Amazon Kinesis Reviews

One use case is consuming sales data and then writing it back into the S3. That's one small use case that we have; from data Shields to data Firehose, from data Firehose to Amazon S3.

There are OneClick data streams that are coming in. For click streams data we established Kinesis data streams and then from Kinesis data streams, we dump data into the S3 using Kinesis Data Firehose. This is the main use case that we have. We did many POC's on Kinesis, as well. Also, one more live project using the DynamoDB database is running in Amazon. From DynamoDB we have triggers that automatically trigger to Lambda, and then from Lambda we call Kinesis then Kinesis writes back into the S3. This is another use case.

Another thing that we did is called Kinesis data analytics where you can directly stream data. For that, we use a Kinesis data producer. From that, we establish a connection to the data stream and then from the data streams to the SQL, which is the Kinesis data analytics tool. From Kinesis analytics, we again establish a connection to the data Firehose and then drive data back into the S3. These are the main use cases that we have for working on Amazon Kinesis.

In my client's company, there is one live database that comes into the DynamoDB. They want to replicate that in Amazon S3 for their data analytics and they do not want data to be refreshed every second. They want their data to be refreshed at a particular size, like five MBs. Kinesis provides data for that. That's the main improvement that we give to the client.

The features that I have found most valuable depend on the use case. I find data Firehose and data streams are much more intelligent than other streaming solutions.

There is a time provision as well as data size. Let's suppose you want to store data within 60 seconds, you can. Let's suppose you want to store data up to a certain size, you can, too. And then you can it write back to the S3. That's the beauty of the solution.

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. That's the one improvement that I'm expecting from Amazon. Apart from that everything is fine.

I have two years of project experience on AWS, and around six months with Kinesis.

I am satisfied with Amazon Kinesis. It is pretty exiting to work on.

Its scalability is very high. There is no maintenance and there is no throughput latency. I think data scalability is high, too. You can ingest gigabytes of data within seconds or milliseconds.

We are a team of five members using Amazon Kinesis. Two are working onshore and three of us are working offshore.

We are all developers implementing, developing, and designing the data pipeline, as well. The thing is we work in a startup company so we have to do all the things from our end on this.

As of now we have not had any contact with customer support because we didn't face any complex types of problems while we were implementing our use cases.

The initial setup is very straightforward. It is very well documented and anyone with simple knowledge or common sense can do it.

Implementing is very simple. You can just do it with your fingertips. There might be some improvements that can be made according to the requirements. For that, we do versioning. First we establish the pipeline from the data stream to the S3. That's very easy. You can do it within hours or within minutes. I can say the process is very simple and it's not as complex as it looks.

One more beauty is that Kinesis data Firehose will directly write to S3 in a partitioned way. Based on the timestamp it can directly write in the year, month, day and hour. That's the good thing I found about Amazon Kinesis.

We follow an implementation. We do the deployment directly on Dev. Once we get our results and our processes, and go through Q&A, we implement it directly throughout.

Our clients definitely see a return on their investment with Amazon Kinesis.

The pricing depends on the number of shards that we are providing and the time the application is running. 

We reduced the cost of the pipeline that we built. We built a generic type of pipeline so that two more times can use same data pipeline.

My advice to anyone thinking about Amazon Kinesis, is that if they have ClickStream or any streaming data which varies from megabytes to gigabytes, they can definitely go for Amazon Kinesis. If they want to do data processing, or batch or streaming analytics, they can choose Amazon Kinesis. And if you want to enable database stream events in Amazon DynamoDB, then you can definitely go for Amazon Kinesis. I don't see any better option for these other than Amazon Kinesis. You can use Amazon Kinesis Data Analytics Tool to detect an anomaly before you process the data. That's one more beauty. The first things we need to determine are the source and the throughput of the data and the latency you want.

On a scale of one to ten I would rate Amazon Kinesis a nine.

In terms of use cases, it depends of which component we're talking about, as we use three of the 4 components. The only one we don't use is the Video Streams.

