Microsoft Azure Synapse Analytics Reviews

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.

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.

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.

The solution offers great flexibility and the resource availability is quite good. It's abundant in the market. 

The initial setup is very simple.

The only concern for us is the cost part. When it comes to the implementation and the support and maintenance, we see high-cost implications.

Of course, it varies from use case to use case. Before we get into the implementation part, we have to validate the pros and cons of the architectural components as part of the design and development. Once that's clear, then we'll go for implementation. We might get into technical glitches, however, there are multiple ways to work around them by putting in the right architectural component, which can solve the problem. There is always a workaround.

We've had a couple of interactive sessions with Microsoft already. We have already recommended that they need to strengthen their presence in the data governance part, the data quality part, and then the metadata management, for example, data lineage. We need more data governance to give the flexibility to handle these data quality issues.

It would be great if they update their data features. 

We have been working with the solution for the past one and a half years.

The solution is mostly stable, however, we do experience glitches here and there. We do have solutions that ultimately correct the issue.

Azure cloud is very scalable and allows this product to scale as well. We can scale horizontally and vertically.

Technical support is so far, pretty good. It's solving the problem now. Some other clouds are coming in, such as AWS or Google Cloud. They'll eventually be much more competitive in terms of comparatively.

We have used hybrid architecture also. We used the storage part and we have used Snowflake also. Everything is available in one place so that the connectivity issues are solved and you get one component talking to everything. 

It's a very simple setup. it's not overly difficult at all. 

In terms of maintenance, it depends on the scope of coverage and other things. If it is a 24 by 7, we need a set of 14. Depending upon the service coverage and scope coverage, you'll need a certain amount of people. Even that depends upon the scope of the work also. Does the setup require monitoring, for example, or enhancements? Basically, it depends upon the type of contact, and what we get into the AMS projects in terms of the team composition.

While we do not have a business relationship with Azure, we are trying to become partners. 

Most of our implementations happen on Azure itself. Wherever we go, we take the solutions and we go to the other customers and we propose them. We tend to recommend this solution.

I'd rate the solution seven out of ten. There is a scope for improvement. Eventually, as they come across different case by case, they'll enhance things. Currently, what I see here is the data governance part is missing here from a development life cycle point of view.

Microsoft Azure Synapse Analytics differs from the old traditional on-premise business intelligence operations, where it's set up to do real-time analytics. For example, with IoT devices. Instead of having patients come to the hospital and do their operations, the hospitals will give patients an IoT device and you can monitor the patients in real-time using Microsoft Azure Synapse Analytics.

The point of the solution is to integrate the business into the analytics or vice versa. In the hospital example above, traditionally analytics is to tell us what happened. We look at reports to see what has happened. Microsoft Azure Synapse Analytics puts us more on the spot by telling us what's going to happen. Rather than what did just happen.

Microsoft Azure Synapse Analytics could improve its compatibility with Visual Studio. One of the challenges for people moving from an on-premise to a cloud solution, such as Microsoft Azure Synapse Analytics, is that you're constantly working in a browser. There are people that have been working for decades on desktop applications. For them to start working in a browser, it's quite a change. Allowing people to work and do their work inside Visual Studio than in the browser, would be a large advantage.

I have been using Microsoft Azure Synapse Analytics for approximately one year.

Microsoft Azure Synapse Analytics is created out-of-the-box to be scalable. That was always the struggle of Analytics and BI Teams you had to spend $2 million to receive the server with RAM and disk space needed to use this type of solution. Microsoft Azure Synapse Analytics will scale automatically behind the scenes. Which is one of its main powerful features.

We have many people using the solution. The data engineer, who is moving data around and brings data in. The data scientist and the IoT developer. There are different areas in Microsoft Azure Synapse Analytics for each role.

I have not used the technical support from Microsoft Azure Synapse Analytics, but I worked with the developers at Microsoft who were top-notch. 

In my proof of concept project, I would simply email Microsoft directly and they were really responsive. The ticketing system on the Azure platform is very good. You just click open ticket, and they will get back to you quickly.

There are not many books on Microsoft Azure Synapse Analytics but there are tutorials and all that information online. However, the tutorials on Microsoft's site are enough to get you started.

The setup of Microsoft Azure Synapse Analytics is different than anything we've used on-site. It is different from the Analysis Services five years ago. However, even though it's different, the setup is easy.

One of the benefits of Microsoft Azure Synapse Analytics is that you shouldn't have to do any maintenance. It's all done behind the scenes. There is a serverless feature, where it'll expand and add RAM and add resources as needed. You have to be careful with the cost.

There's no license required for Microsoft Azure Synapse Analytics. the model is more of a use-based system. You got to pay for computing power and disk storage. Everything has different units and is kept backed up. Microsoft Azure Synapse Analytics uses storage units(SU). This is how everything's computed for cost.

My advice to those wanting to use this solution is to start small to get an understanding of how it operates.

I rate Microsoft Azure Synapse Analytics a nine out of ten.

We use this solution as a data warehouse. Power BI allows us to visualize the data while analyzing data from the data lake through Serverless. We have not used too much of the pipelines within the Data Factory because we have an isolated Data Factory for that.

This is a stable solution with many functionalities.

The only issue that we have run into with the solution's performance is with regard to concurrency.

We would also like to see faster response times from support.

I have used this solution for three years.

The stability of this solution is good.

The solution is scalable. We currently have fifty to sixty people working within it and, now that we are using Serverless more often, we are planning to have more people working with it.

Support is good but they could be a bit faster replying to our tickets.

The initial setup was straightforward.

We had one admin complete the initial setup in ten to twenty minutes.

Price varies by use-case. You pay for the database itself in addition to any consumed data within Serverless plus other fees if you use the Data Factory that is inside.

I would recommend others to use this product but urge them to understand that it is more of a technical database used to prepare data as opposed to serving it.

I would rate this solution a seven out of ten.

The most valuable aspect of this Microsoft Azure Synapse Analytics is its consolidation of technical support from Microsoft, and its ability to securely host large quantities of data within the cloud environment. The overall ability to manage and maintain Big Data within the cloud provides a heightened level of efficiency, reliability, and support from Microsoft. This results in a superior user experience and an increased level of value for the end user.

There may be some challenges in terms of connecting with Virtual Networks (VNETs) to Microsoft Azure Synapse Analytics.

I have been using Microsoft Azure Synapse Analytics for approximately one year.

The stability of Microsoft Azure Synapse Analytics is great. It offers a high level of performance.

The scalability of Microsoft Azure Synapse Analytics is good.

The support from Microsoft Azure Synapse Analytics is excellent.

There is a steep learning curve to do the implementation.

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

The entire Microsoft Azure ecosystem is well connected and Microsoft has done well with the features.

My advice to others is to try out the solution, do your homework and research, and set up a POC environment.

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