Microsoft Azure Synapse Analytics Reviews

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.

We have been using Microsoft Azure Synapse Analytics as our proof of concept in our data warehouse. As for the analytics part, we have not used it because we do not have any SQL pools. We think it is a bit more than we need because our data is not implicated and the data sets are not very large. It is mostly been used for the detection functions and to link up the sources.

The most valuable features of Microsoft Azure Synapse Analytics are how easy and quick it is to set up the linked services.

When I was trying to link services to an SFTP site it was not able to do all the possible encryption that I needed. They can improve by adding more encryption options.

I have been using Microsoft Azure Synapse Analytics for approximately six months.

The solution is incredibly stable. 

The only thing that has not been stable are the pipelines that we are using runtime integration on our on-premise server and something is slowing everything down, it might be a particular ODBC connection. I am not sure if that is due to the Microsoft platform or it might be some other setup or settings that are causing the issue. However, other than this issue, the solution has been brilliant. It has been very reliable and stable.

The solution is scalable. I have changed the interface and most of the operations can be done from the interface by clicking a few buttons, it is quick and easy.

At the moment we only have approximately four people using the solution because it is a data warehouse creation in the back end. We have not reached the stage where our staff will use the product but we are building a single customer view using the platform and we will be increase usage in the future.

The initial installation is straightforward. There is a lot of documentation provided by Microsoft making it very easy to get started.

Having used the serverless option for the database has saved us a lot of money.

We chose the serverless option for the database which has saved us a lot of money. Our costs are approximately £600 per month.

It is beneficial to always plan exactly what the main aims are and then choose the solution that can do it. If you want something quick, I set something up without any knowledge with Azure in three weeks. Once you get going, everything will be at your fingertips.

I rate Microsoft Azure Synapse Analytics an eight out of ten.

We mostly provide cloud data warehousing platforms for major banks in Canada. What we're trying to do is to create a standard platform environment that is compliant with the regulatory requirements imposed by the government and financial overseeing institutions for the banks. We help them to onboard the lines of business to these platforms and migrate the existing workloads to the cloud platforms.

The ability to scale out services on-demand and scale them down when they are not required is most valuable. You are in control of your expenditures, and you are also in control of the horsepower that you need. That's a major advantage.

The major challenge that we're seeing with Azure Synapse is around security concerns. The way it is working right now, it has Managed VNet by Microsoft option, similar to the implementation of Azure Databricks, which may pose a concern for financial institutions. For managed environments, the banks have very strict policies around data being onboarded to those environments. For some confidential applications, the banks have the policy to encrypt it with their own key, so it is sort of like Bring Your Own Key, but it is not possible to manage the resources with Microsoft or Databricks, which is probably the major challenge with Azure Synapse.

There should be more compatibility with SQL Server. It should be easier to migrate solutions between different environments because right now, it is not really competitive. It is not like you can go and install SQL Database in some other environment. You will have to go through some migration projects, which probably is one of the major showstoppers for any bank. When they consider Synapse, they not only consider the investment in the actual service; they also consider the cost of the migration process.

When you scale out or scale down your system, it becomes unavailable for a few minutes. Because it is a data warehouse environment, it is not such a huge deal, but it would be great if they can improve it so that the platform is available during the change of configuration.

I have been using this solution for two years.

Generally, it is stable. We all heard about the Active Directory issue earlier this week, but it was not related to Synapse. It was related to the Azure platform.

Its scalability is good. The only thing is that when you scale out or scale down your system, it becomes unavailable for a few minutes. Because it is a data warehouse environment, it is not such a huge deal, but it would be great if they can improve it so that the platform is available during the change of configuration.

We're working with banks, and they have great support because Microsoft has multiple representatives closely monitoring each account. Whenever there is an issue, they're being proactive. They're making a lot of money out of it. Most of the banks, on average, spend between 30 to 50 million a year on Azure. They're pretty large accounts, and Microsoft has dedicated people supporting everything related to Azure.

We deliver platforms to different banks. Some of the banks go with Synapse, and some of the banks go with Snowflake. Overall, these are two major alternatives available right now.

There are multiple differences in terms of the support of different workloads. When one of the banks made a decision to go with Snowflake, the major reason for it was the support for the multi-cloud environment. The major pro of Synapse is the service, and the major con is that when you decide to move out of Synapse, you would have to rewrite the entire thing, whereas, with Snowflake, it would be just simple migration to different cloud providers.

It is relatively straightforward as long as you understand what you're doing.

You don't really need to maintain it. That's the entire point of the cloud. You pay for it to be maintained. 

We do deal with monitoring and other similar things, but most of the activities are automated. Overall, it doesn't require a lot of labor around it. We're delivering the platform as infrastructure as a cloud, so everything is going through the pipeline. 

