Microsoft Azure Synapse Analytics Reviews

We use it for reporting purposes and for building data warehouses, pipelines, and data lakes for IoT projects.

I like how Microsoft Azure Synapse Analytics integrates with other Microsoft solutions. 

Real-time integration is hard to do in Microsoft Azure Synapse Analytics.

I've been using Microsoft Azure Synapse Analytics for two years.

I would rate the solution's stability at nine out of ten.

For the amount of data that we have, Microsoft Azure Synapse Analytics has worked perfectly fine in terms of scalability. Thus, for us the scalability is a ten out of ten.

The documentation is good, but if you cannot find the right documentation and the right steps, then the initial setup can be hard. I would rate the initial deployment at seven out of ten.

This solution is deployed on the cloud, and it took half a day to deploy it. I configured the data lake storage, created the workspace, and added users and user rights. Building pipelines is a straightforward process.

I think Microsoft Azure Synapse Analytics is priced well, and I would rate the price at eight out of ten.

On a scale from one to ten, I would rate Microsoft Azure Synapse Analytics at seven. Other solutions have more user-friendly and intuitive interfaces.

We are an IT service provider. We develop solutions for our clients and this solution is used in some aspects of that.

The dashboards are good.

We haven't had any issues when it comes to customization.

The security seems to be okay. It hasn't given us any problems and we don't feel insecure.

The pricing seems to be quite fair.

I know their roadmap, and I am looking forward to what they have already published.

We've been happy with the level of reporting and documentation on offer.

There aren't any features that are really lacking in the solution. We don't really have any issues with it in its current form.

When we used the on-premises deployment model, we had data latency issues and suffered from page performance problems. However, since we've moved to the cloud, we haven't had these problems.

The initial setup has a bit of a learning curve.

We've been using the solution for the past 18 months so far.

The solution seems to be scalable. We haven't had any issues with bugs or glitches. It doesn't crash or freeze. It's quite good.

The scalability has been pretty good, in our experience. We have been able to scale it twice to the number of users we needed. We were also able to double the amount of data load. We haven't had issues. If a company needs to expand, it should be able to do so without problems.

We've dealt with technical support and have found them to be quite responsive and knowledgeable. We're quite satisfied with the level of support we have been getting so far. They're helpful.

It's hard to gauge the difficulty or simplicity of the setup. For our company, we were learning the processes as we were doing it. In that sense, it was challenging due to the fact that we had to learn and then do. After a period of time, we were able to get comfortable.

We handled the implementation ourselves. We taught ourselves about the process as we went.

We pay a licensing fee on a monthly basis. We find the pricing to be fair among the competition.

We're just end-users and customers. We don't have a business relationship with the organization.

We're using the latest version of the solution right now.

I'd recommend the solution to other organizations.

I'd rate the solution at a nine out of ten.

I work in the financial industry where the most important thing is PII so the solution's data masking feature is very useful for clients. 

The solution operates like a typical SQL Server environment so there is no alienation in terms of technical knowledge. A developer will feel at home using the solution. 

The product needs a tool that allows for work from a laptop instead of a browser. When working in on-premises environments, it is important to have all tools installed on a laptop rather than relying on internet connectivity which is a big inconvenience. For example, it would be brilliant to add an integration on Visual Studio to create all pipelines in an offline mode. 

The solution cannot store much data and might require purchasing additional storage. For example if you have 1 TB of data, processing it in the solution will cost ten times more than processing it in Databricks. 

I have been using the solution for seven months. 

The solution's stability is good. 

The solution is highly scalable. For example, if you have 100 DWUs, you can increase up to 20,000 DWUs by letting Microsoft know your requirements. Your needs for DWUs will be satisfied based on how much you are willing to pay.

Support wavers depending on the size of the customer. For example, a Fortune 500 customer will receive tremendous support and quick turnaround time. 

Smaller customers might find it challenging to receive support. 

The initial setup is very easy.

The solution is very expensive because it often requires the purchase of additional storage. Many customers are willing to pay for something that is available in the open source market. If the price of the solution isn't reduced, it will not sell well in the future. I rate the price an eight out of ten. 

Our company heavily promotes Databricks because of the solution's cost impact on our clients. Databricks provides a data lake and a warehouse framework that gives the same or better performance when compared to Synapse, but there is a massive learning curve to using it. Most customers are not aware of Databricks so they don't understand it or use it. 

Two negatives of Databricks are that it is cloud native with no option for an on-premises server and it does not have good integration with any of the IDs. I provided this feedback while I was in training and their team acknowledged the web browser is an issue to resolve. Most developers do not like to work in a browser environment because accidently hitting F5 will cause complete loss or the internet goes down and all changes are lost. These type of issues do not occur when working in offline mode. 

Six months ago, Databricks launched a new product called Databricks SQL that offers multiple platforms such as data engineering with or without a SQL server environment. There is a focus on targeting developers familiar with SQL because the product will only require a different kind of syntax but the SQL environment is still there. Databricks is investing heavily in training developers free of cost and providing certification seminars to increase knowledge of the product as it evolves. 