Kinesis Data Stream is the module that we have been using the longest, essentially we use it to hold data which will be processed by multiple consumers. We have multiple data sources and we use Kinesis to funnel that data which is then consumed by multiple other consumers. We gather data coming from IoT devices, user phones, databases and a variety of other sources and then, as we have multiple consumers, we use Kinesis to actually gather the data and then we process it directly in Lambda, in Firehose, or in other applications.

Amazon Kinesis has absolutely improved our organization. Before Data Streams, we were using a couple of other solutions, including Talend and Pentaho, to move data around. Each of them were their own silos. So the ability to have one single flow of  data from multiple consumers simplified our architecture a lot because you didn't need to copy or read the data multiple times, you just pull that data and then use multiple consumers. It actually simplified our architecture. It will also help us in the future when we have to build additional applications based on the same input data. We already have that data available. It will just be a matter of building the application itself. So it saves us a lot of time.

For Firehose, we perceive time-savings as a result of its incorporation. It takes you a couple of minutes to configure and it saves quite a lot of time in trying to get our information into the data lake. 

Regarding Kinesis Analytics, we have real-time alarms and real-time data flows to populate other systems. For example, we populate Salesforce using a tumbling window implemented with Kinesis Data Analytics and Lambda. We also have alarms for things like knowing when someone is affecting our assets and we need to warn the operators in real-time. So Kinesis Analytics has actually given us the ability to track things in real-time that before we didn't have the ability to track.

Because we couldn't do that in the database we needed a component that had the ability to get the last window of data super quickly and if something was wrong, to notify and identify the failing record or the information that we wanted to trigger and with Lambda to notify the user. At certain points, when we had operational issues, we implemented alarms that have the key indicators to help us attack those issues before they grew and it was too late to attack them. So that has been essential for us.

I think that all Kinesis components have their own features and their own value. Starting from Data Streams, you have to have it as the data queue or else you would need to go to Kafka or another message broker (with higher implementation effort if your ecosystem is fully hosted in AWS already). I think that the solution they have put together in Kinesis is fairly easy to use. It is definitely a core component in any data architecture. 

On the other hand, I find Firehose super simple and super useful for certain use cases. I wouldn't say it is as essential as Data Streams, but it is very handy if you want to just dump data. The connection between Data Streams and Firehose allows you to do that without worrying too much about performance and configuration. I find Firehose super simple to use for a very specific use case, but that use case is very common.

Kinesis Analytics is definitely more cutting edge. Out of Kinesis this is the most innovative part. We have used it for some alarms and for some batch processing in time windows. If we are talking about massive amounts of data, then you need to move to other solutions such as EMR or Glue for big data. If the amount of data is manageable and you want something to analyze on the fly, Kinesis Analytics is very appropriate and it gives you the ability to interact via SQL. So it makes your life easier if you want to develop a relatively self-contained application to do analytics on the fly.

I would say that Data Streams, in a matter of weeks, created a massive time-saving. Something that we haven't factored in is cost savings because we don't need to repeat the same data flow multiple times since each of those data flows are actually cost associated. We're talking about a couple of $100's per month, which is significant. In terms of time-savings here, we are in the scale of weeks. 

In terms of what can be improved, I would say that within Data Streams, you have a variety of ways to interact with the data; you have the Kinesis client library, the KCL, and you have the Kinesis agent. When we were developing our architecture a couple years back, all the libraries to aggregate the data were very problematic. So the Kinesis Aggregator, which essentially improves the performance and cost by aggregating individual records into bigger one, is something that I found had a lot of room for improvement to make it a lot more refined. At the time I found a couple of limitations that I had to work around. So definitely on that side I found room for improvement.

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.

I have been using Amazon Kinesis for over 2 years.

Kinesis is super stable. This is one of the only few components in AWS for which we have never had any issues with the stability.

Regarding scalability, you wouldn't use Kinesis Analytics for huge, vast amounts of data or for complex processing. It's for relatively simple processing with not too much data. So I wouldn't say that it is infinitely scalable, it really depends on your application and the volume of data.

