Microsoft Azure Synapse Analytics Reviews

We primarily use it for data transformation purposes, as well as for storing data. Additionally, we use it to connect to Power BI for reporting purposes.

We find the serverless tool the most valuable feature we're currently exploring.

One area for improvement could be better integration with Power BI, as well as data integration with BW. If we could find a connector or process to export data from BW into Azure, that would be helpful.

I have been using the solution for two years. I'm currently using the latest version.

We are just building the solutions, it's like the solution is very much immature. We are setting up those things. So as of now, it's not stable.

I would rate the stability a five out of ten.

The solution is very scalable. We are a medium-sized enterprise, and there are almost 4000 end-users.

We are getting good support from Microsoft.

Positive

I would rate the initial setup a seven out of ten. The initial setup was good.

We are currently working with around five or six people for the deployment. Moreover, about four or five people are needed for maintenance.

I would rate it a four out of ten, where ten is for most expensive.

I would recommend using Microsoft Azure Synapse Analytics because it includes everything from integration to transformation and storage.

Overall, I would rate the solution an eight out of ten.

We are using the solution to process a large volume of data. We are taking advantage of the MTPP functionality feature in Azure Synapse Analytics and connecting our reporting dashboard to its dedicated support tool. These are the two primary use cases we are currently leveraging.

Generally, people have differing opinions on whether to use Azure Synapse Analytics or Azure Databricks. These are two different services from Microsoft, and customers often struggle to decide which is best suited for their particular use case. Both services have some features in common and some that are exclusive to one or the other. It is a trade-off and ultimately depends on the use case that best serves the organization. Incorporating the features of both Azure Synapse Analytics and Azure Databricks would meet most use-case scenarios.

The cost of the solution has room for improvement.

Azure Database has a very good Unity Catalog functionality, which is a form of governance within Azure Databricks. Similarly, for Azure Analytics, having a governance product would be great. However, Microsoft already has Azure Purview, which is meant for governance, but it would be nice to have that functionality built into Azure Synapse Analytics.

I have been using the solution for seven years.

The solution is stable.

The solution is scalable. Generally, the development team of around 20 people uses Azure Synapse Analytics directly. Otherwise, a Power BI dashboard accesses the Azure Synapse Analytics database, and the end user uses the Power BI reports, not being directly exposed to Azure Synapse Analytics. Only the development and software development teams use Azure Synapse Analytics.

We are always in contact with the Microsoft team. We keep up to date with our Microsoft reference architecture and other related matters through our communication with the Microsoft team members.

Positive

The initial setup is straightforward. We can have the dedicated SQL up and running within 15 minutes.

The implementation is completed in-house.

The cost of the solution depends on the type of license we choose, such as pay-as-you-go, one-year reserve, or three-year reserve. The cost varies depending on the options selected and can be very expensive.

I give the solution a nine out of ten.

Depending on the organization's use case, there are multiple options available. For example, if the workload is medium, there are different products available such as Azure SQL Database for small or medium-sized data, and Azure Synapse Analytics for extremely large volumes of data. Ultimately, it is the use case of the organization that will determine the need for these particular services.

We use Microsoft Azure Synapse Analytics in several scenarios, we require access to high-performance computing capabilities. This is where the solution proves to be a valuable asset as it offers a node-based solution for computing needs. In numerous cases, we have to undertake intricate data processing operations using the Python programming language, and that's where the solution comes in as an advantageous tool. These are my primary use cases for Synapse. Whenever we need to handle complex data engineering tasks or require significant computing power to accelerate processes, the solution provides the necessary functionality.

The most valuable features of Microsoft Azure Synapse Analytics are its serverless flexibility and complete power have allowed me to explore various different use cases. While I am not an expert in the product, my experience in programming in Databricks has shown me that Microsoft's investments in Synapse could potentially lead to it becoming a complete replacement for Databricks in the future.

In the future, Microsoft Azure Synapse Analytics has the potential to enhance its capabilities by expanding its connectors, specifically with regard to Oracle solutions, such as operating systems. This would involve a comprehensive approach to adding more connectors for both data input and consumption purposes. By doing so, Microsoft Azure Synapse Analytics would be better equipped to meet the diverse needs of its users and achieve greater efficiency in its performance. The provision of more connectors is definitely a crucial area that needs improvement.

I have been using Microsoft Azure Synapse Analytics for approximately two years.

I have not had any stability or performance issues.

