Amazon Kinesis provides real-time data streaming with seamless AWS integration, ideal for analytics, data transformation, and external customer feeds. It offers cost-effective data management with high throughput and low latency, supporting multiple programming languages.
| Product | Mindshare (%) |
|---|---|
| Amazon Kinesis | 3.9% |
| Databricks | 7.4% |
| Apache Flink | 6.7% |
| Other | 82.0% |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 9 |
| Large Enterprise | 9 |
| Company Size | Count |
|---|---|
| Small Business | 105 |
| Midsize Enterprise | 48 |
| Large Enterprise | 105 |
Amazon Kinesis enables organizations to manage real-time data streams efficiently. Its integration with AWS ensures seamless setup and operation, while features like auto-scaling and fault tolerance make it reliable for diverse data sources such as IoT devices and server logs. The platform's ability to handle large-scale event-driven systems and dynamic workloads makes it suitable for complex streaming architectures. Despite some challenges with costs and setup complexity, Kinesis remains a popular choice for its efficient data management and processing capabilities.
What are the key features of Amazon Kinesis?In industries such as IoT, finance, and entertainment, Amazon Kinesis facilitates the real-time ingestion and processing of data streams. It connects seamlessly to data lakes and warehouses, enabling businesses to harness data-driven insights without performance loss. This capability is essential for managing dynamic workloads and large-scale event systems. By supporting tools like KDS, Firehose, and Video Streams, Kinesis empowers organizations to respond quickly to changing data environments, enhancing operational effectiveness across different sectors.
Amazon Kinesis was previously known as Amazon AWS Kinesis, AWS Kinesis, Kinesis.
Zillow, Netflix, Sonos
| Author info | Rating | Review Summary |
|---|---|---|
| AWS Cloud Architect at a healthcare company with 10,001+ employees | 3.5 | I am using Amazon Kinesis for real-time streaming, drawn to its easy setup, comprehensive documentation, and seamless AWS integration. Its competitive pricing and KCL library documentation need improvement. We considered Kafka but preferred Kinesis for its ease and AWS compatibility. |
| Director of Software Development at a tech vendor with 10,001+ employees | 4.5 | We've used Amazon Kinesis for reliable stream processing of on-premise security events, leveraging real-time analytics and smooth AWS integration, but are considering alternatives due to high costs despite its excellent stability, scalability, and Lambda integration. |
| Senior Software Engineer at a tech vendor with 10,001+ employees | 4.0 | We use Amazon Kinesis for media and live-streaming data, valuing its real-time data streaming capabilities for call monitoring and transcripts. However, the two-minute time delay in live streaming could be improved. Our deployment is on AWS. |
| Senior Engineering Consultant at ASSURANCE IQ, INC. | 4.5 | In our project, we utilize Amazon Kinesis for logging and temporary buffering, finding its scalability and ease of use valuable. It replaced direct API calls to data warehouses and seamlessly integrates with other analytical tools without needing additional features. |
| Director AWS at NeuraFlash India Pvt Ltd | 4.0 | We use Amazon Kinesis with Amazon Connect for voice and screen recording, valuing its VideoStream and DataStream features for real-time streaming. The product meets our needs effectively, requiring no improvements, and serves primarily developers and architects. |
| Data Engineer at Tiger Analytics | 4.5 | I use Amazon Kinesis Data Streams in my company similarly to Kafka topics. It's a managed service that handles automatic scaling, though it has hard limits leading to throughput errors. We consolidated multiple jobs into one to address this issue. |
| Solutions Architect & PMO at AS TV Play Baltics/TV3 Group | 4.0 | Our company uses Amazon Kinesis to integrate data into our data lake as part of our processing workflow. While it serves its primary purpose well, integrating AI for data processing would be beneficial. However, adding too many features may affect its efficacy. |
| Senior Director of Product Management at Sprinklr | 4.0 | I use Amazon Kinesis for data streaming, mainly to integrate with Redshift and Amazon Connect configurations. It's easy to configure and test but needs improvement in reducing delays, especially with Kinesis Video Streams, for seamless third-party integration. |
| DevOps Engineer at Bipolar Factory | 4.0 | I use Amazon Kinesis in my company for streaming AI-based camera data, appreciating its data streaming and real-time analytics features. However, its machine learning capabilities require improvement to enhance user learning and daily operations. |
| Director of Engineering at MemeSpark LLC | 4.5 | I found Kinesis essential for stable and scalable real-time data streaming and machine learning, particularly its integration with Lambda. It significantly reduced server needs and provided excellent ROI. Auto-scaling streams would be a valuable enhancement. |
Positive
We use Amazon Kinesis for stream processing. We get events from on-premise devices to the cloud. We get many device events and we have to process these events that are coming from the devices. To process the events, we use Amazon Kinesis stream processing. We're using it as a stream processor to analyze the events, to enrich the events and similar tasks.
