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

January 2025

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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 February 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 9.1%, down from 14.3% compared to the previous year. The mindshare of Databricks is 14.1%, up from 9.9% 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

"Everything is hosted and simple."

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

"The product's initial setup phase is not difficult because we are using the tool on the cloud."

"The most valuable feature is that it has a pretty robust way of capturing things."

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

"Amazon Kinesis has improved our ROI."

"The scalability is pretty good."

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

More Amazon Kinesis pros

"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"

"The most valuable feature is the Spark cluster which is very fast for heavy loads, big data processing and Pi Spark."

"The fast data loading process and data storage capabilities are great."

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

"Databricks integrates well with other solutions."

"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."

"Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes."

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

More Databricks pros

Cons

"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."

"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."

"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."

"The tool should focus on having an alert system rather than having to use a third-party solution."

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

"Lacks first in, first out queuing."

"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 services which are described in the documentation could use some visual presentation because for someone who is new to the solution the documentation is not easy to follow or beginner friendly and can leave a person feeling helpless."

More Amazon Kinesis cons

"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."

"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."

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

"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."

"Doesn't provide a lot of credits or trial options."

"The integration and query capabilities can be improved."

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

"It would be very helpful if Databricks could integrate with platforms in addition to Azure."

More Databricks cons

Pricing and Cost Advice

"Amazon Kinesis is an expensive solution."

"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."

"The tool's pricing is cheap."

"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."

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

More Amazon Kinesis pricing and cost advice

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

"Databricks uses a price-per-use model, where you can use as much compute as you need."

"We're charged on what the data throughput is and also what the compute time is."

"Databricks' cost could be improved."

"I would rate the tool’s pricing an 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 solution is a good value for batch processing and huge workloads."

"The pricing depends on the usage itself."

More Databricks pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

18%

Financial Services Firm

18%

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...

Azure Stream Analytics vs Amazon Kinesis

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Compared 8% of the time

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Product Reports

Download Amazon Kinesis product report

Amazon Kinesis

February 2025

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February 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