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

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

Before adopting Microsoft Azure Synapse Analytics as my solution of choice, I delved into several other options. One of these alternatives is Snowflake, which I must admit, is a superior choice compared to Microsoft Azure Synapse Analytics. However, it is essential to take into consideration the consumption side when evaluating these solutions. If the consumption side involves Oracle solutions, then an autonomous warehouse would perform better than this solution. On the other hand, if the consumption is within the Azure platform, this solution presents itself as a commendable solution.

I rate the initial setup of Microsoft Azure Synapse Analytics a seven out of ten. 

We have a team that does the implementation.

The solution is worth the cost for our use case, it is worth the money. Despite the fact that there may be room for further optimization of its complete power, the cost-to-flexibility ratio of Synapse is quite favorable. I do not have any specific metrics to support my statement, however, I can assure you that Synapse is not inadequate or disappointing in any manner.

Microsoft Azure Synapse Analytics can be costly, however, a cost-effective approach would be to purchase it in advance through reservation for either one or three years. This will significantly reduce the overall expenses incurred.

After thoroughly evaluating various data management platforms, such as Oracle Autonomous Warehouse, Databricks, Microsoft Azure Synapse Analytics, and Snowflake over the course of the past year and a half, I have come to the conclusion that flexibility is crucial when it comes to choosing the right tool. Microsoft Azure Synapse Analytics stands out as a strong contender, as it offers a unique combination of both traditional warehouse capabilities and modern technological advancements. This combination of features makes Microsoft Azure Synapse Analytics a valuable option and is impressive.

My advice to others is to start with a reserved instance in order to test the waters and make sure it fits their needs before fully committing to using the solution. If, after experimentation, they decide to proceed with using Microsoft Azure Synapse Analytic over its competitors, then I would highly recommend going for a reserved instance to make the most cost-effective and efficient choice.

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

We use this solution to create data pipelines and to improve the self-service environment for end users. We use all the functions of Microsoft Azure Synapse Analytics.

I like the keynotes and their simplicity. Like other Microsoft products, Microsoft Azure Synapse Analytics is simple to understand and use.

The linked services can be improved. We can create dynamic linked services to access a lot of databases but only those of the same type. For example, I can use the same linked services to access 11 SQL databases. However, if I have 11 SQL databases and five Oracle databases, I need two dynamic linked services. I cannot do it with only one linked service.

The UI also needs to be improved. When I have used Azure Synapse for programming with PySpark, Scala, or .NET, for example, the UI has been unstable. If I open two notebooks for programming, one notebook will queue the session of the other.

I've been using this solution for two years.

I would rate the stability at eight out of ten because Microsoft Azure Synapse Analytics is unstable during programming. 

It is simple to scale the service. I'd rate it at ten out of ten for scalability. We had 45 engineers who used the solution.

Technical support staff are knowledgeable. Whenever we have needed support, they have had a good process and have always had the answers we needed. I'd give technical support a rating of ten out of ten.

Positive

The initial setup is simple, and I would rate it at ten out of ten.

The pricing is competitive, but only when you pay upfront. If you pay as you go, it's not as competitive. I'd give pricing a rating of seven out of ten.

On a scale from one to ten, I would rate Microsoft Azure Synapse Analytics at eight.

Our company uses the solution to provide data warehousing for customers. Most of our customers are mid-sized. 

Data can be stored any way you want in the data warehouse. Some customers want monolayers which we add with no issues.

The solution is very fast. 

It is reasonably easy to work within the solution. 

The solution should offer a serverless model like Snowflake so you don't have to manage the hyper or operating system layers. 

There are sometimes problems when connecting to the database. Most issues are solved by the community but Microsoft should act more quickly and participate in the process. They should not wait for the community to build a solution before embracing it. 

I have been using the solution for one year. 

The solution is stable. 

The solution is scalable to a customer's needs. A few customers have written scripts to scale how they want, but most customers use the out-of-the-box database.  

We have not used technical support in awhile but it was okay. 

I previously used Redshift and Snowflake. 

The setup was complex the first few times but we figured out the best way to do it for our mid-sized customers. The setup could be made easier. 

We implement the solution for our customers. 

The pricing is quite reasonable in comparison to other products. Of course, most companies would like the price to be even cheaper. 

Pricing depends on setup but generally ranges from 28,000 to 35,000 Euros per year for a mid-sized company. 

The solution's speed is better than Redshift and Snowflake. 

The solution does take some management. Mid-sized companies would like to work on data instead of hardware or operating system layers. 

It is important to have skilled Azure data engineers to manage the solution. 

I rate the solution an eight out of ten. The solution could improve its rating with an easier setup and more readily-available data connectors. 

