Azure Stream Analytics Reviews

We use the solution to analyze application logs. It helps review incidents and production issues.

The solution's most valuable feature is its ability to create a query using SQ.

The solution's interface could be simpler to understand for non-technical people. Also, the chart feature could be more user-friendly. Presenting or elaborating on an incident to management or executives from other non-technical departments becomes challenging. We have to create another graph and include it in the presentation slides.

We have been using the solution for two years.

The solution is stable.

The solution is scalable. We have around more than 100 developers using it.

We follow virtual documentation in case of technical errors for the solution.

If you have Azure DevOps or are using the Azure ecosystem, you must go for the solution. I rate it an eight out of ten.

We mainly use this product for real-time data injection and reporting purposes.

We find the query editor feature of this solution extremely valuable for our business.

Also, the multiple window types that are available in this solution, are extremely helpful.

The solution doesn't handle large data packets very efficiently, which could be improved upon.

We would also like more variation in the output types that this solution can produce.

We have been using this solution for three or four years.

The stability of this solution was problematic initially, but it has improved as later versions have been released, and is now very good.

We have found this product to be easily scalable.

The initial setup of this solution is very easy, and the deployment takes place instantly.

The licensing for this product is payable on a 'pay as you go' basis.  This means that the cost is only based on data volume, and the frequency that the solution is used.

This solution is easy to use and has good fault tolerance, so I would recommend it to other organizations.

I would rate this solution an eight out of ten.

When it comes to doing partitioning for the data, if you are working with both ModiB and MongoDB, it's very powerful in Microsoft Azure - the clustering platform. If it's Spark Apache, Spark will be very powerful due to the fact that you have the option to have the Delta factory, which makes all that things available within the platform. 

It allows you to take a proactive decision for any abnormal functionality. For IoT, it will give you the facility to make quick decisions on any kind of metrics or output, et cetera.

The solution has a lot of functionality that can be pushed out to companies. There's a lot of analytics to take advantage of. What they have available on the market now is more than enough for companies to work with.

You can install the solution quite quickly and start using it right away.

While it depends on the business scenario, in some cases AWS offers better features. It's hard to speak to missing features at it really depends on the business case. However, in general, it has all the features a typical company might need.

The solution needs to be marketed better. Developers should be pushed or enticed to use the solution more to get it more well-known on the market. It needs more of a presence.

The solution offers a free trial, however, it is too short. You can't really properly test it before you have to start paying. They need to give companies a longer period of time to try it out risk-free. Also, the functionality is very limited. If you want to do a POC, you need the solution to offer more flexibility. Right now, you get a 14-day window, and that's not enough for a proper test.

I've used the solution in the past 12 months.

The solution is very secure. Occasionally we might get a bug or to, however, this typically happens outside of the solution. If you are within the scope of Azure, it functions very well and there is good support from Microsoft if you run into issues. Basically, there are no major bugs or glitches to contend with, unless you have a design flaw. It works quite well.

Scaling is okay, so long as the cluster isn't overloaded.

Microsoft offers excellent technical support. They are very helpful and supportive. We are quite satisfied with the level of service they provide.

The initial setup is very fast. You can set it up and just start using it. In that sense, it's great. A company shouldn't have any issues with getting it up and running.

The product does have a free trial offer, however, it is much too short. It's only 14 days and that's not enough time to run a proper POC.

Overall, we've been quite happy with the product. I would rate it at a nine out of ten.

The company I'm working for is basically one of the biggest companies in the entire Gulf region, including Dubai, Qatar, and Oman. Our core domain is providing logistics. They have different warehouses across the country, and we use it to track the movement of forklifts and people working at the warehouses. 

The main thing we are focusing on right now is accident avoidance. For example, one forklift is coming through one aisle, and another person is trying to enter the same aisle. We provide a solution that can track the person and forklift in real-time. 

We're also using it to solve many business problems one by one. Stream Analytics plays a major role in streaming all these huge datasets because we have warehouses spread across the country. It's able to handle millions and millions of events in a few seconds.