It goes by the usage, and there are some limits. Synapse goes by particular pricing, and it is expensive. Both Azure Synapse Analytics and Snowflake are pretty expensive. They don't have standard pricing. They deal with each customer differently.

For working with Synapse, you need to have an understanding and knowledge of the product to take full advantage of it. Synapse has a lot of features in terms of scalability, such as resource management, distribution, and partitioning. There are a lot of things that you need to consider when you go for it. It is not a simple database that you put in there, and it is running itself. 

I would rate Microsoft Azure Synapse Analytics a seven out of ten.

The stability of the solution is pretty good. It's one of the solution's most valuable aspects.

The useability, the user interface, is very user-friendly.

The solution needs to improve its integration capabilities. It needs better integration with SQL and other databases. 

The initial setup is complex.

The product needs to implement more machine learning and artificial intelligence.

If they had more IoT and streaming capabilities, it would be helpful.

The product could use more analytics tools.

It would be helpful if we could use the product with less coding.

Normally, the solution is quite stable. The performance is pretty good. We don't regularly deal with issues. It doesn't crash or freeze, and I can't recall any bugs or glitches.

The solution can scale, however, it is expensive to do so. If a company wants to expand, it will have to pay. Therefore, they should be prepared for that.

Currently, we have about 200 people using the solution.

I don't have a direct relationship with the technical support when I have a problem. They aren't very good. We aren't satisfied with the level of support we get.

The initial setup is not too straightforward. It's rather complex. We had to get some outside assistance.

The deployment process took us about three months. It was a while.

We hired a consultant to help us with the implementation of the solution.

I don't have exact KPIs on this, however, I need to improve the sizing in order to improve our ROI.

The is a monthly licensing fee. I can't speak to how much we actually pay, however.

There is just one fee. You do not have any more costs over and above that.

While we didn't previously evaluate other options, in 2021, we need to explore the other solutions. I intend to look at the Amazon and Google Cloud platforms as other options.

We are both partners and end-users of the product. We use it internally and we use it with some of our clients.

I would caution new users that there is one parameter they need to watch carefully. That is the size of the data warehouse. If you don't configure this parameter to be as small as possible, your costs will be quite high.

I would rate the solution at a seven out of ten. It's a stable platform, however, the price model is questionable in comparison to Amazon and Google and the contact with support is a problem for me. 

We're using this solution for general data integration. We are customers of Microsoft Azure Synapse Analytics and I'm a data specialist. 

I like the solution because it's very interactive and provides flexibility. It's mostly a drag-and-drop procedure. It also provides a lot of design choices.

We've only recently starting testing this solution and it would be helpful if the documentation was better or if the company could provide some kind of presentation to help us understand how the tool works and what's required to optimize use of the solution.

We've been testing this solution for a couple of months. 

I haven't seen any issues with stability or scalability. We have an infrastructure team of around 40 people. 

The company chose Synapse Analytics because the price is fair and I understand it's cheaper than Snowflake.

We're also hoping to evaluate Snowflake which is an advanced version of both Azure and AWS together. Snowflake has advantages, especially in terms of scalability which can also be implemented separately at the storage level and also at the computer level. 

I think Synapse Analytics is definitely better than the traditional high solution data solutions that we've previously used. It's definitely worth considering and I haven't come across anything negative, only advantages for now.

I would rate this solution an eight out of 10. 

I work with Azure Synapse Analytics, Power BI, and Premier as a data administrator, using Microsoft's Azure cloud infrastructure. Azure Synapse Analytics is the principal data administration tool for our internal data warehousing, but we also use it for our customers' data warehousing and analytics in different industries. For data entry, we use Microsoft Power BI.

Internally, we have about 200 personnel currently using Azure Synapse Analytics, including data specialists, service managers, and other tech workers. It is not extensively used by all, however, because we don't often use it for very large amounts of data, except where financial data is involved.

Azure Synapse Analytics helps us move quickly when it comes to finding data warehousing and analytics solutions for our customers.

We use Azure Synapse Analytics in many different areas and industries, so I like that you can administrate and create pipelines for different sources of data and later integrate and deploy it to other internal areas, such as separate dashboards for financials, and so on.

It is our main data warehouse solution, and the one big feature I appreciate is all its various analytics functions. The built-in analytics also makes it an attractive option for our customers, where we act as a service provider for them.

Unfortunately, we have had some issues with the dashboard reporting. Sometimes, the data for specific periods would just appear blank on the dashboard. To investigate this, we worked with a Microsoft incident agent and it turned out to be a result of bugs in the platform when dealing with specific types of queries in Azure Data Factory.

