Microsoft Azure Synapse Analytics Reviews

I work with Azure Synapse Analytics, Power BI, and Premier as a data administrator, using Microsoft's Azure cloud infrastructure. Azure Synapse Analytics is the principal data administration tool for our internal data warehousing, but we also use it for our customers' data warehousing and analytics in different industries. For data entry, we use Microsoft Power BI.

Internally, we have about 200 personnel currently using Azure Synapse Analytics, including data specialists, service managers, and other tech workers. It is not extensively used by all, however, because we don't often use it for very large amounts of data, except where financial data is involved.

Azure Synapse Analytics helps us move quickly when it comes to finding data warehousing and analytics solutions for our customers.

We use Azure Synapse Analytics in many different areas and industries, so I like that you can administrate and create pipelines for different sources of data and later integrate and deploy it to other internal areas, such as separate dashboards for financials, and so on.

It is our main data warehouse solution, and the one big feature I appreciate is all its various analytics functions. The built-in analytics also makes it an attractive option for our customers, where we act as a service provider for them.

Unfortunately, we have had some issues with the dashboard reporting. Sometimes, the data for specific periods would just appear blank on the dashboard. To investigate this, we worked with a Microsoft incident agent and it turned out to be a result of bugs in the platform when dealing with specific types of queries in Azure Data Factory.

We have also encountered some bugs regarding incompatibility with certain versions of Power BI. In the past, Power BI used to integrate nicely with Azure Analysis Services which we used as a bridge between Power BI and Synapse Analytics, but they have since discontinued the features that worked so well for us.

Apart from fixing bugs, I would also suggest:

• The learning features and documentation could be improved and expanded on.
• The calculation and forecasting tools could be made more clear and easy to use.
• The price could be lower.
  

I have been using Microsoft Azure Synapse Analytics for one and a half years now. 

When it comes to stability, Synapse Analytics has suited us well, because it's very fast, i.e. it has good performance. Compared with the on-premises SQL solutions we've used, the results are two times as fast.

There are some bugs that we've encountered with the platform but we are able to work with Microsoft directly to resolve them as they come up.

I'm very impressed by the scalability of Synapse Analytics, even though we don't use it for huge amounts of data most of the time. Combined with the availability of machine learning and other features in the cloud, it has a lot of potential, especially in the area of finance.

In our context as service provider, normally we have direct contact with the server. But in some cases, we have needed extra support which was very slow.

For technical issues, I often solve them myself using Microsoft's learning resources and support forum.

For me, it's the fifth data warehouse I've used. Other specialists here also use other warehousing solutions based on SQL but for the moment Synapse Analytics is the best overall solution for us primarily because of its ease of implementation and flexibility.

Because I was not familiar with the solution beforehand, the initial setup was quite complex. I had to learn a lot in the beginning stages. I'm grateful that there is help to be found in the Microsoft forums, however, and there are also options to study for certifications that help you learn how to use the platform properly.

It took approximately six months to implement Azure Synapse Analytics in our organization. We set up the data warehouse features for internal use across 200 users, including data specialists, service managers, and tech workers who manage our own data and also our customers' data.

For the main deployment, we used a third-party support provider in Chile who helped us greatly.

We normally pay between $300 and $500 per month, which is quite expensive for how much we actually use it, performance- and usage-wise. They have a cheap version and an expensive version, and our usage usually falls in the middle ground, which makes it not as cost-effective as it could be.

We evaluated on-premises SQL solutions for data warehousing, but our main motivation for using Synapse Analytics instead is that it lets us come up with and manage customer solutions very quickly. Some specialists are still using other SQL solutions as well, however.

There is always room for improvement with Azure Synapse Analytics, but for our use cases it works very well. 

I would rate Microsoft Azure Synapse Analytics a seven out of ten.

We are an IT service provider. We develop solutions for our clients and this solution is used in some aspects of that.

The dashboards are good.

We haven't had any issues when it comes to customization.

The security seems to be okay. It hasn't given us any problems and we don't feel insecure.

The pricing seems to be quite fair.