If we're not using Stream Analytics, how can we track the real-time? From the end user's perspective, Stream Analytics forms the main backbone for the entire pipeline and all the technologies.

The company can see the real-time location and track everything, just because of Stream Analytics. Without Azure Stream Analytics, we can't do any real-time tracking. We can use other messaging systems like SQL, but when it comes to scaling, collecting, getting a lot of events, recalling it, find out where it's used, Stream Analytics is better. You might have to collect from millions and millions of services and devices and beacons. All of that would be pushing the data into the Stream Analytics. 

I like the IoT part. We have mostly used Azure Stream Analytics services for it. This is the most valuable part because this is using the streaming service. It's valuable because there's no other way for us to handle it. 

It has the support of Azure storage, long storage, and access data. It has support from the SQL server. Azure also supports added access to data because we need the data from static data and dynamic data to represent them, and that's not going to change very frequently.

When we are getting the warehouse's location through this stream analytics, we have to merge some information from our static database, and finally, we have to show it all within a dashboard or something on the map.

Without the streaming analytics part, I don't think it's possible to handle it. We can use some other messaging system, but we might have some scaling issues and among others too. I know that Stream Analytics is fantastic in that we don't have to worry about any other activities. We can further scale it too. We can go for the upgraded service if needed, based on our traffic and the number of data we have been receiving.

Natively, I found it beneficial, and the integration was smooth. We're already using some other Microsoft technology packs, so it's easy to integrate them all.

As the Stream input and output enables very smooth integration with other cloud services, for example, Azure Cloud Concepts, or Cosmos DB, with minimum coding, and with the minimum level of queries, we can directly output all these outputs and push the normal data for historical data storage.

The collection and analysis of historical data could be better. We use historical data and an assimilating algorithm to give us insights into the entire business process. 

We can collect all the historical data periodically to get insights into current business trends. For example, which area is getting emptied most of the time or which area is getting underutilized, and so on. 

I've been working with Azure Stream Analytics for about two years.

We don't have to worry about scalability. It's in the cloud and can have millions and millions of things connected. The software part is easy to scale. You just have to add all the hardware. For the web application, the hosting part can be scaled. We don't have to worry about the desktop as the solution is deployed in the cloud. The scalability is based on our choices. It's not like it's manually hosted in private, and we have to scale it vertically.

Our infrastructure team has the flexibility to call the Microsoft guys to look into the matter if there is something wrong on their part.

The initial setup was very complex because of the hardware. We had to spend almost an entire day just to put the hardware part in the right places, following some best practices. 

It took us more than one and a half years, and we're still left with some deployments to do. We initially tested it in a few small areas, and then we expanded it to cover the entire area.

I found it a little challenging, we struggled, and we did it. We're still doing a lot of stuff for the elite features and other deployments. We follow the deployment strategy, and it's almost automated. We're trying to add a few features and deploying them. The final stage of deployment is where the rest of the entire process is done through continuous integration.

It requires maintenance in terms of hardware and the software part. I don't think any solution is totally bug-free. We generate service requests all the time, and they are fixing it.

The IoT hardware requires more maintenance because we know we have limited battery life, and we have to check all the devices. We need to keep checking those things, and we have automated that. But it still needs to be manually reconnected to the battery.

We have a team of people supporting this project. We have about ten members, some of whom were core developers. Four or five developers developed the cloud part. Two hardware engineers were responsible for all these deployments in the warehouse.

I would advise potential users to properly plan and structure their static data and the reference data before putting it into the Stream Analytics.  

On a scale from one to ten, I would give Azure Stream Analytics an eight.

Our primary use case is mainly to ingest real time data streams into permanent storage places like databases, block storage, etc.

The biggest improvement for us has been that it now takes much less time for us to receive valuable information. Basically, as soon as it appears in our real time data source, in a matter of seconds, it is already in our database.

The value of this solution is the deep integration it provides with other Azure resources which we use a lot. Our whole infrastructure is pretty much based on Azure so ease of integration is a valuable feature. Secondly, the simplicity of the solution is great. You don't need to set up much, you just make a selection, select a destination, and you're off. 

