Microsoft Azure Synapse Analytics Reviews

We encountered data processing and transformation issues while working with Apache Spark languages for the product. This particular area needs improvement. 

The product's error messaging could be friendlier. Additionally, the monitoring process using Azure ML SDK and Python should depend on the data for the results. Along with the documentation, they should provide essential training for proficiency in CLI. It will help in better monitoring.

We have been using Microsoft Azure Synapse Analytics for two years.

The product is expensive.

I rate Microsoft Azure Synapse Analytics a seven out of ten. It is a fantastic product; we are satisfied with its features and performance. However, there could be more information available about the platform.

Microsoft Azure Synapse Analytics can be used for analytics and brings together data integration.

The integrated workspace in Microsoft Azure Synapse Analytics where everything comes together, such as Power BI and Data Factory, is very good. Additionally, the ability to do dedicated SQL pooling is a benefit.

Microsoft Azure Synapse Analytics can improve querying. I have recently used Microsoft Azure Synapse Analytics to connect to the Delta Lake file and I have noticed some issues. It has not been able to read the latest version of the Delta Lake file and I have found this to be a disadvantage that they can improve.

In the future, Microsoft Azure Synapse Analytics could improve the performance, there are other solutions that are better, such as Databricks.

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

On the client's side, there are approximately 100 users of this solution.

Have I reached out to Microsoft technical support in a few instances and we have raised a couple of tickets. A couple of agents have been knowledgeable, but not everyone is.

The installation of Microsoft Azure Synapse Analytics was easy. It was not complicated, but I have not explored the VNet Injection or the Private Endpoint though I think it would be straightforward.

We are on a monthly payment plan for the use of Microsoft Azure Synapse Analytics.

We have done some comparisons between Microsoft Azure Synapse Analytics in many areas, such as processing queries, versus Databricks. We have found Databricks to be better in performance.

I would recommend this solution to others. The client that I am working for, their preferred database is Synapse.

I rate Microsoft Azure Synapse Analytics a seven 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 primary use case is for the fast retrieval of data, which is in the data warehouse on-premises and migrating it to the cloud.

The most valuable feature is performance gains.

The scalable nature of the data warehouse on the cloud, and the flexibility that you get, and important features.

I would like to see them provide the ingestion of images.

I have been using this solution for more than three years.

It's a production that keeps changing and we are always using the latest version.

This solution is quite stable. It's in the cloud and we have not faced any issues.

This solution is scalable.

Technical support was not needed because of all of the documentation available.

The initial setup is straightforward.

All of the prices are available online.

My advice would be to move your on-premises data warehouse to the cloud. Move it there faster, as you can get more performance gains and it's also a robust infrastructure.

I would rate this solution an eight out of ten.

My primary use case of this solution is as a data warehouse.

The most valuable feature of this solution is its filling of the gap between big data and classic data warehouses. As a solution, Synapse allows you to do almost everything. 

An area for improvement would be advanced analytics. The product also comes with a pretty steep learning curve, which could be improved. In the next release, I would like an improvement in internal security, which currently doesn't work at all.

I've been working with this solution for about two years.

Initially, the product had some issues with stability, but these seem to have improved with time.

Scalability is one of the main advantages of this solution.

Tech support is ok but more difficult tickets can take a long time to solve.

The setup was complicated because the data must be organized differently from the classic SQL database. There are also different techniques for query performance and managing caches, which makes the process more complicated.

Synapse is a very costly solution, and other products are available for less.

One of the main pros of Synapse is the ability to work with the classic data warehouse and the big data. Another is the ability to use the Spark engine for preparation. Before implementing this product, consider whether it will provide the results you want. If you are already using Microsoft products, Synapse is easy to integrate. I would score this solution as eight out of ten.

We are service providers for our clients.

My clients primarily come from the enterprise segments, such as manufacturing, retail, pharmaceutical, and industrial. Most are supported by a large ERP solution like SAP.

Some of our clients are mid-size and others are large enterprise companies, some of the largest in India.

Some of the most valuable features would include Familiarity for practitioners like us and also familiarity for the businesses. SQL servers have been around for several decades and there are many who are familiar with SQL servers, such as SSIS and SSRS.

