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: January 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

Data Science Platforms (1st)

Mindshare comparison

As of January 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 9.8%, down from 15.3% 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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Parag Bhosale

Senior Data Engineer at a logistics company with 51-200 employees

Integrating engineering and learning, but cost challenges arise with cluster management

We often use a single cluster to ingest Databricks, which Databricks doesn't recommend. They suggest using a no-cluster solution like job clusters. This can be overwhelming for us because we started smaller. We prefer using a small to mid-sized cluster for many jobs to keep costs low, but this sometimes doesn't support our operations properly. We need to stay in sync with the DVR versions, and migrations can pose challenges. For example, issues arose when we moved a cluster from a previous version to the latest one. We could use their job clusters, however, that increases costs, which is challenging for us as a startup. Maintaining this infrastructure can be a headache.

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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 Kinesis VideoStream and DataStream are the most important features."

"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."

"The solution's technical support is flawless."

"I have worked in companies that build tools in-house. They face scaling challenges."

"There is no problem with the tool's stability."

"Great auto-scaling, auto-sharing, and auto-correction features."

"The integration capabilities of the product are good."

"Amazon Kinesis also provides us with plenty of flexibility."

More Amazon Kinesis pros

"The time travel feature is the solution's most valuable aspect."

"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."

"The most valuable feature is the ability to use SQL directly with Databricks."

"When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks."

"It helps integrate data science and machine learning capabilities."

"The solution is very easy to use."

"The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions."

"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."

More Databricks pros

Cons

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

"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."

"Amazon Kinesis should improve its limits."

"Something else to mention is that we use Kinesis with Lambda a lot and the fact that you can only connect one Stream to one Lambda, I find is a limiting factor. I would definitely recommend to remove that constraint."

"One thing that would be nice would be a policy for increasing the number of Kinesis streams because that's the one thing that's constant. You can change it in real time, but somebody has to change it, or you have to set some kind of meter. So, auto-scaling of adding and removing streams would be nice."

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

"It would be beneficial if Amazon Kinesis provided document based support on the internet to be able to read the data from the Kinesis site."

"The solution has a two-minute maximum time delay for live streaming, which could be reduced."

More Amazon Kinesis cons

"I believe that this product could be improved by becoming more user-friendly."

"I would like more integration with SQL for using data in different workspaces."

"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."

"The integration and query capabilities can be improved."

"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."

"It would be better if it were faster. It can be slow, and it can be super fast for big data. But for small data, sometimes there is a sub-second response, which can be considered slow. In the next release, I would like to have automatic creation of APIs because they don't have it at the moment, and I spend a lot of time building them."

"They release patches that sometimes break our code. These patches are supposed to fix issues, but sometimes they cause disruptions."

"It should have more compatible and more advanced visualization and machine learning libraries."

More Databricks cons

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."

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

"The tool's entry price is cheap. However, pricing increases with data volume."

"The solution's pricing is fair."

"Amazon Kinesis is an expensive solution."

"The tool's pricing is cheap."

"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."

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

More Amazon Kinesis pricing and cost advice

"There are different versions."

"I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."

"My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month."

"I rate the price of Databricks as eight out of ten."

"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."

"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."

"The billing of Databricks can be difficult and should improve."

"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."

More Databricks pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

18%

Manufacturing Company

10%

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...

Azure Stream Analytics vs Amazon Kinesis

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Compared 8% of the time

Dremio vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

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

Learn More

Amazon Web Services (AWS)

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.

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