternatives?

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

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

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

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.

Spark Streaming - Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.

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

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