I know their roadmap, and I am looking forward to what they have already published.

We've been happy with the level of reporting and documentation on offer.

There aren't any features that are really lacking in the solution. We don't really have any issues with it in its current form.

When we used the on-premises deployment model, we had data latency issues and suffered from page performance problems. However, since we've moved to the cloud, we haven't had these problems.

The initial setup has a bit of a learning curve.

We've been using the solution for the past 18 months so far.

The solution seems to be scalable. We haven't had any issues with bugs or glitches. It doesn't crash or freeze. It's quite good.

The scalability has been pretty good, in our experience. We have been able to scale it twice to the number of users we needed. We were also able to double the amount of data load. We haven't had issues. If a company needs to expand, it should be able to do so without problems.

We've dealt with technical support and have found them to be quite responsive and knowledgeable. We're quite satisfied with the level of support we have been getting so far. They're helpful.

It's hard to gauge the difficulty or simplicity of the setup. For our company, we were learning the processes as we were doing it. In that sense, it was challenging due to the fact that we had to learn and then do. After a period of time, we were able to get comfortable.

We handled the implementation ourselves. We taught ourselves about the process as we went.

We pay a licensing fee on a monthly basis. We find the pricing to be fair among the competition.

We're just end-users and customers. We don't have a business relationship with the organization.

We're using the latest version of the solution right now.

I'd recommend the solution to other organizations.

I'd rate the solution at a nine out of ten.

The product is easy to use. 

One potential area for improvement could be the availability of an on-premises data lake implementation, as the product is currently only implemented in a cloud environment. 

Additionally, the possibility of integrating data from multiple sources could be beneficial. 

We have used Microsoft Azure Synapse Analytics for approximately three months.

The platform's scalability is similar to that of SQL databases. It requires developing components, implementing services, and performing other related tasks.

The initial setup process is straightforward. 

My advice would be to remain open to learning new things, particularly because working with Synapse Analytics requires a shift in mindset from traditional tools like SQL Management Studio to newer tools and approaches.

I rate it an eight out of ten. 

We encountered data processing and transformation issues while working with Apache Spark languages for the product. This particular area needs improvement. 

The product's error messaging could be friendlier. Additionally, the monitoring process using Azure ML SDK and Python should depend on the data for the results. Along with the documentation, they should provide essential training for proficiency in CLI. It will help in better monitoring.

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

The product is expensive.

I rate Microsoft Azure Synapse Analytics a seven out of ten. It is a fantastic product; we are satisfied with its features and performance. However, there could be more information available about the platform.

I use this solution for client service forecasting. I work with medium-sized businesses, and I work through a virtual machine.

Synapse Analytics gives you information and reports in real-time.

The platform is not flexible, and the graphical user interface needs to be improved because the interface makes it hard for the end user to use it.

Also, artificial intelligence could be used to improve analytics.

I have used the solution for over a year.

The solution gets support, which helps its stability.

Microsoft's technical support is good when I compare it with other platforms like Oracle. You can also fix many problems yourself because Microsoft has an active community.

The initial setup is easy if you have information about analytics visualization and data warehousing. It's easy enough to install and configure the blade. It takes one day to deploy the solution.

The solution is expensive at the enterprise level. The license is monthly.

Microsoft is a good platform because it's compatible with data warehouse, power analytics, and visualization, and you now have excellent machine learning and AI tools like GPT-4. The platform is also very stable, and support can be migrated for software.

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

We use it for reporting purposes and for building data warehouses, pipelines, and data lakes for IoT projects.

I like how Microsoft Azure Synapse Analytics integrates with other Microsoft solutions. 

Real-time integration is hard to do in Microsoft Azure Synapse Analytics.

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

I would rate the solution's stability at nine out of ten.

For the amount of data that we have, Microsoft Azure Synapse Analytics has worked perfectly fine in terms of scalability. Thus, for us the scalability is a ten out of ten.

The documentation is good, but if you cannot find the right documentation and the right steps, then the initial setup can be hard. I would rate the initial deployment at seven out of ten.