This is not specifically for IoT. I was into IoT, but now I'm into firewall and security. These are the events coming from firewall, the events coming from on-premise switches or on-premise security devices.
We use Kinesis Data Stream (KDS) to receive the data, process the data through Lambda functions, and then put it in an S3 bucket for further analytics. We also use Amazon Kinesis real-time analytics to analyze the incoming data.
It is effective for stream processing. I can write a Lambda function directly to process the events as the data comes in. The integration between Amazon Kinesis and Lambda helps us significantly.
Real-time analytics in Amazon Kinesis is excellent. We use it for real-time analytics to some extent.
Amazon Kinesis integrates easily with the AWS environment. We use Bedrock, Lambda, KDS, and RDS. Additionally, Amazon Kinesis is simple to set up.
We are contemplating moving away from Amazon Kinesis primarily because of the cost. It is very useful, but if we write our own analytics and data processing pipeline, it would be much cheaper for us. The cost is a primary hindrance. That's why we are not using it widely. For our critical pipeline we are using it, but after that we are putting it in an S3 bucket. Other pipelines directly put the events in an S3 bucket and then process from there.
There is no lack of functions in Amazon Kinesis. Functionality-wise, we feel it's complete. The cost aspect is what we are really concerned about.
We have been using Amazon Kinesis for three years.
I would rate the stability of Amazon Kinesis as high, giving it a 10. We have never had any stability issues.
I would rate the scalability of Amazon Kinesis as a nine. When we had a burst of events, we had some issues, but then we had to scale again.
Positive
We moved away from WSO2. We are currently using AWS API Gateway after moving from the vendor.
We tried both Azure and Google, but we are primarily an AWS shop, so we didn't really explore much on the Google side or Azure side. We have not used them in production grade environments.
If it is a very critical pipeline and organizations can afford the cost, they should certainly go for Amazon Kinesis. On a scale of 1-10, this solution receives a rating of 9.
We use Amazon Kinesis for media streaming and live-streaming data.
The most valuable feature of Amazon Kinesis is real-time data streaming. If a customer calls the contact center and gets connected to an agent, the call is simultaneously streamed and stored in an Amazon S3 bucket. We can simultaneously enable the transcript through Amazon Kinesis on a live call and send that data to any team. We can use the solution for the live monitoring, live review, and live transcript of those calls.
We can use the solution's features to check whether all the data is replicating and stored in Amazon S3.
The solution has a two-minute maximum time delay for live streaming, which could be reduced.
I have been using Amazon Kinesis for ten years.
Amazon Kinesis is a stable solution, and I haven't faced any issues with its stability.
Over 5,000 users are using the solution in our organization. The solution's scalability has impacted 99.9% of our data processing capability because Amazon manages it.
You need not install Amazon Kinesis. Once you create an instance, it will automatically integrate with Amazon Kinesis. After that, you only need to define the S3 bucket path created by Amazon S3. Amazon Connect automatically streams through Amazon Kinesis. In the Amazon Kinesis stream, you need to define the URL. After that, it will go to Amazon S3. We need not do it from our side as Amazon totally manages it.
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.
In our project, my company uses Amazon Kinesis and some other products from AWS. There are also products from Azure, like static web applications or when we need good storage. In my new job, my company is completely dependent on Azure.
The tool is mostly for the logging part and temporary buffer. In our company, we generate lots of logs and some background events, and we want to send an aggregate of those to our data lake. We use the tool for a data stream or for processing things.
The most valuable feature of the solution is that we do not have to worry about other things. After setting the pipeline, we can just get any of the logs, and that will be deposited to the data lake easily.
I am not exactly sure about where improvements are needed in the tool. When I was working on the tool, it was very scalable, and the only thing we needed in our company was temporary streaming stuff that could work well. We didn't want to set up our own Kafka, other queues, or processing systems. As it is a cloud tool, it is easy for us to use the tool, and it satisfies all our requirements. Maybe for the other cases, if we need, then it may need some improvements. The tool satisfies our particular needs.