What I found most valuable in Microsoft Azure Synapse Analytics is that it's native only for Azure, so you get better performance and there's no issue. To explain further, many different types of data come, in particular, structured and unstructured data. For audit purposes, there's also unstructured data, so the most important aspect is that with Microsoft Azure Synapse Analytics, you have the capability of using both technologies, meaning that you can use or mix structured and unstructured data which is important. This can also be done in Hadoop, and on other platforms, so you have everything in one place. You don't have to worry about how to manage both structured and unstructured data and where to store information. With Microsoft Azure Synapse Analytics, you can take care of everything, particularly in Azure.

The solution also provides you with many features apart from analytics, for example, storage which makes it better.

An area for improvement in Microsoft Azure Synapse Analytics is its user interface. You can use it for analytical purposes, but its platform should be a little bit more user-friendly.

Another small point for improvement in Microsoft Azure Synapse Analytics is its stability. It's good currently, but it could still be improved.

Microsoft is combining different tools and technologies into one solution, so in the future, I'm expecting to see even more improvement in Microsoft Azure Synapse Analytics.

An additional feature I'd like to see in the next version of Microsoft Azure Synapse Analytics is the drag-and-drop feature. If you're doing some integrations where you can write Scala or you have SPARK programming or SQL, or you're combining different programming, the process should be seamless, and you should be able to drag and drop in Microsoft Azure Synapse Analytics. When doing reporting in the solution, you should also be able to drag and drop. There should be connectors available and a drag-and-drop feature available in the user interface of Microsoft Azure Synapse Analytics, so you won't have to worry about how all processes would work together. You need to be able to drag and drop even from the backend, and having this feature will make the solution more user-friendly.

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

In my opinion, Microsoft Azure Synapse Analytics is stable. I would rate its stability seven out of ten.

As Microsoft Azure Synapse Analytics is on the cloud, it's scalable, and you won't have that many issues with scalability. If a solution is cloud-based, you won't have to worry about whether it's scalable or whether it supports other features, because you'd have all features in the cloud itself. You can scale up or scale down Microsoft Azure Synapse Analytics based on your requirement, so it all depends on what exactly you want. In the cloud, you won't have to schedule, wait, think, or plan. You can scale up or scale down automatically anytime.

I'm working on behalf of a vendor for the client, so my team is supporting not just users of Microsoft Azure Synapse Analytics within my company, but several other companies as well. Apart from supporting the infrastructure, data-related services, and other services, my team provides a combined type of effort for clients. My team is a big team with people working together from three different companies providing support for Microsoft Azure Synapse Analytics. My team provides technical support for the solution.

The initial setup for Microsoft Azure Synapse Analytics is straightforward because Azure makes it very easy. Any Azure solution is very user-friendly, but you just have to know how to use the solution, and that's it. Setting up Microsoft Azure Synapse Analytics is not that complex if you're knowledgeable.

I evaluated AWS, and if you compare Microsoft Azure Synapse Analytics with AWS, Azure excels more than AWS.

Amazon or AWS is established, and there's no doubt about it. It's also less costly in comparison with Microsoft Azure Synapse Analytics, but since I worked on both platforms, if you want everything where you have to pay a little bit, and you don't want to pay or invest in some other development areas, and you want certain features to be automatically available for you, apart from a lot of features to be available on any platform you use, then your choice should be Microsoft Azure Synapse Analytics. Amazon is a cloud provider only and relies mostly on open-source, but Azure has support from Microsoft which is an innovative company. Whatever product is offered by Azure, Microsoft support is there, and as a company, Microsoft is always very innovative, so a product such as Microsoft Azure Synapse Analytics is very useful and very user-friendly, which you won't get that much, at least for now, from Amazon. If Amazon wants to be comparable to Azure and have the same capabilities, then it will need to depend on some other companies. Whereas for Microsoft products, for example, GEO, it's native to the Microsoft platform, so it's comfortable to use, even though it's open-source and you're never sure of what type of problems could arise. Microsoft Azure Synapse Analytics, because it's an Azure or Microsoft solution, is more advantageous than AWS.

I'm an architect and I'm using Microsoft Azure Synapse Analytics, and apart from that solution, I'm also using other types such as Big Data, AIML, SPARK, and Scala. I'm also into other languages and reporting services.

I'm using the solution for my clients. Currently, I'm using it for clients, particularly Netherland-based banking institutions that have a cloud setup on Azure, but the deployment is now hybrid because of an ongoing migration. It's not completely migrated yet, so there's still something left in the data center. The clients have an ongoing migration from Hadoop and SD inSITE as well, which would be moved completely to Azure Cloud, so as of now, the deployment of Microsoft Azure Synapse Analytics is still hybrid.

