Microsoft Azure Synapse Analytics Reviews

Dealing with big data is the primary use case.

Its scalability and ease of use are valuable. It is fairly simple for a tool that's that powerful. If you have a background in Microsoft SQL Server, it is a very easy-to-transition path.

They should automate some of the features. There are some things, such as the creation of external tables, that you have to do manually. They should be automated.

In terms of additional features, they're adding new features all the time. They have new features faster than I can figure out what to do with them.

I have been using this solution for five years.

So far, it has been pretty good. In five years, it has had two failures, which is pretty acceptable from my standpoint. Those were more environmental failures.

It is pretty much infinitely scalable if you got the money to pay for it.

Currently, we probably have 600 or 700 users in the county. They are mostly data analysts and data scientists. As far as developers go, we probably have 15 developers working with it right now.

It is fairly simple. It is a fairly simple tool to set up. It is all just clicking buttons. There is nothing overly complex to the initial setup process.

In terms of maintenance, it is a cloud product. So, the updates happen behind the scenes.

It just depends on how big your instance is. It could be anywhere from 1,000 to 50,000 per year depending on how big your instance is. You pay based on how big you make your database. Essentially, they charge you per hour of usage.

I am generally very satisfied with it. So, I would definitely recommend it.

I would rate it a nine out of ten.

We use the solution for data storage.

The product is easy to use, and anybody can easily migrate to advanced DB.

Adding more transformations and plugins to the solution is very important.

I have been using Microsoft Azure Synapse Analytics for 10 years.

I rate the solution’s stability a seven to eight out of ten.

Around 20 to 30 users, including developers and architecture engineers, regularly use the solution for migration purposes. For a small organization, fewer people would be required for the migration.

I rate the solution a seven to eight out of ten for scalability.

The solution provides good technical support. The support team's response time depends on the issue's criticality. They will call you back in two or three hours if the severity is high. If the severity is low, they will take more than 24 hours.

Microsoft Azure Synapse Analytics is a moderately priced solution. We pay a yearly licensing fee for the solution. If you get help from partners, it will be expensive for you.

Microsoft Azure Synapse Analytics is a cloud-based solution. Since we are migrating, the cost is high. We assume the cost will be reduced post-migration, increasing the company's profitability. Microsoft Azure Synapse Analytics will be more profitable for any company in the long run. I would recommend the solution to other users.

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

I use the solution in my company for graphs. We need graphs for some resources on Office 365. We also do a lot of stuff with other applications, specifically third-party applications. In Microsoft Azure Synapse Analytics, we use pipelines to manage the data from third-party applications.

I love the way Microsoft Azure Synapse Analytics works, and I can also manage Microsoft Fabric. I can gather all the information using Microsoft Azure Synapse Analytics on Microsoft Fabric, including areas revolving around reporting using Microsoft Power BI. I love the way all the application resources from Microsoft can synchronize, help me with a lot of stuff, and reduce the time or hour of having maybe a resource for an hour a day while also being able to use artificial intelligence.

In our company, we always want to improve our data management, including how to manage structured and unstructured data. Every day, we need to do a lot of stuff because we are a bank. We have reports from user accounts and client accounts. Eventually, I want to use machine learning bots, maybe since they can help our company.

I have experience with Microsoft Azure Synapse Analytics.

I am good with the tool's stability part. For stability, I would prefer Microsoft Azure Synapse Analytics over other tools. Stability-wise, I rate the solution an eight out of ten.

It is a scalable solution. Scalability-wise, I rate the solution a seven to eight out of ten.

I rate the technical support an eight out of ten.

Positive

To be able to manage the product's initial setup phase, I believe that you have to know a little bit of networking. On a scale of one to ten, if ten is the easiest, I rate the setup phase as a five or six.

Three people can deploy the solution. A developer, an administrator, and I, the vice president of technology, can deploy the tool.

The tool's on-demand query capabilities help enhance our analytical tasks. I like the way the tool works, as I have people doing queries and an analyst doing a lot of stuff in the product. I love the way that we can manage Microsoft Azure Synapse Analytics.

One person can maintain the tool.

I rate the tool a seven out of ten.

Our company uses the solution to mine generous amounts of data for customers. We are partners with Microsoft and are totally dependent on the solution. 

Synapses are very good and can deal with petabytes of data. 