Currently, the pipeline setup is very simple. For our particular use cases, it is because we just want to get the data and send it to the different data lakes or some logging system. Previously, we also used Amazon Kinesis to log those to Splunk, and later on, we removed Splunk and transferred that to Datadog. For our use cases, I don't want any new features in the tool. Amazon Kinesis' use case is for collecting, processing, and analyzing. If anything can be added to the tool, then I feel one should be able to use the same kind of tool so that everything is there in the product, like an alert system, and so that one can analyze, make a query, and do sourcing from the solution itself rather than using other logging and monitoring systems. The tool should focus on having an alert system rather than having to use a third-party solution. We can just get the data over Amazon Kinesis, and we can directly use all the benefits of current analytical tools, like in the areas involving BI, Looker, and Tableau. One would not need to buy the aforementioned tools, and we can just get started with Amazon Kinesis.
I have been using Amazon Kinesis for three and a half years. I am a user of the tool.
It is a stable solution.
It is a scalable solution. If I include my team, data engineer, and the data centers, I would say that the data center is more focused on data lake only, so Amazon Kinesis is used by around 24 people.
Before Amazon Kinesis, my company used to write those directly through the API call to the endpoint of the data warehouse, and there was no mediation, which was a problem for us. We needed some fast writing for some temporary storage where we could apply it as a data stream, and while we searched for a good solution, we came across Amazon Kinesis.
The product's initial setup phase is not difficult because we are using the tool on the cloud. In our company, we choose the tool's cloud version option because it will be easier rather than setting up our own instances.
The solution is deployed on AWS Cloud.
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.
For the use cases, the only thing with Amazon Kinesis is that you already have another queue system or storage like Kafka or any other already-built streaming systems, so you don't have to invest in Amazon Kinesis. If you don't have anything, you can easily start with Amazon Kinesis.
For the logs, handling the streaming part, and if you already have many AWS products while also needing to send data to S3 or any other storage or cloud tool, it will be very optimal to use. I rate the tool a nine out of ten.
We work with our customers using Kinesis in conjunction with Amazon Connect because Amazon Connect relies on the Kinesis product for voice and screen recording. This is the primary use case for utilizing Kinesis.
If the agents do not receive real-time transcriptions, they will not know the actual words being spoken, impacting the overall experience. Kinesis aids our organization by providing these real-time transcriptions and sentiments processed by other services.
The Kinesis VideoStream and DataStream are the most important features. It provides a high rate of throughput, which is ideal for real-time streaming of voice or video.
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 directly. It's more for developers and architects.
I have been working with Amazon Kinesis for around four to five years.
There could be valid data in Kinesis that is not being processed, which affects stability. Although it rarely happens, this issue has been observed in many deployments, making it not completely stable.
The data stream and sharding are very important features. The sharding gets throttled, allowing scaling based on the number of video calls or voice calls a particular customer encounters. The platform scales up well, offering good configuration.
The customer service and support could be improved because it's a different team, and they take a bit more time analyzing the logs and returning with a response.
Neutral
The initial setup would be rated as seven out of ten. It is generally easy but has a few challenges.
It usually requires the developers and architects for deployment; it could be one to three people.
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.
Ensure you consider the volume and the amount of data you want to process and provision them accordingly. Evaluate the prerequisites well as it's a back-end service.
I'd rate the solution eight out of ten.
I wanted to use the solution in my company for topics, but in the beginning, we did some EOC to use the data analytics part, but then we didn't go with that. We are currently using Amazon Kinesis Data Streams.
The best thing about the tool is that I use it as a normal service. Similar to Kafka topic, we use Amazon Kinesis in our company. It is a managed service that can be shared automatically based on consumption and consumer needs, so we don't have to worry. Sometimes, we also get the throughput error, and there are data contaminants. Multiple jobs try to help the queue.
There are some kind of hard limits on Amazon Kinesis, and if you hit that, then you will get the throughput exceed error. We have to deal with and reduce how many consumers are hitting Amazon Kinesis Data Streams. We combined multiple jobs into a single job, so that there are no multiple jobs in the mainstream.
I have been using Amazon Kinesis for two years. My company has a partnership with the tool.