My rating for Microsoft Azure Synapse Analytics is seven out of ten as it combines many different features that you can use, so it's a good solution, and in the future, I'm expecting it to be better and better.

Several teams use Microsoft Azure Synapse Analytics, but then in my team, around twenty-five people use it, but that's not the complete number of users. Many people use the solution, with different features being used.

I don't see a product comparable to Microsoft Azure Synapse Analytics that's available on the cloud. Currently, there's no comparison, so you can't say whether it's good or bad, though room for improvement in any product will always be there. Based on all the features of Microsoft Azure Synapse Analytics, my rating for it is seven out of ten, at the moment, there's no competition for the solution because I've not come across any other tool that's comparable to Microsoft Azure Synapse Analytics.

Microsoft Azure Synapse Analytics is used for analytics.

The most valuable features of Microsoft Azure Synapse Analytics are the interface and the agility of the on-cloud platform.

Microsoft Azure Synapse Analytics's overall integration within the Azure ecosystem could improve. The native Microsoft solution versus another solution, such as Databricks, there are areas where there could be some improvements.

I have been using Microsoft Azure Synapse Analytics for approximately two years.

Microsoft Azure Synapse Analytics is stable.

The scalability of Microsoft Azure Synapse Analytics is very good.

We have hundreds of people using this solution.

Compared to the traditional data center approach, the cloud has pushed forward well for maintenance reduction. There is a 60 to 80 percent reduction.

My advice to others is this solution is not meant for small databases.

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

Our primary use case for Azure Synapse is as a data warehouse, for creation of data pipelines. It allows login into to one central workspace, manage our databases, and the entire warehouse. We can embed business intelligence (BI), using Power BI. This allows us to show visualizations all in one central place.

I think potential areas to improve on could be performance and if they offered a decoupled compute from storage kind of service that would be nice. But I don't think that is possible as it's a fundamental change in the underlying architecture and Microsoft won't make that decision easily. 

We have been using Microsoft Azure technology for the last 4-5 years, including Synapse.

As Synapse is hosted on the Azure cloud, it's very stable. 

The Azure Synapse service is highly scalable. 

The initial setup is very straight forward. Azure Synapse offers you one workspace where you can do everything, creation of your data warehouse, ETL pipelines using Azure Data Factory, Create storage and data marts. Also use Power BI for visualization. 

Before Synapse was available, all of these was offered as separate services and this is how a data warehouse was constructed. Synapse is one layer on top of this where we make use of one single workspace to initiate and manage the entire set of services that you need for creating and managing your data platform - Data Warehouses and marts using SQL Warehouse, ETL pipelines using Azure Data Factory, Data Lake using Azure Blob Storage, and it offers server-less SQL - meaning you can run queries without having to initiate an SQL database or SQL data warehouse instance. It also offers Spark compute to process non-structured data.

We are a Microsoft partner and have setup and built Azure Synapse based solutions for our manufacturing, energy and healthcare clients. We are very customer centric and build and manage solutions based on our clients needs. We recommend what the best technology stack is for them. 

If I hosted a Microsoft setup on premise, I would need to invest in licensing for different tools and services, SQL server, SSIS, SSRS, Power BI or SSAS. Compared to this if you use Azure Synapse, the return on investment is very high. You get rid of your hardware, licensing and you move to a subscription based pay as you use model. Your operational costs reduce and your optimization increases. Capital expenditure absolutely diminishes and you move to an OpEx model. 

Finally, the overall management of it is simplified as compared to on premise. This of course leads to high RoI. 

Azure Synapse is best for people who are already invested in Microsoft technologies, in particular those who already use Microsoft data warehousing services, including MS SQL-Server based data warehouse technology. For them, migrating to Azure is very straight forward and Synapse adoption stays easy. 

With Azure Synapse, there is no database installation, no licensing cost, no hardware setup, everything is available as a cloud service, you then pay for the service, pay only for what you use. 

With regards to pricing, as I said you pay for what you use. The amount of data you store and compute power contributes to your pricing. If I use Azure's blob storage, the pricing depends on how much I use. If I utilize Azure Data Factory, pricing depends on how much data I process through the ETL pipelines and so on.

For some customers we recommend Snowflake, for others Azure Synapse or Google BigQuery. In one of our cases we are building a solution with both Azure Synapse and Snowflake. 

We have approximately 20-25 team members with knowledge of Azure Synapse service capabilities. 

With regards to deployment and maintenance, an Azure Synapse based solution may need anywhere between 3-15 people. This depends on what type of warehouse and analytics you want to create, the number of reports and visualization. Typically a small team size would be of 3-4 people and a large team size would be of around 12-14 members.