They are available on the Cloud, and the platform is very intuitive.

It integrates very well with Power BI, which I would rate as the best self-service BI. All the data warehouses eventually end up in BI or Machine Learning. 

Instant Apps that are more integrated rather than desperate components. You need to get different components engineered together, and with SQL everything comes together.

Instant Apps is a combination of your Spark compute platform, your SQL data platform, and your object storage. It's a platform that is integrated all together. 

It's easy to work with a faster turnaround time on my project deliveries.

Also, the integration with Power BI self-service is much, much easier and familiar.

You can have several SQL servers, they have large .NET artifacts that combine various components together very well.

What I would like to see more and more of in Azure is its support for IoT and streaming media information.

It needs strong support for social media, internet data, and native support for NoSQL. At this time, it works very well with the structured data

Stability could be improved.

Technical support is very good, but could also be improved.

My company has been working with SQL Data Warehouse for approximately three and a half years, and I have been working with this solution for one and a half years.

In the last six months, there have been a couple of incidents. We have experienced som outages. One of our clients was in a blackout for a couple of hours. Most of the clients in India have experienced outages also. They were restored, so I refer to them as glitches.

In principle, it's a limitless, serverless solution. As long as your region has resources, you can go on expanding. We have not had any problems with scalability.

Microsoft Azure comes integrated with Databricks as a Machine Learning platform, it's highly scalable.

Technicals support engages very well. We have account managers in place who correspond with technical support. 

The technical support team has a good relationship with our Account managers.

If we have an issue, we advise our account managers and present the ticket, they contact Technical support and resolve the issue very quickly.

If however, I have to contact Microsoft technical support at the backend, they are not very knowledgable. They have a good understanding of their products, but most of the time they struggle with that.

The initial setup is easy. The learning curve for Microsoft platforms is very low. An engineer with very little experience would be able to complete the initial set up with ease.

It has a quick turnaround time. We have completed the deployment in weeks.

For a BI project scheduled for six months, within the first month we would have completed the data warehouse installation, and the remaining five months would be for building the content, the data pipelines from the sources, the semantic models, and the BI.

When you compare it with other solutions that take months to get your sandbox running or to get your development running.

They are cost aggressive, and it integrates well with other Microsoft tools. For example Downstream and Teams.

We use the serverless platform as a service, that is only available with Microsoft Azure.

You don't have to hire experienced engineers to set up and implement the solution, saving you money, because it is very intuitive and the turnaround time is very low compared to other data warehouses. I would put it in par with solutions such as Redshift and BigQuery.

It's very modern, easy, and intuitive.

Most of the services are cheaper than other vendors.

This definitely falls into one of the top three or top four solutions and platforms in the world. I would definitely recommend it. If someone should ask me if they should use Microsoft Azure SQL Data Warehouse, I would say yes.

Personally, I don't see one great solution that exceeds in all aspects. Every solution has some good points and I think they have very beautifully found their strength and they're building upon it.

When asked what would best suit me, I would assess their environment requirements and could easily recommend any of the top four, being AWS Redshift, Google, Big Query, and Microsoft Azure.

I would rate this solution an eight out of ten.

We use Microsoft Azure Synapse Analytics for analytics.

If I'm looking for something good in the cloud, I would want it to have better standard connectors.

I have been working with Microsoft Azure Synapse Analytics for two years.

We are up-to-date with the latest version.

I have been working with Microsoft, and they have been very helpful.

It's the only product I've used. I'm not sure what's wrong or what could be done better.

I don't know about the price, but I'm trying to get a sense of the cost. I believe having a good comparison would help.

This is the only product I've ever used, as such, it's extremely difficult for me to say anything about it. For example, if I needed a bike, and now I have one and am riding it. If you ask me some questions, it's difficult for me to say, "Oh, I really missed this one on my first bike," unless I've ridden a few bikes. I'm not sure why everything is the way it is. I had a need, and I'm only using it now.

I'm trying to understand what else is out there and how it compares to what is out there.

I am able to use it. If I am able to use it, I would rate Microsoft Azure Synapse Analytics an eight 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.