Amazon Kinesis vs Databricks comparison

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Amazon Kinesis

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Executive SummaryUpdated on Dec 17, 2024

Databricks and Amazon Kinesis both compete in the data analytics and streaming domain. Databricks appears to have the upper hand in scalability and integration owing to its versatile Lakehouse architecture, while Amazon Kinesis leads in real-time streaming capabilities within AWS environments.

Features: Databricks offers large-scale analytics with built-in optimization, flexible programming options, and seamless integration with various environments. Its Lakehouse architecture and collaborative features resonate with data professionals. Amazon Kinesis excels in real-time data streaming, capturing, processing, and analyzing large data volumes. Its integration and management services simplify streaming processes significantly.

Room for Improvement: Databricks could improve visualization capabilities, data source integration, and machine learning libraries. Pricing and clearer documentation are also points that users suggest need attention. For Amazon Kinesis, better documentation, scalability enhancements, and more automation of functions are areas for improvement. Users also seek more data retention options and reduced latency.

Ease of Deployment and Customer Service: Both Databricks and Amazon Kinesis are mostly deployed on public cloud environments. Databricks supports hybrid and multi-cloud deployments with good customer service, although it has a learning curve for technical expertise. Amazon Kinesis integrates smoothly within AWS and allows straightforward deployment for real-time applications but requires familiarity with AWS systems.

Pricing and ROI: Databricks is considered expensive, suitable for enterprises with a focus on batch processing and data management, enabling ROI through infrastructure cost savings. It offers flexible pricing models but requires efficient use to justify costs. Amazon Kinesis provides competitive pricing for streaming, with cost-effectiveness noted in comparison to building separate structures, enhancing ROI as part of AWS without extra hardware.

To learn more, read our detailed Amazon Kinesis vs. Databricks Report (Updated: March 2025).

Amazon Kinesis vs. Databricks

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Review summaries and opinions

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

Categories and Ranking

Amazon Kinesis

Ranking in Streaming Analytics

2nd

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

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

Ranking in other categories

Cloud Data Warehouse (7th), Data Science Platforms (1st)

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 8.7%, down from 13.9% compared to the previous year. The mindshare of Databricks is 14.6%, up from 10.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Rajni Kumar Jha

Senior Software Engineer at JPMorgan Chase & Co.

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.

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ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

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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Quotes from Members

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

Pros

"The most valuable feature of Amazon Kinesis is real-time data streaming."

"The solution's technical support is flawless."

"One of the best features of Amazon Kinesis is the multi-partition."

"What turns out to be most valuable is its integration with Lambda functions because you can process the data as it comes in. As soon as data comes, you'll fire a Lambda function to process a trench of data."

"The management and analytics are valuable features."

"The feature that I've found most valuable is the replay. That is one of the most valuable in our business. We are business-to-business so replay was an important feature - being able to replay for 24 hours. That's an important feature."

"The integration capabilities of the product are good."

"The Kinesis VideoStream and DataStream are the most important features."

More Amazon Kinesis pros

"The solution is built from Spark and has integration with MLflow, which is important for our use case."

"Databricks helps crunch petabytes of data in a very short period of time."

"The processing capacity is tremendous in the database."

"Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good."

"It's great technology."

"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."

"We can scale the product."

"The tool helps with data processing and analytics with large-scale data or big data since it is associated with managing data at a large scale."

More Databricks pros

Cons

"Lacks first in, first out queuing."

"The price is not much cheaper. So, there is room for improvement in the pricing."

"I think the default settings are far too low."

"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."

"We were charged high costs for the solution’s enhanced fan-out feature."

"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."

"If there were better documentation on optimal sharding strategies then it would be helpful."

"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."

More Amazon Kinesis cons

"I would like to see more documentation in terms of how an end-user could use it, and users like me can easily try it and implement use cases."

"There is room for improvement in the documentation of processes and how it works."

"Costs can quickly add up if you don't plan for it."

"It would be great if Databricks could integrate all the cloud platforms."

"Databricks could improve in some of its functionality."

"In the future, I would like to see Data Lake support. That is something that I'm looking forward to."

"If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks."

"I would love an integration in my desktop IDE. For now, I have to code on their webpage."

More Databricks cons

Pricing and Cost Advice

"The fee is based on the number of hours the service is running."

"It was actually a fairly high volume we were spending. We were spending about 150 a month."

"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."

"Amazon Kinesis pricing is sometimes reasonable and sometimes could be better, depending on the planning, so it's a five out of ten for me."

"Amazon Kinesis is an expensive solution."

"I rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive."

"The solution's pricing is fair."

"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."

More Amazon Kinesis pricing and cost advice

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

"Databricks' cost could be improved."

"The price is okay. It's competitive."

"We have only incurred the cost of our AWS cloud services. This is because during this period, Databricks provided us with an extended evaluation period, and we have not spent much money yet. We are just starting to incur costs this month, I will know more later on the full cost perspective."

"I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself."

"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."

"The pricing depends on the usage itself."

"The solution requires a subscription."

More Databricks pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

18%

Financial Services Firm

17%

Manufacturing Company

Retailer

Financial Services Firm

17%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

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 is moderately priced. In comparison with other competitors, it is fairly priced, however, if they reduced the price a little, it could add more value to customers.

What needs improvement with Amazon Kinesis?

I do not see any scope for improvement as it does what it is supposed to do. No changes are required. Since it's predominantly a back-end service, any end-user isn't going to interact with it direc...

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...

Confluent vs Amazon Kinesis

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Compared 5% of the time

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Compared 11% of the time

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Compared 9% of the time

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Compared 8% of the time

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Compared 7% of the time

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Product Reports

Amazon Kinesis

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Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Overview

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.

Amazon Web Services (AWS)

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

Sample Customers

Zillow, Netflix, Sonos

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

Amazon Kinesis vs. Databricks