The solution has far better synapse analytics than Redshift. 

The solution handles single or complex items within a second. 

Power BI is a great management tool that has email in the synapse itself so you can move regulations. 

The solution does not support oriented scaling in the synapse. 

I have been using the solution for seven months. 

The solution is stable and properly distributes all kinds of data including symmetric multiprocessing. 

The solution is scalable.

I have not needed technical support. If I have an issue, there is always another staff member who can assist. The staff can solve complexities within seconds. 

The setup is quite easy and similar to Jira. 

We implement the solution for customers. 

Deployment can take some time because you first have to collect and develop the data through client relations. This can cause lags. 

The computer and storage costs are totally separate which allows you to work independently or manage costs. 

In contrast, Redshift combines both costs. 

The solution is far better than Redshift in terms of secondary indexing and the level of international support.

It is easy to migrate data to the solution from other clouds, even for big organizations. A complete migration can be done in less than ten hours.  

The solution is better than other cloud platforms so I recommend it. 

I rate the solution a nine 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 use the product to transform the data from the on-premise environment to the cloud. It is a flexible platform and integrates with multiple data sources.

The platform has multiple valuable use cases. They include performance, compatibility, flexibility, and cost.

Microsoft Azure Synapse Analytics's pricing could be reduced.

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

I rate the platform's stability a seven or eight out of ten. It could be improved.

We manage four to five Microsoft Azure Synapse Analytics customers in our environment. It is a scalable software.

The initial setup is easy using the infrastructure code. The deployment takes a maximum of a day to complete as we conduct a POC enrollment before the setup. It helps us understand the process. Later, we develop it in the production environment.

We need assistance from a third-party vendor for product implementation.

I am evaluating Oracle GoldenGate. However, it is a licensed software that induces extra costs. Instead, I want to utilize Microsoft cloud services to their full potential and avoid additional licensing costs. By doing so, I aim to reduce reliance on Oracle software throughout the migration journey, opting for cloud-native solutions that align with the goals of cost-effectiveness.

Many customers prefer using Microsoft Azure Synapse Analytics. It allows them to develop applications in Java and .NET. It provides security, flexibility, and efficient UI as well. It enables the ease of configuring APIs and overall configurability. As a certified cloud specialist, I advise others to consider use cases and requirements while purchasing.

I rate the product an eight out of ten.

When this application is in use, it's utilized to extract data and observe its performance and behavior. It also helps to make adjustments to fine-tune the application.

Azure Synapse combines the strengths of SQL technologies for effective enterprise data management. So, essentially, it excels in managing enterprise data.

I'm not very certain about suggesting specific improvements, but Microsoft consistently introduces enhancements, approximately on a quarterly basis, which is commendable. It could be beneficial to focus on integration with various data sources and similar enhancements. I noticed they have already integrated Power BI, which is advantageous. These developments are gaining prominence, and perhaps incorporating generative AI and leveraging it could be part of their future plans. 

It also provides both serverless and dedicated resource models. Azure Synapse already incorporates SQL machine learning models to some extent, but I anticipate that it will offer even more comprehensive AI capabilities in the future.

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

Azure products are highly scalable, so I would rate it nine out of ten. 

It is good most of the time, but it can be improved.

Positive

Before Microsoft Azure Synapse Analytics, we used different technology but shifted mainly because of the scalability.

If the person has a general understanding and some basic knowledge of the subject, setting it up shouldn't pose a significant problem or challenge. I would rate the setup an eight out of ten.

There are multiple teams, each with their individual setups, while the overall backend configuration and maintenance tasks are handled by a dedicated team. This team might be a global team consisting of more than twenty members who manage these responsibilities.

Microsoft's pricing is relatively high, and it varies according to the extent of our usage. The pricing is directly tied to our consumption. This decision is ultimately a business choice.

There were tests conducted alongside Microsoft Azure, although Azure was the preferred partner for our organization. It's important to note that the choice isn't solely based on price or the best product; sometimes, it's driven by the existing setup running on a particular platform, leading to the selection of the best available option.

There is a need to analyze the business objectives and technical needs before making a decision. If Synapse aligns with your business goals and meets your data warehousing requirements, you can proceed. Additionally, it's advisable to conduct price and scalability assessments and perhaps carry out a small proof of concept (POC) using production data before making a final decision.

I would rate the solution an eight out of ten.