Amazon Kinesis vs Databricks comparison

Read 89 Databricks reviews

5,271 Views
3,846 Comparison Views

96% willing to recommend

Amazon Kinesis

Comparison Buyer's Guide

Download the report

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: April 2025).

Amazon Kinesis vs. Databricks

April 2025

Download the complete report

Helped 850,671 peers since 2012

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 May 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 8.3%, down from 13.6% compared to the previous year. The mindshare of Databricks is 14.6%, up from 10.5% 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.

Read full review

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.

Read full review

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."

"Amazon Kinesis has improved our ROI."

"Everything is hosted and simple."

"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."

"What I like about Amazon Kinesis is that it's very effective for small businesses. It's a well-managed solution with excellent reporting. Amazon Kinesis is also easy to use, and even a novice developer can work with it, versus Apache Kafka, which requires expertise."

"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."

"The management and analytics are valuable features."

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

More Amazon Kinesis pros

"Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance."

"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."

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

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

"We have the ability to scale, collaborate and do machine learning."

"Databricks serves as a single platform that can handle numerous end-to-end machine learning tasks."

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

"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."

More Databricks pros

Cons

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

"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."

"Amazon Kinesis should improve its limits."

"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."

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

"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."

"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."

"Kinesis can be expensive, especially when dealing with large volumes of data."

More Amazon Kinesis cons

"Databricks can improve by making the documentation better."

"Can be improved by including drag-and-drop features."

"Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics."

"The API deployment and model deployment are not easy on the Databricks side."

"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."

"There has been a significant evolution in databases. One area of improvement is the Databricks File System (DBFS), where command-line challenges arise when accessing files."

"The connectivity with various BI tools could be improved, specifically the performance and real time integration."

"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."

More Databricks cons

Pricing and Cost Advice

"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."

"Under $1,000 per month."

"Amazon Kinesis is an expensive solution."

"The tool's pricing is cheap."

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

"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."

"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

"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."

"It is an expensive tool. The licensing model is a pay-as-you-go one."

"The pricing depends on the usage itself."

"We pay as we go, so there isn't a fixed price. It's charged by the unit. I don't have any details detail about how they measure this, but it should be a mix between processing and quantity of data handled. We run a simulation based on our use cases, which gives us an estimate. We've been monitoring this, and the costs have met our expectations."

"The solution is based on a licensing model."

"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."

"Databricks are not costly when compared with other solutions' prices."

"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."

More Databricks pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

850,671 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

18%

Financial Services Firm

17%

Manufacturing Company

10%

Retailer

Financial Services Firm

18%

Computer Software Company

10%

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

Comparisons

Compared 31% of the time

Azure Stream Analytics vs Amazon Kinesis

Compared 23% of the time

Apache Flink vs Amazon Kinesis

Compared 18% of the time

Amazon MSK vs Amazon Kinesis

Compared 8% of the time

Google Cloud Dataflow vs Amazon Kinesis

Compared 5% of the time

More Amazon Kinesis Competitors

Dataiku vs Databricks

Compared 11% of the time

Microsoft Power BI vs Databricks

Compared 9% of the time

Dremio vs Databricks

Compared 7% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Tableau vs Databricks

Compared 5% of the time

More Databricks Competitors

Product Reports

Download Amazon Kinesis product report

Amazon Kinesis

May 2025

Download Databricks product report

May 2025

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