There are some improvements that could be made, first of all in pricing, because right now the pricing is a bit unclear. It's hard to estimate how much of that is a local issue but you can't figure out how prices are calculated or the proprietary part of the cost. Another area that could be improved is that if something does go wrong, it's very hard to investigate what caused it and why. The logging is available but it lacks detail and doesn't provide much information.

I've been using this solution for two years. 

The solution has an acceptable level of stability although, as mentioned, if it does fail, it's pretty difficult to find out the cause. 

It's very easy to scale this solution. We probably have a couple of hundred users and we have developers who deal with maintenance. This is our main tool for real time data streaming. 

The initial setup is quite straightforward. Because of the good integration, you select your real time data, store the destination where you want to write it and you probably don't even need to transform with data. You basically create a mapping descent source. We had a proof of concept in place, so I would say deployment took two working days without having a deployment plan. 

We have a good ROI because we are able to deliver solutions very quickly and customers are happy with that. 

We evaluated and carried out a comparison with Oracle. The results were pretty much the same for both in terms of real time data streams, but were very much tied to their own cloud solutions. If you work with Oracle i'ts probably best to go with Amazon.

My simple advice would be to not scale up initially. Also, if you have questions don't just rely on the official documentation, but use other resources such as a blog by a developer, because sometimes that can be more helpful than documentation provided by the company.

The best advice I can offer would be that if there is a simple solution available, do not try to complicate things. 

I would rate this solution an eight out of 10. 

This solution is connected with our Microsoft license. We use the E3 license, which also includes the software and analytics. We bought it and we are trying to get the best we can from the software. For now, we have some analytics, what's happening, and one of our guys looks at it and prepares reports or maybe requests some additional interventions.

It is mostly for analysis so that when something happens we can analyze it and do something about it.

Our biggest pain point is our housekeeping. We have lot of systems, we must do a lot of configurations and, because of the number of systems is not a simple part of the job, we may forget something. This solution is something which reminds us of what should we do, what stuff is most critical, what we should work on.

We would like to have something that includes the desktops, and perhaps the main system, so we can protect our systems before a threat happens.

My experience with it is that it is stable. The stability is okay and we are satisfied with it.

Our system has approximately 700 servers and a lot of other components and we are satisfied with it at this level.

We went with it because we had it. We had the opportunity to use it and started to do so. Before that, we had several areas where we wanted to improve our status. This is a tool we can check with from Microsoft and, for now, it is good for us.

The most important criteria when selecting a vendor is that the product is user-friendly. A lot of tools that are rated highly in industry reviews have complicated configurations. We would like something which is friendly enough for its administrator to use, to configure. Another criterion is price. We look for solutions we can afford. We read a lot of free white papers and try to find partners that have favorable write-ups. We use the software and do a phone conference where we can discuss further questions which are important to us.

Our firm works in the former Yugoslavia: Croatia, Bosnia, and Serbia, Macedonia, and Montenegro. The prices are different in these markets. Of course, we always want to have the lowest price but that doesn't mean price is the main factor when buying something. It is one of them, for sure. Maybe we cannot afford the most sophisticated tool there is, so price is important, but not the most important thing when we buy something.

We use Microsoft Analytics in the security department to try to raise the level of security analysis and security in general. We have implemented a lot of tools. We also have Nexpose for threat analytics, among others. Microsoft Analytics is just one of them and we implemented it because we already had it.

We tried to buy something for the desktop level. We also have systems like anti-virus, anti-malware, but these are all systems which only partly cover the threats which are now mainstream. So we would like to have a tool we can use that is faster, maybe with predictive analytics.

We have a mixed system. We also use IBM AIX in addition to the Microsoft platform. That means that ATP works successfully with Microsoft for us. If you want to implement it, the benefits are greater if you have a Microsoft in the surrounding systems.

Implementation is always easy. The hard part is then when you must have to use it, when you must work with it. An important part is education, this shouldn't be neglected.

I would rate Azure Analytics at eight out of 10. It gives us what we need, we are satisfied with the results.