Microsoft Azure Synapse Analytics Reviews

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. 

We are a marketing company so we help our customers with marketing strategies. The marketing is based on the final customers, and we use Microsoft Azure Synapse Analytics for CDP.

The most valuable feature of Microsoft Azure Synapse Analytics is the pipeline that is the ETL tool. It's very well designed and is overall very good. We usually don't use the ETL tool in Databricks, but we use the ETL tool in this solution.

The SQL features in the solution are very good.

Microsoft Azure Synapse Analytics could improve the section in the solution where you can implement the Python Spark pipelines, it's not the same as in Databricks which would be better.

The data visualization in Microsoft Azure is provided by Power BI, it's not needed to have something in Synapse. The data governance tool is outside Synapse, but there is a data governance tool that is called Purview in Microsoft Azure. They need to improve the Spark part of the solution then it would be complete. 

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

Microsoft Azure Synapse Analytics is highly stable because it's based on SQL Server technology. It's a very old technology, which is solid.

Microsoft Azure Synapse Analytics is scalable.

I did not use another solution similar to Microsoft Azure Synapse Analytics.

The initial setup of Microsoft Azure Synapse Analytics is simple. It's a managed service in Microsoft Azure, you only need to search for it and install it.

You have to be very careful with one specific service inside Microsoft Azure Synapse Analytics which is called the Sequel Data Warehouse Dedicated. It is very reliable and performs well, but it's expensive. You need to define the tier well because you can choose between several tiers and you have to define which suits your needs and not overperform the tier because it's quite expensive.

Once a service is managed as a service in a cloud platform, the maintenance is very easy. You have to monitor it, but the maintenance infrastructure is extremely easy.

This solution is very easy once you have a Microsoft Azure subscription to go straight to Microsoft Azure Synapse Analytics because it's native. However, there are other kinds of technologies one can use. I would suggest before using this solution, look at other solutions, such as Databricks or Snowflake, and not stop at the first solution that you can receive in Microsoft Azure.

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

The good thing about Synapse is the scale factor. It can handle a lot more volume of data compared to Azure SQL. There is Azure SQL, and there is a SQL Data Warehouse, which is now called Synapse. SQL is for smaller databases, and SQL Data Warehouse (Synapse) is for larger databases. Performance-wise, if you process a huge amount of data, SQL Data Warehouse is good due to its parallel processing capability. It's scalable. 

The setup is pretty simple. 

It's stable. 

In Azure, when you do the scaling up, it is not totally simple. It takes time to scale up. It actually kind of rebuilds the database behind this when you scale. If I am utilizing 1,000 of what they call the Data Warehouse and you need 1,200, there is downtime required.

There is a limit on the number of concurrent queries to around 125 for Azure Synapse.

I've been using the solution for a while at this point. 

The solution is pretty stable. The dedicated pool is pretty stable. It can handle quite a heavy workload.

The product can scale. 

For one of my clients, it was used as an Enterprise Data Warehouse, so it has got all kinds of insurance functions, and users. It has got an underwriting team, it has got the actual team, it has got a claims operations team, et cetera. All the enterprise Data Warehouse users start consuming data while using the visualization tools like Boll-BI, and then MicroStrategy, et cetera.

The initial setup is not complex. It's pretty simple and straightforward. 

There was a pool of DBAs that maintain all the Data Warehouse on-premise and cloud. There were hundreds of databases, and this is just one of the databases added to that list. The DBA team is comprised of five to six DBA team members.

I'm not sure of the exact cost, however, it is around $100,000 a year. 

We are Microsoft partners. 

Potential customers should check out the ease of management. This solution is easier to maintain compared toother options. 

I'd rate the solution seven out of ten. There are some challenges related to this replication and then there is quite a lot of design thinking to be done.

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.

We primarily use the solution for our cloud data warehouse. 

The usability has been excellent. 

It offers good integration. 

The product works well with other Azure products.

The initial setup is very convenient.

It is scalable.

We have found the solution to be stable. 

The security is fine.