Right now I don't see us using more of Kinesis. It has a very clear role in our architecture and satisfies that perfectly well. This is one of the initial components that you build. In a roadmap that would be the first 10%. All our work is spent in different actions right now, but we don't have any plans to grow Kinesis further. We used to do some specific real-time analysis with Kinesis Analytics on a case by case basis.It's more on a per need basis.

In other companies we use Kafka, but we didn't replace it with Kinesis.

The initial setup is relatively straight forward. 

In terms of the initial setup of Kinesis Streams, is no big deal, you just choose the number of streams and assign a name to your application and that's pretty much it. The effort is in the applications that talk to Kinesis. I would say implementation took around six weeks. Deployment just took two people.

We have our own internal strategy which we started from scratch. So obviously we knew which components we would be deploying first. At the time we didn't use either CloudFormation or CodeBuild. So when we started, we didn't have these tools which we now use all the time for managing the architecture and CICD. But we didn't have it in the initial deployment. 

Amazon Kinesis has improved our ROI. We obviously pay monthly for Kinesis but for us it is an enabler. We wouldn't have an architecture, or we'd have a terrible architecture, if Kinesis wasn't there. 

For the data analytics component, we definitely saw that our ROI clearly improved. The alarms are something that we have actually implemented in very critical tasks when we had a company issue and that we have given visibility and a prompt response to the issues thanks to Kinesis Analytics. So that has definitely proven its ROI. 

In terms of the prices, I think it is a fair price. Kinesis Data Stream has a very fair price relative to the value that it provides. Same for Firehose. As for Kinesis Analytics, I find it on the more expensive side because it's a newer component, something fewer people use, and something more innovative, cutting edge, and more specific. I would say Analytics is more on the expensive side of the spectrum. I would say that Kinesis Analytics is the only one that I may complain about if you like low pricing.

Kafka is comparable to Data Streams, not to Kinesis Analytics. For Analytics on the fly, I can talk about doing Spark streaming, which is a lot more complex and you need to spend a lot more time setting it up, but it also has more capability in terms of the scaling, so I wouldn't say it's a one-to-one comparison.

I also used StreamSets in the past, where you can gather data and you can also do some transformations on the fly. But again it's not comparable one-to-one so I wouldn't use it for the same use cases.

My recommendation for Data Streams is to do a deep dive into the documentation before implementing to avoid what we did at the beginning. You try to process record by record or push record by record into Kinesis and then realize that it is not cost effective or even efficient. So you need to know that you  need to aggregate your data before you push it into Kinesis. So documenting yourself about the best practices in using Kinesis is definitely something I would recommend to anyone. For Kinesis Analytics, I was actually surprised at how easy it is to use an application with such power. I would say with a trial, users will realize that for for such a fairly complex application such as Kinesis Analytics, it is something that you can do very quickly with minimal resources and it gives you a lot of value for specific use cases. 

On a scale of one to ten, I would give Amazon Kinesis a nine. I don't have much to complain about Kinesis.

I work as a senior software engineer in eCommerce analytics company, we have to process a huge amount of data.

Only a few people within our organization use Kinesis. My team, which includes three backend developers, simply wanted to test out different approaches.

We are now in the middle of migrating our existing databases in MySQL and Postgres, to Snowflake. We use Kinesis Firehose to ingest data in Snowflake at the same time that we ingest data in MySQL, without it impacting any performance.

If you ingest two databases in a synchronous way, then the performance is very slow. We wanted to avoid that so we came up with this solution to ingest the data in the stream.

We use Kinesis Firehose to send the data to the stream, which then buffers the data for roughly two minutes. Afterwards, it places the files in an S3 bucket, which is then loaded automatically, via an integration with Snowflake that's called Snowpipe. Snowpipe reads and ingests every message and every file that's in the S3 bucket. This stage doesn't bother us because we don't need to wait for it. We just stream the data — fire and forget. Sometimes, if the record is not ingested successfully, we have to retry. Apart from that, it's great because we don't need to wait and the performance is great.

There are some caveats there, but overall, the performance and the reality of it all has been great. This year, 100% of the time when there was an issue in production, it was due to a bug in our code rather than a bug in Kinesis.