There are use cases for Azure Synapse with customers who do not want to move away from SQL Server or want a similar experience. The solution offers the ability to create an on-premises server with a SQL syntax that is familiar to developers with no learning curve. Most customers prefer Synapse until they realize the high cost and then they switch to Databricks. 

I rate the solution an eight out of ten. 

We have a huge amount of data, and we need a platform where we can process the data using Spark clusters. We wanted an environment that included databases that are capable of parallel processing, and Azure Synapse was one of the best for that.

This product has given us a lot of processing power with the database, and at the backend as well.

The most valuable feature is the level of processing power, and being able to complete tasks in parallel. This is the feature that attracts us the most because we have a lot of data that we need to process in a short time.

This is a user-friendly environment.

Integration with other products is an area that can be improved.

I have been working with Microsoft Azure Synapse Analytics for between five and six months.

Synapse definitely seems like a stable product.

Synapse Analytics is deployed in a cloud environment and one of the best advantages of this is that it provides scalability.

I have not had any experience with Microsoft technical support.

I also have experience with the services available on AWS.

Configuring this solution was easy.

We implemented it with our in-house team. I was part of the team, one of my colleagues was the solution architect, and we have some engineers that assisted us. It was a team effort.

The costs are billed such that we pay for what we use, which is a good thing. For example, we have an environment where we don't need access to certain services, so we don't have to pay for them.

Compared to the services available on AWS, this solution is a little bit pricey.

At this point, Synapse Analytics is fulfilling all of our requirements for the use cases that we have.

I would rate this solution a seven out of ten.

The solution is generally used as more of an Analytical Data Warehouse. It's more of an offline enterprise scale. Sometimes I've used it from a marketing perspective, as a marketing data platform. I've not really used it from a risk and the enterprise center as it tends to be more back office, and therefore less around the customer, and more around the finance procurement of HR risk and that type of thing.

The solution is scalable.

It's better than a similar solution offered by Amazon.

Technical support is okay in terms of the help they provide.

The product could be easier to use. For example, I find Snowflake more user-friendly. They should offer simpler operations.

The initial setup could be faster.

Technical support can get expensive as they charge per use. The product could do more around crowdsourcing to help users find answers or answer questions without having to resort to contacting technical support directly.

The product could be more feature-rich.

They should look at some of what AWS and Snowflake are doing. They've both got a feature stack and also push the integration of things. This is a little bit lacking with Azure.

The analytics side of the solution is pretty weak right now. 

I have used this solution for a while. I used it before it was even called Synapse - when it was just Azure SQL Data Warehouse. It's been three or four years.

Technical support is okay, however, it can get expensive. The more you use it, the more it costs. I do not like this particular model.

I also have experience using Snowflake. I use both products. I originally started using Snowflake in 2017.

The solution takes longer to set up than I would like. There are a lot of questions that need to be addressed with the customer, and this can draw out the process. 

You do have to pay more the more you use technical support.

Clients have had a free year and then a monthly cost that they need to pay for licensing and access.

I don't use it personally, as I'm a consultant. However, if I implement it for clients, I do use it.

I'd rate the solution at a seven out of ten. There's still some work that needs to be done on the product that can make it a lot better.

I would recommend the solution to a company if they were a Microsoft shop. In that case, it's the obvious thing for them to do and it's a good choice.

We are using this product data warehousing and analytics.

Pretty much all of the features are valuable, including storage and compute. All of the features are essential to what you do.

It's very reliable and cost-effective. It meets the needs of our clients.

It's user-friendly and has a good dashboard.

I am very sure that there are areas in need of improvement, but I can't recall what they are off the top of my head.

I have been using Microsoft Azure Synapse Analytics for a couple of years.

It's a stable product and we're satisfied and comfortable with it. Our clients and partners have found it to be very stable and scalable, as well.

Synapse Analytics is a scalable product.

Generally, support from Microsoft is always good. I can't recall the specific instance where we had to contact them, but it was good.

The initial setup is very straightforward. It only took five minutes to set up.

We deployed the solution in-house, and we also deploy it for our clients.

This is a cost-effective product.

I highly recommend this solution to others, and we recommend it to our clients.

Overall, it's exceptionally good.

I would rate this solution a nine out of ten.

We are using this solution for data warehousing and analytics.

Pretty much all of the features are valuable. The storage, compute, all essentially differentiate what you do.

They are very reliable and cost-effective. It meets the client's requirements.

It's user-friendly, has a good dashboard, and my team is happy with everything.

I am pretty sure that there are areas that need improvement but I just can think of them off the top of my head.

I have been using this solution for a while, I would say approximately two years.

We are using the latest version. It's always updated on the cloud.

We found it to be a very stable product.

It's a scalable solution and we find it to be easy to scale.