We have also encountered some bugs regarding incompatibility with certain versions of Power BI. In the past, Power BI used to integrate nicely with Azure Analysis Services which we used as a bridge between Power BI and Synapse Analytics, but they have since discontinued the features that worked so well for us.

Apart from fixing bugs, I would also suggest:

• The learning features and documentation could be improved and expanded on.
• The calculation and forecasting tools could be made more clear and easy to use.
• The price could be lower.
  

I have been using Microsoft Azure Synapse Analytics for one and a half years now. 

When it comes to stability, Synapse Analytics has suited us well, because it's very fast, i.e. it has good performance. Compared with the on-premises SQL solutions we've used, the results are two times as fast.

There are some bugs that we've encountered with the platform but we are able to work with Microsoft directly to resolve them as they come up.

I'm very impressed by the scalability of Synapse Analytics, even though we don't use it for huge amounts of data most of the time. Combined with the availability of machine learning and other features in the cloud, it has a lot of potential, especially in the area of finance.

In our context as service provider, normally we have direct contact with the server. But in some cases, we have needed extra support which was very slow.

For technical issues, I often solve them myself using Microsoft's learning resources and support forum.

For me, it's the fifth data warehouse I've used. Other specialists here also use other warehousing solutions based on SQL but for the moment Synapse Analytics is the best overall solution for us primarily because of its ease of implementation and flexibility.

Because I was not familiar with the solution beforehand, the initial setup was quite complex. I had to learn a lot in the beginning stages. I'm grateful that there is help to be found in the Microsoft forums, however, and there are also options to study for certifications that help you learn how to use the platform properly.

It took approximately six months to implement Azure Synapse Analytics in our organization. We set up the data warehouse features for internal use across 200 users, including data specialists, service managers, and tech workers who manage our own data and also our customers' data.

For the main deployment, we used a third-party support provider in Chile who helped us greatly.

We normally pay between $300 and $500 per month, which is quite expensive for how much we actually use it, performance- and usage-wise. They have a cheap version and an expensive version, and our usage usually falls in the middle ground, which makes it not as cost-effective as it could be.

We evaluated on-premises SQL solutions for data warehousing, but our main motivation for using Synapse Analytics instead is that it lets us come up with and manage customer solutions very quickly. Some specialists are still using other SQL solutions as well, however.

There is always room for improvement with Azure Synapse Analytics, but for our use cases it works very well. 

I would rate Microsoft Azure Synapse Analytics a seven out of ten.

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.

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.

We are a consulting agency and the Microsoft Azure SQL Data Warehouse is what we use for general BI reporting and visualization.

The most valuable feature is that you can suspend the computer if it is not being used, which means that you don't incur costs. This is better than Amazon Redshift, where the costs continue to accumulate.

The documentation is good.

This solution needs to have query caching so that if the same query is run and the results are available, it will return the data from the cache without having to re-run the query. As it is now, you still have to use compute for each query.

We would like to have integration with the data lake so that we can avoid retrieving the data from it, just to store it in Synapse before we can use it. From an architectural perspective, avoiding this replication would be good.

I have been using this product for about one year.

Stability-wise, this is a good product.

This solution is scalable, although I don't expect that we will expand it beyond the current usage.

We have not had to reach out to support. Rather, we have relied on the documentation. It is easy to solve your own problems.

I also have experience with using Snowflake. It has the option to cache query results, which is helpful. 

Amazon Redshift is another similar solution that I have experience with, although it lacks the ability to suspend the computer to save costs when it is not being used.

The initial setup is straightforward.

The Azure SQL Data Warehouse has been renamed to Synapse, although not all of the features have been implemented yet. I am looking forward to a more mature version of this product in the future.

I would rate this solution a seven out of ten.

I use this solution for client service forecasting. I work with medium-sized businesses, and I work through a virtual machine.

Synapse Analytics gives you information and reports in real-time.

The platform is not flexible, and the graphical user interface needs to be improved because the interface makes it hard for the end user to use it.

Also, artificial intelligence could be used to improve analytics.

I have used the solution for over a year.

The solution gets support, which helps its stability.

Microsoft's technical support is good when I compare it with other platforms like Oracle. You can also fix many problems yourself because Microsoft has an active community.

The initial setup is easy if you have information about analytics visualization and data warehousing. It's easy enough to install and configure the blade. It takes one day to deploy the solution.

The solution is expensive at the enterprise level. The license is monthly.

Microsoft is a good platform because it's compatible with data warehouse, power analytics, and visualization, and you now have excellent machine learning and AI tools like GPT-4. The platform is also very stable, and support can be migrated for software.

I rate Microsoft Azure Synapse Analytics a ten out of ten.