We save a lot of time with Kinesis, but it's difficult to measure just how much. We actually have something similar regarding some other processes. We have developed somewhere else a tool that takes note of the contents of the stream, places them into a file, manually uploads them to the S3, and copies the files into Snowflake. That could be done with Kinesis, but it could take two weeks or 1 month less to get it production-ready.

The first would be the one found in the AWS SDK using the asynchronous client: put Record batch function which allows you to put a list of records in one put record request, which saves time and it's more efficient. Also, by using the asynchronous client, the records are sent in the background using an internal thread pool that can be configurable for your needs. In our performance testing, we came across this setting was the fastest solution. It didn't impact anything in the performance of the system process.

The second one would be the ability to link the stream to other places other than S3 via configuration of the stream and without changing a line of code.

Lastly, you can also link a lambda function to the stream to transform the data as it arrives in before writing it in S3, which is great to perform some aggregations or enrich the data with other data sources.

The default limit that they have, which at the moment is 5,000 records per second (I'm talking about Kinesis Firehose which is a specialized form of the Amazon Kinesis service) seems too low. Actually, on the first week that we deployed it into production, we had to roll it back and ask Amazon to increase the default limits.

It's mentioned in the documentation, but I think the default settings are far too low. The first week it was extremely slow because the records were not properly ingested in the stream, so we had to try it again. This happened the first week that we deployed it into production, but after talking with Amazon, they increased their throttling limits up to 10,000 records. Now it works fine.

We've been using this solution since September 2019.

The stability is great. I'd say that maybe we have it running 99% of the time, and nothing stops it.

Amazon Kinesis is definitely scalable. We have huge spikes of data that get processed around midnight and Kinesis handles it fine.

It automatically scales up and down, We don't need to compute it for that. It's great.

The only time that we needed to contact Amazon was to ask them to increase the throttling limit. They replied to us very quickly and did what we asked.

Initially, we were evaluating Kafka. I think Kafka is faster, but it's less reliable in terms of maintenance; however, when Kafka works, and you have it properly configured, it's much better than Kinesis, to be honest.

On the other hand, Kinesis provides us with better maintenance. Our DevOps team is already oversaturated, so we didn't want to increase the maintenance cost of the production environment. That's why we decided to go with Kinesis; because performance-wise, it's easy to configure and maintain.

I found this solution to be really easy to configure. The essential parts of the configuration include naming the stream and also configuring the buffering time that it takes for a record to get ingested into S3 (how long it will be in the stream until it's put into an S3). You also need to link the Amazon S3 buckets with the Amazon Kinesis stream. After you've completed these configurations, it's pretty much production-ready. It's very, very easy. That's a huge advantage of using this service.

Deployment took a few minutes.

You don't need a deployment plan or an implementation strategy because once you configure it, you can just use a stream. It's not an obligatory version that needs a library, etc. This stream is completely abstract in that way. You only need to configure it once, that's it.

We have seen a return on our investment with Amazon Kinesis. We are able to process data without any issue. It's our solution for ingesting data in other databases, such as Snowflake. 

Developing the stream process manually or using Kafka

If you want to use a stream solution you need to evaluate your needs. If your needs are really performance-based, maybe you should go with Kafka, but for near, real-time performance, I would recommend Amazon Kinesis.

If you need more than one destination for the data that you are ingesting in the stream, you will need to use Amazon Kinesis Data Streams rather than Firehose. If you only want to integrate from one point to another, then Kinesis Firehose is a considerably cheaper option and is much easier to configure. 

From using Kinesis, I have learned a lot about the synchronous way of processing data. We always had a more sequential way of doing things. 

On a scale from one to ten, I would give this solution a rating of eight.

Our primary use case of this solution is for a streaming bus architecture, we get events and they come in through Kinesis like a Jason event. It's usually a change to a database, but it can be any event such as in our application, which feeds into the Kinesis and then we have a Lambda that consumes them and then finally it puts those into a data warehouse which is the ultimate goal. So it's a near real-time data warehouse. 

Instead of doing batch jobs of an ETL, like moving data into a data warehouse, we're now able to do it all through a continuous stream in Kinesis. It means that the data is more up-to-date in our data warehouse, it's more real time.

