Microsoft Azure Synapse Analytics Reviews

We have implemented it for two solutions - data warehousing and BI Synapse (Business Intelligence). We also use it for merchandising, where based on our booking patterns, Synapse is used to analyze and target customers with more products. We use it for virtualizing, and BI is for analyzing business strategies.

The best thing about it is that it has integration at multiple places. It can talk to more than 90 types of data sources, which is one good thing about it.

There are some limitations based on the fact that it is a new platform. Some of our users have recommended switching to a different platform they are more familiar with. However, from an infrastructure standpoint, I believe it can be more secure in terms of availability.

Currently, even though we have locked down public access, the Synapse can still be viewed with the URL without VPN access. It only allows viewing the configuration and not any sensitive data or pipelines. We are working with Microsoft to address this issue, but it seems like they have recommended that it be open, and there is currently no solution. Perhaps there will be improvements in other aspects in the future.

We have been implementing and working with it for about a year now.

It's quite stable. So far, we haven't experienced any failures. If any failures occur, it will be due to the type of data or changes in it. But apart from that, the pipeline runs fine. We are getting our data reports that the team runs from Power BI. So everything seems to be stable so far.

We are premium customers. So, we have access to their top-tier team. I can say it's pretty good.

Positive

It was very complex, but we have grown out of it now. We have at least six environments only on Synapse.

This is the first solution that we are trying out. But if we plan to compare it with another solution called Snowflake, it will open up more dimensions for our thoughts to work. 

However, based on what we have achieved so far, I can rate it as an eight out of ten overall.

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. 

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.

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.