Amazon Kinesis vs Databricks comparison

Read 91 Databricks reviews

20,775 Views
3,740 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: September 2025).

Amazon Kinesis vs. Databricks

September 2025

Download the complete report

Helped 872,655 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:

ROI

Sentiment score

7.0

Organizations benefit financially from Amazon Kinesis through improved data processing, cost savings, and seamless AWS service integration.

Sentiment score

6.6

Organizations benefit from Databricks' cost-effectiveness and efficiency, though some find evaluating immediate gains challenging due to specific contexts.

With Lambda, there is no need for data transfer charges, which is beneficial for less frequent workloads.

For more quotes and insights, download the Amazon Kinesis report

AWS Cloud Architect at a healthcare company with 10,001+ employees

For a lot of different tasks, including machine learning, it is a nice solution.

For more quotes and insights, download the Databricks report

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

When it comes to big data processing, I prefer Databricks over other solutions.

IshwarSukheja

Head CEO at bizmetric

Customer Service

Sentiment score

7.2

Amazon Kinesis support varies, with response quality influenced by user-AWS relationships and complexity of the issues faced.

Sentiment score

7.1

Databricks customer service is praised for responsiveness and expertise, despite occasional delays and communication issues via Microsoft.

We receive prompt support from AWS solution architects or TAMs.

For more quotes and insights, download the Amazon Kinesis report

AWS Cloud Architect at a healthcare company with 10,001+ employees

Whenever we reach out, they respond promptly.

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

As of now, we are raising issues and they are providing solutions without any problems.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.

Lax Kas

Data Engineer at CRAFT Tech

Scalability Issues

Sentiment score

7.3

Amazon Kinesis offers robust scalability with sharding and auto-scaling, ideal for high data throughput, despite some cost considerations.

Sentiment score

7.4

Databricks provides excellent scalability, supporting diverse data sizes and sectors with high-performance cloud infrastructure and cost-effective management.

Amazon Kinesis provides auto-scaling with streams that handle large volumes well.

AWS Cloud Architect at a healthcare company with 10,001+ employees

I would rate the scalability of Amazon Kinesis as a nine.

For more quotes and insights, download the Amazon Kinesis report

Director of Software Development at Cisco

The patches have sometimes caused issues leading to our jobs being paused for about six hours.

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

Databricks is an easily scalable platform.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

I would rate the scalability of this solution as very high, about nine out of ten.

Lax Kas

Data Engineer at CRAFT Tech

Stability Issues

Sentiment score

7.8

Amazon Kinesis is reliable with minor issues, praised for consistent performance and effective fault-tolerance features.

Sentiment score

7.7

Databricks is stable and reliable, with high performance and robustness, despite occasional minor issues resolved quickly.

I would rate the stability of Amazon Kinesis as high, giving it a 10.

For more quotes and insights, download the Amazon Kinesis report

Director of Software Development at Cisco

They release patches that sometimes break our code.

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

Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

Databricks is definitely a very stable product and reliable.

AvivCohen

Data Engineer at Cellebrite

Room For Improvement

Amazon Kinesis users seek enhancements in data aggregation, integration, automation, retention, cost reduction, compatibility, machine learning, and documentation.

Databricks users desire advanced visualization, better integration, enhanced documentation, predictive analytics features, and improved user experience and tools.

There is no lack of functions in Amazon Kinesis. Functionality-wise, we feel it's complete.

Director of Software Development at Cisco

Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes.

For more quotes and insights, download the Amazon Kinesis report

AWS Cloud Architect at a healthcare company with 10,001+ employees

Adjusting features like worker nodes and node utilization during cluster creation could mitigate these failures.

ShubhamSharma7

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

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.

For more quotes and insights, download the Databricks report

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

We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.

Rama Subba Reddy Thavva

Solution Architect at Mercedes-Benz AG

Setup Cost

Amazon Kinesis offers competitive pricing, though costs rise with scaling, large data volumes, and Kinesis Analytics can be expensive.

Databricks' pricing is seen as high for large data volumes but competitive for batch processing on cloud platforms.

Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.

For more quotes and insights, download the Amazon Kinesis report

AWS Cloud Architect at a healthcare company with 10,001+ employees

It is not a cheap solution.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

Valuable Features

Amazon Kinesis provides easy, scalable streaming with AWS integration, supporting analytics and monitoring without complex infrastructure management.

Databricks simplifies large-scale analytics with user-friendly UI, powerful integrations, and scalable features for enhanced performance and collaboration.

Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.

AWS Cloud Architect at a healthcare company with 10,001+ employees

Amazon Kinesis integrates easily with the AWS environment.

For more quotes and insights, download the Amazon Kinesis report

Director of Software Development at Cisco

Databricks' capability to process data in parallel enhances data processing speed.

ShubhamSharma7

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

The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.

For more quotes and insights, download the Databricks report

Data Platform Architect at KELLANOVA

The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.

Lax Kas

Data Engineer at CRAFT Tech

Categories and Ranking

Amazon Kinesis

Ranking in Streaming Analytics

2nd

Average Rating

8.0

Reviews Sentiment

7.0

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 (9th), Data Science Platforms (1st)

Mindshare comparison

As of October 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 6.7%, down from 10.5% compared to the previous year. The mindshare of Databricks is 12.5%, down from 12.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Databricks	12.5%
Amazon Kinesis	6.7%
Other	80.8%

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

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

872,655 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

18%

Financial Services Firm

17%

Manufacturing Company

Comms Service Provider

Financial Services Firm

18%

Computer Software Company

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	10
Large Enterprise	9

By reviewers
Company Size	Count
Small Business	25
Midsize Enterprise	12
Large Enterprise	56

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 and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.

What needs improvement with Amazon Kinesis?

Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes. Also, the KCL library's documentation could be improved to better explain the configuration parameters...

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...

Apache Flink vs Amazon Kinesis

Comparisons

Compared 25% of the time

Confluent vs Amazon Kinesis

Compared 24% of the time

Azure Stream Analytics vs Amazon Kinesis

Compared 19% of the time

Redpanda vs Amazon Kinesis

Compared 9% of the time

Amazon MSK vs Amazon Kinesis

Compared 6% of the time

More Amazon Kinesis Competitors

Microsoft Power BI vs Databricks

Compared 9% of the time

Dataiku vs Databricks

Compared 8% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Dremio vs Databricks

Compared 5% of the time

Tableau Enterprise vs Databricks

Compared 4% of the time

More Databricks Competitors

Product Reports

Download Amazon Kinesis product report

Amazon Kinesis

October 2025

Download Databricks product report

October 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 offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.

Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.

What features make Databricks unique?

Notebook: Enables collaborative work among team members.
Delta Lake: Optimizes data management operations.
Unity Catalog: Provides governance over data assets.
Cloud Integration: Seamlessly connects with major cloud platforms.

What benefits can users expect from Databricks?

Versatility: Supports diverse applications in data science and engineering.
Performance: Delivers efficient handling of large-scale analytics tasks.
Collaboration: Enhances teamwork in data projects.
Unified Environment: Centralizes machine learning and analytics activities.

In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.

Sample Customers

Zillow, Netflix, Sonos

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