I've used Kafka in the past and Kinesis is a lot simpler. It's all hosted, it's nice it's really good. There aren't too many knobs and things to turn and ways to screw up. It's a pretty simple product and a lot easier to manage because it's hosted by AWS and it accomplishes what we need it to. The other nice thing is that we can make it available to external customers if they want to get a Kinesis feed of our things.  

I would say that the solution probably has the capability to do sharding so that you can do a lot of things in parallel. I think that the way the sharding works could be simplified and include features that make it easier to scale in a parallel way.

I've been using this solution for five years. 

It's much easier to maintain than a Kafka cluster, but there were some nuances with it. I'd say maybe once a month or once every couple of months we'd get some weird things like AWS getting overwhelmed or the service was degraded for a few hours in a day. I guess that's a drawback of going with the fully managed service, it's just that it depends on the company keeping everything up. To me it's pretty stable, we would just get kind of slow once every month or once every couple of months. Overall I would say it's not perfect, but it was totally great, acceptable.

You can do a bunch of shards, we only use one or a few shards and you can scale way up. It's way more scalable than we ever needed. And we were doing massive, millions of updates per minute. This is a backend service, so it's mostly used by developers and data engineers that were using Kinesis in our company, but our customers all benefited. Customers were sending send data through it, but they don't know that they are using Kinesis. We built the system and then they use it, so they don't know that under the covers it was consistent. We have five to 10 internal employees using it, but probably around 5,000 customers. Where I worked before, we used it basically everywhere. Probably 70% of all of our data was falling through Kinesis. Where I am now, we're just starting, so it's not yet being widely used. We plan to increase usage. 

When we had some of those slow downs, we used AWS support and I can't say that we had a great experience and they resolved the issues, but they looked into some of the flow downs and ultimately we just decided there was nothing we could do. It left me feeling there was something lacking on the technical support side. They didn't get to the bottom of all my issues, but the issues weren't bad enough to be unhappy about the product overall.

We previously used Apache Kafka. We switched because we were already using Amazon for everything else so it made sense, and it was a nice managed solution that would be a lot easier to deal with. It also integrated well with Lambdas. The whole AWS ecosystem is nice to work with because everything integrates with each other.

The initial setup is definitely straightforward because it's a managed service and you only get a few options when you set up a Kinesis stream. It's a lot less overwhelming than setting up a whole Kafka cluster or even if you've managed Kafka, there's still a lot of configuration required to get it up and running and all the choices you can make about topics and things. Kinesis is just much simpler. It only lets you configure what you need to configure. I'd say that kind of POC took about a week and then real production probably a month. We used Terraform for our implementation strategy, but I used CloudFormation in my past job to do that. The deployment was essentially running the CloudFormation template.

Compared to what we were doing with Kinesis or with Kafka, which was taking about 30% just to keep things together, with Kinesis I think we're probably saving tens of thousands, if not $100,000 per year. 

I would say pricing is really great. If pricing is an issue, I'd definitely recommend Kinesis because our Kinesis costs are under $1,000 a month. The product is super cost effective and it's the same with the licensing. Compared to Google Cloud and Azure, they're probably pretty similarly priced. I wouldn't say you're going to get a huge benefit going to Kinesis, but if you're considering using Kafka or another solution that's not hosted, it's not really worth all the effort when you could just go with a managed solution. It's a lot better cost-wise.

We took a look at Google cloud and Azure, they're Pub/Sub solutions, but not really in depth. Because we were already using Amazon, it just didn't make sense to use any other cloud provider.

It's nice to deploy this with the Amazon goodness of Cloud Formation and Terraform, to have it all deployed in a repeatable way. I know that it's easy to go into the console and do it manually, but it's best to do infrastructure as code, in particular with Kinesis.

I would rate this solution a nine out of 10. 

I work in a gaming company that builds games for the global market. We use Amazon Kinesis to stream events. 

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

Amazon Kinesis should improve its limits. 

I have been using the product for a month. 

I rate the tool's stability a ten out of ten. 

My company has two to three users for Amazon Kinesis. 