There is no problem with the tool's stability. I think with the tool, you cannot send or store more than one MB of data in one go. You have to split the two MB of data that you want to store. You have to split it into two messages, and then you can store it. Later on, on the consumer side, you have to deal with it and see how you can merge the data. For most of the devices, the data which we get is not more than one MB. In some use cases, we find the need to do something from our end.
Scalability-wise, I rate the solution a five out of ten since we faced some errors in the tool.
In terms of integrating Amazon Kinesis with other tools, I would say that we don't directly do anything. We have our jobs that consume the data. You have multiple ways to read Amazon Kinesis. If you have a traditional job, then you can use some SDK-based libraries to store data or send data. If you want to consume data, there are ways that you can directly do it, such as using JAR files and reading the data from them. If you have Redshift or Amazon Kinesis Data Firehose, you can directly send data to S3, and then later on, you can consume it. There are multiple options to consume data, but we are currently using a via script, and we are not directly sending it to S3 or some other database.
I recommend the tool to others.
If you have to deal with more than one MB, then you have to deal with your process of how you want to send data to places. I just put the data, and then on the job, you have to combine that, which would be tricky. Another way is to store the metadata and then store the actual data somewhere, like in SQL or somewhere else, and later on, you can put the locations. You need to make sure not to have multiple consumers creating the same data stream. Otherwise, you can get the throughput errors.
I rate the tool a nine out of ten.
My company primarily uses Amazon Kinesis within our data processing workflow to push the data into our data lake.
AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now. Amazon Kinesis has a specific purpose, and it may not fit into use cases anymore if it is loaded with too many features.
I have been using Amazon Kinesis for a year. My company has a partnership with Amazon. My company is also a customer of Amazon.
Stability-wise, I rate the solution a nine out of ten. I have not encountered any problems with the product's stability.
I have not encountered any problems with the product's scalability. Scalability-wise, I rate the solution a nine out of ten.
My company mostly deals with enterprise-sized businesses.
Whether I recommend the product to others depends on the applications, but moving a lot of data in real-time is a very good solution.
The technical support a user receives from Amazon depends on the kind of relationship that they have with AWS. If you are not known by Amazon, support would be low from AWS. If you have a specific relationship, then the support is okay. I rate the technical support an eight out of ten.
Positive
I rate the setup phase a seven on a scale of one to ten, where one is difficult, and ten is easy.
The solution is deployed on a public cloud.
The time required to deploy the solution is something that depends. A quick deployment can be done in a few minutes. If it is a production deployment where all the scripts and other related areas must be managed, it takes a couple of weeks. It is not the deployment itself of the tool alone that is involved since there is a need to prepare everything around it.
I rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive.
In terms of the most useful part of the tool for our company's data analytics, I think that because we decided to use AWS, Amazon Kinesis is present in our organization. It is easy to configure, highly available and offers high throughput. Amazon Kinesis is one of our company's main resources.
The scalability hasn't impacted our real-time data processing capabilities. Actually, users can scale up, and it doesn't impact our company. There might be a positive impact on scalability, so there are no issues.
The product has had a positive impact on operational efficiency because it actually allows our company to filter out some data and save costs.
The integration capabilities of the product are good. My company has integrated Amazon Kinesis with some APIs, AWS Lambda, S3, and Amazon EKS.
The adoption of data processing and data lakes is the purpose for which Amazon Kinesis is designed.
I rate the tool a seven to eight out of ten.
It's a kind of data stream tool that actually does kind of external serverless data streaming to different services at Amazon. Especially for the usage I've done, it's basically for getting data to Redshift.
Kinesis can be one of the sources, and at the same time, Amazon Kinesis can be used to get the data out to other systems through other APIs. Kinesis is one of the tools I have used for typical data streaming.
I've utilized Kinesis Data Streams along with Kinesis Video Streams for different use cases. Again, these are required for working with data as part of Amazon Connect configurations.
Kinesis is fairly easy to use; it's kind of a service that is there and then fairly easy to configure. And it gathers lots of data while having proper authentication mechanisms. It's easy to activate and easy to test and set up as well.
Typically, all the contact tracing codes that are there as part of Amazon Connect and also some of the real-time data can be passed with different sources of streaming. Like, Genesys also has data streaming and video streaming, which actually passes the real-time data as well.
So it's pretty straightforward to configure, easy to understand. It's a typical service that is required in Connect integrations, specifically.
