Microsoft Azure Synapse Analytics Reviews

We use this solution to analyze big data. We collect data from different sources then we analyze that data with Synapse.

One of the most valuable features of this solution is its ability to integrate well with other services offered by Azure. 

Scaling this solution up and down is not quick and easy. This could be improved. The pricing of this solution could also be improved. 

We have used this solution for two years. 

This is a stable solution but we have experienced some unavailability issues. These were not critical issues. 

This solution is quite easy to scale depending on your workload. Overall it is a little bit expensive. You have to know exactly what you are doing as you scale up, otherwise the cost goes up quite quickly.

This solution is relatively easy to set up and use. The time it takes to setup depends on the complexity of your environment. If you want to complete a basic setup, it only involves a few clicks. In a production environment for a complex setup, it's it takes some time to complete the configuration. 

We completed the setup in-house. 

Synapse is useful for very big loads of data. 

I would rate this solution 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.

Our primary use case is to process data and provide it to our clients.

The most valuable feature is data processing.

The architecture, including compute and storage, is good.

The performance and data consistency need to be improved.

Support for PSQL functionality should be included, as it is something that is missing right now.

I have used Microsoft Azure Synapse Analytics over the past 12 months.

The stability has been getting much better. Two years back, there were issues with it.

Scalability is something that is improving.

The initial setup is straightforward.

We deployed it ourselves.

I would rate this solution an eight out of ten.

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.

I used it for a project thesis for my school project.

I like its performance. It is well-structured and offers a lot of practical options. Because of the visual design of the product, it is very easy to find icons and other visual elements.

It took me more than two weeks to understand how it works. I had an intensive training session to understand it, which was enough. It was not hard to understand how it works.

I am a researcher. For people to be able to research a solution, there should be at least a free trial. Just advertising a product or saying that this product is better doesn't work. I would strongly recommend providing a lot of free trials and trainings. This will also help Microsoft in having more users or customers. Oracle provides some free trials. You can just go for a free trial and use your database online, which is very good.

I have been using Microsoft Azure SQL Data Warehouse for three months.

It was ready when I started to use it. I just had to do my project.

Its price could be better. It was a school project, and I got it for free. If I try to pay through my company, it is a little bit more expensive as compared to Oracle.

I am also using an Oracle solution. Oracle has been in the database field for a longer time, and it provides you more familiar terms that the users understand. When users start to work with an Oracle solution, they are more familiar with the terms. They just need basic training to understand it, but with Microsoft Azure SQL Data Warehouse, they have to be a little bit more experienced. In order to understand this product better, they have to know how other Microsoft products work.

It is a powerful asset if we can use it and learn more about it. If Microsoft can give some free training sessions for students and amateurs, it would be helpful in knowing more about this product from inside. 

I would rate Microsoft Azure SQL Data Warehouse an 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.

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.

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.