I rate the tool's deployment a nine out of ten. Deployment takes one day to complete. 

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

I rate Amazon Kinesis a seven out of ten. 

We use the solution for streaming data, in simpler terms. For example, there is a backend application; we need to make that data available for analysis. On the backend side, we don't store the history. We get all the events regarding changes incrementally. If something changes, an event is generated. This is a convenient way to keep track of all the changes.

Amazon Kinesis is similar to Kafka, another type of streaming technology, which can be referred to as a queue service to exchange data. Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it. In comparison, Kafka requires setting up a cluster, even if it is available in the cloud, which can be time-consuming. Amazon Kinesis has a user-friendly interface, making it easy to adjust and scale up the number of shards if needed. The cloud is especially useful when starting something new and not needing a lot of resources initially, but with the potential to upgrade later when there is a larger load. Although there is a cost associated with using the cloud, Amazon Kinesis is very flexible and can be easily adjusted when necessary, making it a great advantage.

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

I have been using the solution for two years.

The solution is stable and I don't recall any issues. Once we set the solution up, it usually works and we only investigate if we encounter a problem. However, if there is a large number of events to process, due to limited capacity for example with the shards, then some events may be delayed. This can be easily resolved by adjusting the configuration to provide more capacity.

The solution is scalable, but this also comes with a financial cost. If we want to increase throughput, we can simply increase the number of shards or adjust some config parameters, which can be done in a matter of minutes if we know how to do it. We can scale the solution almost without limitation.

There are a lot of details involved with the initial setup, so if we need something at the outset, we can set up the solution easily. However, the details are important since they are related to how much money we pay and we need to tailor the solution to our needs. If we want to do something more sophisticated, then we need to spend more time comprehending all the details. Initially, we can easily set something up, but eventually, we need to understand it better and adjust it more to our needs.

Cloud services are often cheaper in the beginning, but when the amount of data and needed resources grows, they cost more and more. In my opinion, it is sometimes simpler to use an existing service rather than having to maintain our own internal infrastructure. This way, we can focus on the things we are good at and can make money from, rather than having to employ people to support the infrastructure. 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. However, if the scale is large, I would consider making changes depending on the situation.

I give the solution a nine out of ten. Amazon Kinesis is easy to use and configure, especially in the beginning. The solution is stable and I have not encountered any issues with it, nor am I aware of any. The solution is effective.

I don't see any missing features in Amazon Kinesis. I haven't spent a lot of time with this interface, as I have only configured it once. If any changes need to be made, I simply adjust Amazon Kinesis and it works. I only go into Amazon Kinesis if there is a need for a new data stream to be included or if the throughput needs to be increased. This doesn't happen very often.

Depending on the requirements, if there is a need to stream data and access it in real time, then I would consider Amazon Kinesis. However, if there is no need for real-time data access, then I will look for some other cheaper options. Companies such as Redshift, Snowflake, and BigQuery are developing databases with built-in streaming functionality. Depending on the case, this may be an option to consider. It also depends on the target; sometimes it is better to use the mechanisms available in the target tool. If we want to have the data on a stream or some hot stories, then I would consider Amazon Kinesis in that case.

We do data acquisition based on what is pumped from the remote data and process it centrally so that we may present to our customers meaningful reports, charts, additional layers of support, or alerts. 

At the moment, I am not using Amazon Kinesis, but Azure Event Hub, which I have found to be more meaningful and easier to use. 

I like the event bubbling feature of Amazon Kinesis, although I ultimately switched to Azure Event Hub. Both solutions have similar features, but the latter offers us certain operational advantages. 

Amazon Kinesis is not a bad product, but Azure Event Hub provides us with certain operational advantages, as our focus is on Microsoft related coding. This is why .NET is what we use at the backend. While we can use both Azure Event Hub and Amazon Kinesis towards this end, I feel the latter to be less customized or developed for use in connection with the server-less programming.

Amazon Kinesis has a less meaningful and easy use than Azure Event Hub. 

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

I have been using Amazon Kinesis for the past year, although I have since switched to Azure Event Hub. 