This solution is deployed on the cloud, and it took half a day to deploy it. I configured the data lake storage, created the workspace, and added users and user rights. Building pipelines is a straightforward process.

I think Microsoft Azure Synapse Analytics is priced well, and I would rate the price at eight out of ten.

On a scale from one to ten, I would rate Microsoft Azure Synapse Analytics at seven. Other solutions have more user-friendly and intuitive interfaces.

We use this solution to analyze big data. We collect data from different sources then we analyze that data with Synapse.

One of the most valuable features of this solution is its ability to integrate well with other services offered by Azure. 

Scaling this solution up and down is not quick and easy. This could be improved. The pricing of this solution could also be improved. 

We have used this solution for two years. 

This is a stable solution but we have experienced some unavailability issues. These were not critical issues. 

This solution is quite easy to scale depending on your workload. Overall it is a little bit expensive. You have to know exactly what you are doing as you scale up, otherwise the cost goes up quite quickly.

This solution is relatively easy to set up and use. The time it takes to setup depends on the complexity of your environment. If you want to complete a basic setup, it only involves a few clicks. In a production environment for a complex setup, it's it takes some time to complete the configuration. 

We completed the setup in-house. 

Synapse is useful for very big loads of data. 

I would rate this solution a seven out of ten. 

I am the Associate Director of a company that is a partner of Synapse.

The solution offers strong scalability opportunities.

I would like to see more ready-to-use products from Synapse. Right now, everything seems a bit futuristic without much modern use.

The solution is scalable.

Price is dependent of the use case of the customer. Customers are not likely to only use this as an individual platform/database, they are likely to bundle it with the Azure stack.

Snowflake is a solution that is much more ahead of the game. There are many gaps in Synapse that Snowflake has fulfilled (i.e. usage, and storage).

Be sure to be clear about the ideal pipeline for Synapse, differentiate your needs, and validate based on use-case.

I would rate this solution a six out of ten overall.

We have a huge amount of data, and we need a platform where we can process the data using Spark clusters. We wanted an environment that included databases that are capable of parallel processing, and Azure Synapse was one of the best for that.

This product has given us a lot of processing power with the database, and at the backend as well.

The most valuable feature is the level of processing power, and being able to complete tasks in parallel. This is the feature that attracts us the most because we have a lot of data that we need to process in a short time.

This is a user-friendly environment.

Integration with other products is an area that can be improved.

I have been working with Microsoft Azure Synapse Analytics for between five and six months.

Synapse definitely seems like a stable product.

Synapse Analytics is deployed in a cloud environment and one of the best advantages of this is that it provides scalability.

I have not had any experience with Microsoft technical support.

I also have experience with the services available on AWS.

Configuring this solution was easy.

We implemented it with our in-house team. I was part of the team, one of my colleagues was the solution architect, and we have some engineers that assisted us. It was a team effort.

The costs are billed such that we pay for what we use, which is a good thing. For example, we have an environment where we don't need access to certain services, so we don't have to pay for them.

Compared to the services available on AWS, this solution is a little bit pricey.

At this point, Synapse Analytics is fulfilling all of our requirements for the use cases that we have.

I would rate this solution a seven out of ten.

We use Microsoft Azure Synapse Analytics for analytics.

If I'm looking for something good in the cloud, I would want it to have better standard connectors.

I have been working with Microsoft Azure Synapse Analytics for two years.

We are up-to-date with the latest version.

I have been working with Microsoft, and they have been very helpful.

It's the only product I've used. I'm not sure what's wrong or what could be done better.

I don't know about the price, but I'm trying to get a sense of the cost. I believe having a good comparison would help.

This is the only product I've ever used, as such, it's extremely difficult for me to say anything about it. For example, if I needed a bike, and now I have one and am riding it. If you ask me some questions, it's difficult for me to say, "Oh, I really missed this one on my first bike," unless I've ridden a few bikes. I'm not sure why everything is the way it is. I had a need, and I'm only using it now.

I'm trying to understand what else is out there and how it compares to what is out there.

I am able to use it. If I am able to use it, I would rate Microsoft Azure Synapse Analytics an eight out of ten.