We'd like the solution to have more analytics capability.

We would like more detail on reports so that they are easy to understand in terms of usage of data and services. It could be improved.

I've been using the solution for one year. 

It's a stable product. It is reliable. The performance is good. It doesn't crash or freeze. There aren't bugs or glitches. 

We can scale the product up and down according to our needs.

We have around 100 people using the solution right now. 

My customer uses it on a daily basis.

We are satisfied with technical support. I haven't used them too much.

Neutral

I've also used Snowflake. 

Azure Synapse is a more integrated platform. With Snowflake, you need to buy it separately and run it off Microsoft. Snowflake, however, has better cost control. Snowflake is comparatively cost-efficient.

It is a straightforward setup. It's easy and not overly complex. I have not measured how long it actually takes; however, I can say the implementation is convenient. My administrator handled the setup. I did not handle it directly myself. 

The solution can get expensive, and it's hard to monitor the costs. It does depend on how you utilize it. You can configure it in a certain way.

I'd rate the solution seven out of ten. 

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 company includes 50 analysts who use the solution on a daily basis to predict customer behavior. 

The solution's best feature is its predictive analytics. 

Integration with other vendors has limitations and could be improved. 

Integration of process analytics would be a nice addition that allows us to analyze efficiency. 

I have been using the solution for six months. 

The solution is stable. 

The solution is a web data warehousing and analytical platform built for enterprise environments, so it is definitely scalable. 

Technical support is very responsive and knowledgeable. 

I rate support an eight out of ten. 

Positive

We do not use other solutions because we operate in a Microsoft environment that is not expected to go to AWS.

The initial setup is complex and I rate it a four out of ten. 

The solution was implemented through a third-party vendor team. 

The solution has helped to predict sales and focus attention on enhancements that have improved the bottom line. 

I rate the ROI a six out of ten. 

I don't know specifics about pricing but I hear that it is quite expensive. For example, Power BI is not as expensive as this solution.  

The solution has superior modeling and predictive analytical abilities in comparison to Power BI. There isn't really an expectation that you would get these superior abilities in Power BI because the solutions have different purposes and use cases. 

I rate the solution an eight out of ten. 

We use Microsoft Azure Synapse Analytics for a combination of a data warehouse with some advanced capabilities, to allow us to run some machine learning models on top of it. 

The most valuable feature of Microsoft Azure Synapse Analytics is the capabilities, and the integration with other Azure resources, such as Data Factory, Databricks, and Spark for data processing. The overall ability to compose the solution with other Azure resources is valuable.

Microsoft Azure Synapse Analytics could improve in usability. I have found the same issue with all Microsoft solutions.

I have been using Microsoft Azure Synapse Analytics for approximately 10 months.

Microsoft Azure Synapse Analytics is stable.

The scalability of Microsoft Azure Synapse Analytics is very good.

We have approximately 40 people using the solution. We have plans to increase our usage in the future.

I rate the support from Microsoft Azure Synapse Analytics a three out of five.

Neutral

I did not use another solution prior to using Microsoft Azure Synapse Analytics.

The initial setup of Microsoft Azure Synapse Analytics is straightforward. The full process took a couple of weeks when we started and finished.

We did the implementation of Microsoft Azure Synapse Analytics in-house.

We have a cloud services team with approximately three or four people and they take care of everything we need to support Microsoft Azure Synapse Analytics.

It's difficult to compute a return on investment in data analytics.

The price of Microsoft Azure Synapse Analytics can vary. Other solutions are typically fixed prices and a fixed cost regardless of usage, but this solution is based on usage or consumption.

The best comparison for Microsoft Azure Synapse Analytics would be with or against Snowflake. The only difference is that Microsoft Azure Synapse Analytics is more modular. If you want to build something very complex, you can. However, if you want to do something simple, you could stick with easy-to-use, more integrated platforms, such as Snowflake.

My advice to others is they should leverage other Azure components if they can. If they are looking for an integrated solution that's easy to use, they should look at competitors.

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