The scalability is pretty good. One can have any number of nodes spawned or replicated on the primary. Any load can be handled, perhaps a few terabytes with ease in around 15 seconds. One can scale up to this. 

While we have not had occasion to contact Amazon tech support concerning the solution, we have in relation to other matters. We felt it to be good. 

The initial setup and configuration of Amazon Kinesis was more involved than that of Azure Event Hub. 

The solution's pricing is fair. The trick lies in Amazon's pricing. They charge according to the different layers of or types of data that is transfered.

In addition to Azure Event Hub, we also have experience with Apache Kafka, which I feel to offer greater power but more complex configuration. This solution has more features for a variety of purposes. 

The question of whether I would recommend Amazon Kinesis over Azure Event Hub is tricky. While both have their advantages and I consider them to be almost equal, we feel the latter to be better suited to our environment, which is why we went with it. The data transferring policies and associated costs of Amazon were the deciding factors for me.

I rate Amazon Kinesis as an eight or nine out of ten. 

We use this solution for quite large environments.

We use it to capture and process a lot of data. We use it, for example for data analytics and query and analyze a stream's data.

We are a sizable organization and as such, have a lot of data. The solution allows for real-time analysis and you can use a scaler to handle data flows. 

The solution is very flexible and allows for a lot of configuration. It just offers up a lot of possibilities.

I'm using Amazon S3 and Redshift using Amazon server. I can make large configurations and update in near real-time, so that we have real-time use for batch intervals. 

The solution is great for scanning in order to handle environmental data.

The data stream feature on offer is excellent. We use it quite extensively.

The solution works well in rather sizable environments. We deal with a lot of data and it handles it very well.

The solution has a very good alerts system to allow us to respond in real-time.

The dashboards are excellent.

The solution offers very good data capture and integrates well with Power BI and Tableau, for example.

The product makes it very easy to create jobs.

The automation could be better. The solution needs to be better at information capture.

Some jobs have limitations which can make the process a bit challenging.

In order to do a successful setup, the person handling the implementation needs to know the solution very well. You can't just come into it blind and with little to no experience.

I've used the solution for six or seven years or so.

We work with very large environments and haven't had any issues with feeling constricted by the solution.

Personally, based on my past experience and my long history with the solution, the initial setup was not complex. It was pretty straightforward. I find it very easy to use these tools.

A user will need to understand how to create analytics using processing a large amount of information. There may be legacy solutions in the mix as well. A new user will need to understand the environment and all of the requirements before really digging in.

What I will need, basically, is a data map, where I can find any legacy data. From there I can do the setup and it goes pretty smoothly.

I handle the implementation myself.

You can compare this solution to Data Factory and Hadoop. They have a few overlapping characteristics. However, for my industry, Hadoop, for example, wouldn't work as it was lacking some characteristics and parameters and some understanding of the industry itself.

I have a lot of experience in Kinesis and data analytics including in networking in the Amazon AWS environment. My experience is as a big data architect. I draw all environments in AWS. 

On a scale from one to ten, I would rate the solution at a six. It's pretty good, and great for big environments, however, you do need to be well versed in the product to set it up.

We use it for:

• Security 
• DDoS attacks
• Server application firewall
• Kinesis makes it easy to collect the process 
• Real-time analysis
• Streaming data

We can get insight and react quickly to new information.

Kinesis is in real-time. It enables you to process stream data in real-time. You can drive it in seconds or minutes instead of hours or days.

Kinesis is a fully managed program streaming application. You can manage any infrastructure. It is also scalable. Kinesis can handle any amount of data streaming and process data from hundreds to thousands of processes in every source with very low latency.

Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors. 

I have been using Amazon Kinesis for two years. 

We have used their support before. They are quick to help. Support is very good. They can resolve an issue within ten minutes. 

The initial setup was straightforward. The deployment took two months. 

There are 15 employees who work on it in my company. Some roles include myself as the team lead, support, and solution architects. 

I have seen ROI. 

Kinesis has the best of Amazon: data streaming, building processes, data analytics, data in real-time are very good. The output and monitoring are easy. It has good performance.

I would rate it a nine out of ten.