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Amazon Kinesis vs Databricks comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

ROI

Sentiment score
7.8
Amazon Kinesis offers significant cost savings, seamless integration, improved monitoring, and reduces data ingestion costs, enhancing ROI and architecture.
Sentiment score
6.5
Databricks enhances efficiency and ROI, offering scalable solutions and cost savings over traditional Hadoop with easy setup.
With Lambda, there is no need for data transfer charges, which is beneficial for less frequent workloads.
When it comes to big data processing, I prefer Databricks over other solutions.
For a lot of different tasks, including machine learning, it is a nice solution.
 

Customer Service

Sentiment score
7.1
Amazon Kinesis customer support is generally quick and effective, but experiences vary in technical guidance and response times.
Sentiment score
7.2
Databricks' support is generally praised for responsiveness, though some note delays, with resources often sufficient for independent problem-solving.
We receive prompt support from AWS solution architects or TAMs.
As of now, we are raising issues and they are providing solutions without any problems.
Whenever we reach out, they respond promptly.
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
 

Scalability Issues

Sentiment score
7.3
Amazon Kinesis is scalable for reliable streaming, but complex processing and costs may vary with implementation and data volumes.
Sentiment score
7.5
Databricks is praised for its adaptability, scalability, automation features, and performance across industries but needs improved autoscaling control.
Amazon Kinesis provides auto-scaling with streams that handle large volumes well.
Databricks is an easily scalable platform.
I would rate the scalability of this solution as very high, about nine out of ten.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
 

Stability Issues

Sentiment score
7.8
Amazon Kinesis is praised for stability and fault tolerance, though some users report slowdowns and capacity issues.
Sentiment score
7.7
Databricks is highly rated for stability and performance, with occasional minor issues often due to user or external factors.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
They release patches that sometimes break our code.
Databricks is definitely a very stable product and reliable.
 

Room For Improvement

Amazon Kinesis requires improvements in throughput, automation, setup complexity, data retention, machine learning features, and user-friendly interfaces.
Databricks should improve visualization, integration, user experience, and scalability, addressing concerns about pricing, error messages, and onboarding.
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes.
We could use their job clusters, however, that increases costs, which is challenging for us as a startup.
This feature, if made publicly available, may act as a game-changer, considering many global organizations use SAP data for their ERP requirements.
If I could right-click to copy absolute paths or to read files directly into a data frame, it would standardize and simplify the process.
 

Setup Cost

Amazon Kinesis is cost-effective compared to self-managed solutions, but prices can increase with high data usage and features.
Databricks offers flexible, often expensive pricing, mitigated by cloud deployment and tiered licensing, with varied user cost experiences.
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
It is not a cheap solution.
 

Valuable Features

Amazon Kinesis offers easy configuration, real-time analytics, and robust AWS integration, ideal for managing large, complex data workflows.
Databricks offers an intuitive interface for data processing, integrating SQL, Python, and features like Delta Lake and MLflow.
Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
Databricks' capability to process data in parallel enhances data processing speed.
 

Categories and Ranking

Amazon Kinesis
Ranking in Streaming Analytics
2nd
Average Rating
8.0
Reviews Sentiment
7.1
Number of Reviews
28
Ranking in other categories
No ranking in other categories
Databricks
Ranking in Streaming Analytics
1st
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
91
Ranking in other categories
Cloud Data Warehouse (8th), Data Science Platforms (1st)
 

Mindshare comparison

As of June 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 7.9%, down from 13.0% compared to the previous year. The mindshare of Databricks is 14.5%, up from 10.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Prabin Silwal - PeerSpot reviewer
Pipeline setup is very simple
I am not exactly sure about where improvements are needed in the tool. When I was working on the tool, it was very scalable, and the only thing we needed in our company was temporary streaming stuff that could work well. We didn't want to set up our own Kafka, other queues, or processing systems. As it is a cloud tool, it is easy for us to use the tool, and it satisfies all our requirements. Maybe for the other cases, if we need, then it may need some improvements. The tool satisfies our particular needs. Currently, the pipeline setup is very simple. For our particular use cases, it is because we just want to get the data and send it to the different data lakes or some logging system. Previously, we also used Amazon Kinesis to log those to Splunk, and later on, we removed Splunk and transferred that to Datadog. For our use cases, I don't want any new features in the tool. Amazon Kinesis' use case is for collecting, processing, and analyzing. If anything can be added to the tool, then I feel one should be able to use the same kind of tool so that everything is there in the product, like an alert system, and so that one can analyze, make a query, and do sourcing from the solution itself rather than using other logging and monitoring systems. The tool should focus on having an alert system rather than having to use a third-party solution. We can just get the data over Amazon Kinesis, and we can directly use all the benefits of current analytical tools, like in the areas involving BI, Looker, and Tableau. One would not need to buy the aforementioned tools, and we can just get started with Amazon Kinesis.
ShubhamSharma7 - PeerSpot reviewer
Capability to integrate diverse coding languages in a single notebook greatly enhances workflow
Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.
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Top Industries

By visitors reading reviews
Computer Software Company
19%
Financial Services Firm
17%
Manufacturing Company
10%
Insurance Company
4%
Financial Services Firm
18%
Computer Software Company
10%
Manufacturing Company
9%
Healthcare Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Amazon Kinesis?
Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive.
What is your experience regarding pricing and costs for Amazon Kinesis?
Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.
What needs improvement with Amazon Kinesis?
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes. Also, the KCL library's documentation could be improved to better explain the configuration parameters...
Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...
How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...
Which would you choose - Databricks or Azure Stream Analytics?
Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...
 

Comparisons

 

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis
Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
 

Overview

 

Sample Customers

Zillow, Netflix, Sonos
Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
Find out what your peers are saying about Amazon Kinesis vs. Databricks and other solutions. Updated: June 2025.
856,873 professionals have used our research since 2012.