Generally, support from Microsoft is always good.

I can't recall where we had an issue that we had to contact technical support, but the support is very good.

The initial setup was very straightforward. It took only five minutes to set up.

We completed the deployment in-house, we did it ourselves. We also implement it for our clients. 

It's pretty straightforward, we do not need help with this.

Because it's a cloud solution, the cost is a different convention and the licensing costs are not the same.

I would highly recommend this solution to others who are interested in using it. We recommend it to our clients.

It's exceptionally good, and our clients and other partners find it both stable and scalable.

I would rate this solution a nine out of ten.

We primarily use the solution for analytic purposes. It allows us to store data for years and then go back and look up information to learn things like how users function, react, follow trends, etc. It allows us to follow past and recent demands for products as well so that we might be able to find the reasoning behind the action or trend.

With this solution, I can better understand buying patterns and user habits. I can tell in real-time a customer's review and purchase based on that. I can tell that from the cloud data of Azure Data Warehouse.

The solution offers a variety of different features.

There are two layers, so data storage and computation are separated.

For the customer, there aren't any storage limitations, so you are able to explore and size of data including megabytes and terabytes. Once you have stored the data you can analyze the data that if you have in order to write some complex queries. After that, the solution makes it possible to visualize that data itself. 

The computation makes it so that you can run scenarios without an impact on your storage location.

If you compare the product to other solutions, you'll notice it takes less time for less cost. All other vendors have different architecture so their pricing is a little bit different and they are charging pricing per second. Microsoft charges per unit, or DTU, Data Transfer Unit. It will charge based on how much data you are consuming and how much data you are doing transactions with. It is monthly not daily.

Microsoft provides both the platform and the data center, so you don't have to look for a cloud vendor. It saves you from having to deal with two vendors for the same task.

The solution is consistently improving a lot of things, including security features. There are eight or nine different security features there. You can even encrypt your data.

There are features coming int he next few quarters that will be helpful. Soon, Power BI will be directly integrated into Azure. We need to have some Spark tools also available so we can directly select customers and don't need to install everything.

There will be features added that relate to application development. There's hopefully going to be more flexibility with the XML. Right now, for example, Data Warehouse is not able to give XML files and your file put is not correct. The feature will hopefully allow us to read XML.

The performance needs to improve in future releases.

We're hoping that Microsoft will add integration with the Amazon AWS platform.

I've been using the solution for four years.

The solution is quite stable. We haven't had any issues with it in terms of experiencing bugs and glitches.

Microsoft has different sizing options that comes at different price points. It's easy to scale up or down. If you find, after a few years, your needs are growing, you can increase your BTU size the maximum may be about 13,000. 

With this solution, we can actually automate scalability, which makes it very easy to scale. If you require more data, it will automatically increase your DP size and process the data. We have the flexibility we need so that, whenever a node gets filled, we never have to worry. Another node will pick up and process the data.

With Snowflake, in comparison, if your system is not running, then Snowflake will go into an auto suspend mode. You can sync the time, and, within the 10 minutes, if the system is not running, it will go to an automatic suspended mode. There is no charge in this mode. In that case, we have to manually find what we need to virtualize the function. 

Azure has other mechanisms in place. We can write Azure functions and we can schedule the Azure functions in order to automate them. We can do similar kinds of functionality, however, we have some additional coding we require for Snowflake. 

We've had no need for support. Everything is automated and pretty much taken care of. We need to configure everything and the Azure portal will get us to the dashboard. In that dashboard, you'll get all of your current information including information on how the system and the cluster is running. 

The backend is very nice. There are a lot of additional features that help you manage the product. As users, you will get the visualization dashboard. It is very easy to see, which nodes are running and how much data is processed. We can see everything on the portal, and that makes everything very easy to handle.

I've had prior data warehouse experience with traditional systems like the Oracle, Superserver, or Teradata.

Snowflake is the only provider with no cloud platform. You have to buy a platform in order to adopt it. Microsoft Azure, AWS, and Google Cloud all have their own dedicated platform. They have their own dedicated data centers.

The initial setup is not difficult. If you have access to the subscription you can start using Azure Data Warehouse and being to create the services. There are security features also. You can give someone full access, and you can set access for others too. Developers will need access so that they can develop it out. It's a pretty straightforward process.

In comparison, I find that AWS is much more costly.

We're a Microsoft partner.

I have experience with the Microsoft Cloud Data Warehouse specifically within the Unix cloud environment.

We're using the latest version of the solution.

It's important for organizations considering the solution to consider their business requirements and expectations. They need to be clear about what type of cloud solution they are looking for. We help our clients do this and interview them to find out what their needs are so that the best platform can be chosen for them. It may be Azure. It may be Snowflake. It depends on the company's needs.

At the end of the day, the customer will always want the best possible pricing. They'll typically ask how they can save money but have high throughput or more input with less price. If that's the case, Microsoft may be the perfect solution.

I'd rate the solution eight out of ten.