For me, especially with video streams, there's sometimes a kind of delay when the data has to be pumped to other services. This delay could be improved in Kinesis, or especially the Kinesis Video Streams, which is being used for different use cases for Amazon Connect. With that improvement, a lot of other use cases of Amazon Connect integrating with third-party analytic tools would be easier.
It's a stable product overall. So far, I haven't seen any issues wherever I have used it.
This is kind of more like a backend tool used by our developers and the engineering team, including myself, to improve configurations and connectivity. Typically, it depends on the use cases and company requirements, but for my company, three people generally do this work.
The initial setup is pretty easy and straightforward. It can be done pretty quickly with the direct documentation we have from AWS for Amazon Connect and Amazon Kinesis. That helps things to be configured very quickly.
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.
But, from an Amazon Connect perspective, where I have used this, it's quite good. It's pretty much cheaper compared to alternative methods.
I would recommend using Kinesis. Again, it depends on the use cases. One specific advantage of Kinesis is how straightforward it is, and it offers a lot of integration possibilities. So, for those wanting to use it with Amazon Connect, this is a good solution.
Overall, I would rate it between an eight out of ten because of its ease of use. I like the ease of use and how we can quickly get the configurations done, making it pretty straightforward and stable.
I use the solution in my company for streaming purposes, considering that my company has an AI-based camera for streaming.
The most valuable features of the solution are data streaming and the real-time data of our screen, which helps provide the analytics our company needs.
There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required. There is a need to introduce something more into the machine learning area because it helps users learn and get newer things in their day-to-day lives. I think Amazon Kinesis should update machine learning and be up to the mark.
Stability-wise, I rate the solution a nine out of ten.
Scalability-wise, I rate the solution a ten out of ten.
There are around three users of the product in my company.
The solution's technical support is flawless. I rate the technical support a ten out of ten.
Positive
One of the plus points of the product that makes it so much better is that it is very easy to set up, especially since it is managed by AWS, making it a smooth process.
The product falls on a bit of an expensive side.
There is a separate team in my company that looks after the real-time data analytics in our organization, which I don't know much about. As a DevOps engineer, I take care of the cloud, and because of this, I know why my company uses the product.
The most valuable feature of the product for our company's data processing needs stems from the fact that it operates in real-time. The simplicity of the services offered by the product and the way I can use them is very smooth and easy to understand. The tool also provides good storage and an increase in on-demand capacity, which I think is the best for our company.
Integrating Amazon Kinesis with other AWS services has smoothly helped our analytics workflow. It is also very easy to integrate with other AWS services. The analytics part is also very good with Amazon Kinesis.
I recommend the product to those who plan to use it. Those who are new to data streaming and want to start with a new product can go for Amazon Kinesis, as it is very easy to set up, especially the installation part, which is very easy to handle. Amazon Kinesis is the first option others should consider since it is easy to set it up.
I rate the tool an eight out of ten.
We had real-time streaming of data and a very large volume of user activity. We applied machine learning to the data streams. So, Kinesis basically made sure that we got the data, and we didn't lose the data.
I'm not using it at my current organization. I used it at the last company. Kinesis replaced a whole tier of servers. So, we didn't need to have a server to catch the data and then send the data somewhere else. Kinesis was the input port for very large amounts of data.
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.
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.
I'd like to see the size of a Kinesis message go to at least one megabyte per message. That would be nice, but that's an extreme case.
I have been using it since it came out in 2014.
It is stable.
It is scalable. In the previous company, it was customer-facing. So, there were hundreds of thousands of users. There were very large data volumes.
In this company, we'll probably use Kinesis. We haven't used it yet. We have some more projects that'll come along, and then we'll need it.
I was there when it was beta. They were pretty good then, and they're still pretty good. So, they were five out of five. They were right on top of it.
Positive
We didn't use any other solution for streaming data.
It was pretty straightforward. I had one person who did all DevOps. It did not need a dedicated person.
We had seen an ROI. We wouldn't have been able to build the product without it.
It was actually a fairly high volume we were spending. We were spending about 150 a month.
There were some that we considered, but it was a little cruder.
To someone who would like to implement it, I would simply tell not to shove giant bricks in. Data has to be reasonably sized. A single Kinesis message is measured in K, not megabytes. It's not meant for gigantic things. There is a different strategy for streaming data.
I'd rate it at least a nine out of ten. It was very close to perfect.
