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Amazon Kinesis vs Databricks comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Amazon Kinesis
Ranking in Streaming Analytics
2nd
Average Rating
8.0
Number of Reviews
26
Ranking in other categories
No ranking in other categories
Databricks
Ranking in Streaming Analytics
1st
Average Rating
8.2
Number of Reviews
82
Ranking in other categories
Data Science Platforms (1st)
 

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 10.6%, down from 15.4% compared to the previous year. The mindshare of Databricks is 14.0%, up from 9.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Rajni Kumar Jha - PeerSpot reviewer
Apr 3, 2024
Used for media streaming and live-streaming data
It is not compulsory to use Amazon Kinesis. If you don't want to use the data streaming, you can use just the Kinesis data firehose. Using the Kinesis data firehose is compulsory because we can't store all chats and recordings in Amazon S3 without it. When a call comes in the Amazon Kinesis instance, it will go to Data Streams if we use it. Otherwise, it will go to the Kinesis data firehose, where we need to define the S3 bucket path, and it will go to Amazon S3. So, without the Kinesis data firehose, we can't store all the chats and recordings in Amazon S3. Using Amazon Kinesis totally depends upon the user's requirements. If you want to use live streaming for the data lake or data analyst team, you need to use Amazon Kinesis. If you don't want to use it, you can directly use the Kinesis data firehose, which will be stored in Amazon S3. Overall, I rate the solution an eight out of ten.
Dunstan Matekenya - PeerSpot reviewer
Jul 10, 2024
Process large-scale data sets and integrates with Apache Spark with notebook environment
I primarily use Databricks to process large-scale data sets with Apache Spark. My main use case is processing large data sets, such as 600 GB or 800 GB Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of…

Quotes from Members

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

Pros

"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"The solution works well in rather sizable environments."
"One of the best features of Amazon Kinesis is the multi-partition."
"Its scalability is very high. There is no maintenance and there is no throughput latency. I think data scalability is high, too. You can ingest gigabytes of data within seconds or milliseconds."
"I have worked in companies that build tools in-house. They face scaling challenges."
"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."
"Amazon Kinesis has improved our ROI."
"Great auto-scaling, auto-sharing, and auto-correction features."
"What I like about Databricks is that it's one of the most popular platforms that give access to folks who are trying not just to do exploratory work on the data but also go ahead and build advanced modeling and machine learning on top of that."
"There are good features for turning off clusters."
"The Delta Lake data type has been the most useful part of this solution. Delta Lake is an opensource data type and it was implemented and invented by Databricks."
"Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."
"The setup was straightforward."
"I like cloud scalability and data access for any type of user."
"The solution offers a free community version."
"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."
 

Cons

"Snapshot from the the from the the stream of the data analytic I have already on the cloud, do a snapshot to not to make great or to get the data out size of the web service. But to stop the process and restart a few weeks later when I have more data or more available of the client teams."
"In general, the pain point for us was that once the data gets into Kinesis there is no way for us to understand what's happening because Kinesis divides everything into shards. So if we wanted to understand what's happening with a particular shard, whether it is published or not, we could not. Even with the logs, if we want to have some kind of logging it is in the shard."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"Amazon Kinesis should improve its limits."
"Lacks first in, first out queuing."
"Kinesis Data Analytics needs to be improved somewhat. It's SQL based data but it is not as user friendly as MySQL or Athena tools."
"Could include features that make it easier to scale."
"The price is not much cheaper. So, there is room for improvement in the pricing."
"I would like more integration with SQL for using data in different workspaces."
"Databricks' technical support takes a while to respond and could be improved."
"Would be helpful to have additional licensing options."
"There would also be benefits if more options were available for workers, or the clusters of the two points."
"Databricks has added some alerts and query functionality into their SQL persona, but the whole SQL persona, which is like a role, needs a lot of development. The alerts are not very flexible, and the query interface itself is not as polished as the notebook interface that is used through the data science and machine learning persona. It is clunky at present."
"The initial setup is difficult."
"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."
"The product cannot be integrated with a popular coding IDE."
 

Pricing and Cost Advice

"The pricing depends on the use cases and the level of usage. If you wanted to use Kinesis for different use cases, there's definitely a cheaper base cost involved. However, it's not entirely cheap, as different use cases might require different levels of Kinesis usage."
"The tool's pricing is cheap."
"Under $1,000 per month."
"The solution's pricing is fair."
"Amazon Kinesis is an expensive solution."
"In general, cloud services are very convenient to use, even if we have to pay a bit more, as we know what we are paying for and can focus on other tasks."
"The product falls on a bit of an expensive side."
"The fee is based on the number of hours the service is running."
"The cost is around $600,000 for 50 users."
"There are different versions."
"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."
"The cost for Databricks depends on the use case. I work on it as a consultant, so I'm using the client's Databricks, so it depends on how big the client is."
"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."
"The solution uses a pay-per-use model with an annual subscription fee or package. Typically this solution is used on a cloud platform, such as Azure or AWS, but more people are choosing Azure because the price is more reasonable."
"I would rate the tool’s pricing an eight out of ten."
"We only pay for the Azure compute behind the solution."
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Top Industries

By visitors reading reviews
Computer Software Company
18%
Financial Services Firm
18%
Manufacturing Company
10%
Retailer
4%
Financial Services Firm
16%
Computer Software Company
12%
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?
I think for us, with Amazon Kinesis, if we have to set up our own Kafka or cluster, it will be very time-consuming. If one considers the aforementioned aspect, Amazon Kinesis is a cheap tool.
What needs improvement with Amazon Kinesis?
There are some kind of hard limits on Amazon Kinesis, and if you hit that, then you will get the throughput exceed error. We have to deal with and reduce how many consumers are hitting Amazon Kines...
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: October 2024.
814,649 professionals have used our research since 2012.