Microsoft Azure Synapse Analytics Reviews

I mainly use it to run queries, deploy servers, and make connections across data. I help clients with data warehousing and analytics.

It's quite quick for querying, even with large datasets, and it's scalable. It's also flexible to use, so it's easy to update and get data quickly without wasting time.

The feature I like depends on what I'm doing. I go online to check how to do something and then use the features I need. They're all quite helpful.

I use it for data warehousing, so the SQL pool is a good feature to start with, and the pipeline easily aggregates data in the data flow. I also use the data flow tool, the pipeline, and connect it to PySpark for analytics.

One area that could be improved is the schema management. For instance, with Azure Data Lake, I sometimes try to create mappings. In MySQL or MongoDB, I can easily see the datasets and create connections without knowing the exact schema beforehand. I'm not as familiar with that process in Azure Synapse Analytics. It might be possible through tutorials, but it would be helpful to have more integrated tools for data scanning and schema exploration within the studio itself. This could help streamline the workflow and reduce the need to switch between different applications.

So, I'd like to have additional tools for data scanning and schema exploration within the Azure Synapse Analytics studio.

I have been using it for a year now. I'm a student, and I also use it for freelance work with clients.

I work with the latest version, I guess it is version 3. I use it on my MacBook. 

I would rate the stability a seven out of ten. There's been a failure at one point.

For me, it's been quite good. I haven't really had a problem with it. 

I will rate the scalability an eight out of ten. It depends on how much you scale it, but it's been proven to be quite scalable for what I'm working with.

Around eight end users are using this solution. The usage frequency depends on the job. Sometimes, it could be almost every day, depending on if we're receiving a lot of information. And at some point, it's quite slow, so we have to be on it almost every few days to check and update.

So, in the future, we may use it more than we are using it now.

When we have issues, customer service, and support get back to us with solutions. That's good enough.

I've used AWS and Google Cloud but for different purposes. I used AWS for learning, and Google Cloud was used for educational purposes. I helped a friend with AWS for their business on Amazon.

However, this is the first solution I've used for data warehousing. There are a lot of Google Cloud products, but for this job, we're focused on Azure.

I would rate my experience with the initial setup an eight out of ten, where one is difficult, and ten is easy because it is quite easy to set up. It is a user-friendly tool; even a new user can find a workaround. 

I have not seen any complexity related to the setup. It is quite easy. It took a couple of minutes to set up. 

For deployment, basically, I navigate to the Azure portal. I have my container ready, and I connect it to my repository. I use a deployment pipeline to deploy the solution. Sometimes, I use the Azure CLI to put in my code and connect it. It really just depends on where I'm getting my data from. It doesn't take me long, just a few minutes. It just depends on how I'm trying to access the data.

I work with one other person, so it's not just me. So, two people were involved in the process. 

We did see an ROI. Before, there were issues with the data warehouse and its use. My clients have seen improvements in the efficiency and implementation of the data. They can use the data much better and utilize it for solutions.

I would rate the ROI a nine out of ten, where one is zero value and ten is 100% value. 

It's about how it integrates into your work. If it's easy to integrate into your workflow, then I'd recommend it. It's quite easy to use, scalable, and has good processing time. Those are the major things for me. It's easy to use, scalable, and integrates well, then it's a good choice.  

Overall, I would rate the solution 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.

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.

It can be used for reducing hardware expenses.

It is a highly stable solution and it's easy to use. 

The security performance and cost are the two things that needs improvement. 

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

It is a stable solution.  

It is a scalable solution.  

The initial setup is easy. It does not take much time to deploy the solution. 

You have to go to the resources and the security SSC group and the system for deployment. It can be done in-house. 

We have a licensing cost to pay. 

Overall, I would rate the solution a nine 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. 

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.

We use Synapse to build views for our data warehouse. It is the place where we get our reports and views. We ingest data into our SQL Servers and Azure SQL, then build all our reporting views within Synapse.

I'm the cloud architect, and I have a two-person DevOps team plus another enterprise architect. Five people are using the interface at any given time. 

Synapse makes it easy to integrate and onboard data from other Microsoft and Azure sources. The interface is familiar because we were using Azure Data Factory before Synapse. It made the transition even easier because the Synapse interface is exactly the same. 

Cost is always the primary pain point. However, we've gotten to the stage where we can set aside finding, and the price is reasonable for our project. 

We have used Synapse Analytics for about a year.

I rate Synapse nine out of 10 for stability. We've had some issues in the past that needed to be addressed, but those were related to how we did our transformations. We needed to learn from our mistakes and figure out how to properly schedule jobs, imports, etc. 

I rate Synapse 10 out of 10 for scalability. Scaling up Synapse is quick and easy. That's one thing I appreciate the most about it. You can scale with a click 

I rate Microsoft support 10 out of 10. We have direct professional support, and they always come through for you if you run into any issues. Our tickets are never open for more than two days. 

Positive

We didn't have anything in place because we implemented Synapse for a specific project. We formulated our use case, considered our options, and decided on Synapse because ware primarily an Azure shop. Using Synapse made sense because it's easier to manage and support. 

I rate Synapse Analytics eight out of 10 for ease of setup. I've set up Synapse so many times in various environments that it's seamless. I have no issues with the product at this point.

We did extensive research and a POC before implementing Synapse Analytics. It took us a couple of days to deploy, including the POC. We had to separate our groups and stuff like that. The second step was to leverage templates for creating the environment. Everything is automated now. If want to do this again, I don't have to go to the portal. I can run it from a template. 

I rate Synapse Analytics seven out of 10. We had to learn a lot before we could get costs under control and budget appropriately. There were a lot of unknowns initially. 

I rate Azure Synapse Analytics eight out of 10. I would recommend Synapse depending on the needs you're trying to address. You need to properly build out your users and see that the technology is going to address your use case. After that, you can make an informed decision about what's best for your company. 

Companies come in all shapes and sizes. Synapse may not be a good fit for one company, but it can work well for others. Try it out first and see if it's appropriate for you.

The intention is for the company to utilize Microsoft Azure Synapse Analytics for analytics purposes. The team manages numerous databases and utilizes them to handle multiple tasks.

The most advantageous aspect of Microsoft Azure Synapse Analytics is its simplified data transformation process compared to traditional databases. This makes data cleansing and transformation more manageable and straightforward, which we appreciate. It is much easier to build as well.

The support and price could improve.

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

The stability is great.

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

The solution is scalable.

We use the solution intermittently, approximately 50 percent of the time.

We have approximately 1,500 users using the solution.

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

The level of support from Microsoft Azure Synapse Analytics is average because it takes some time to reach the appropriate person, causing delays. Although we eventually connect with the right person, the initial representative assigned to the call is often unqualified to provide the necessary assistance. They simply repeat what is already available on the website, which we are capable of accessing ourselves. Our goal is to obtain a reliable technical solution, and the turnaround time from the point of contact to receiving a response is longer than ideal. This delay applies to both L1 and L2 support.

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

Neutral

The initial setup took us two weeks at the beginning and now we can do it in less than one week.

The initial setup of Microsoft Azure Synapse Analytics is straightforward.

We accomplished the deployment of the solution by ourselves with the assistance of two architects and four DevOps professionals.

Microsoft Azure Synapse Analytics is an expensive solution and there is a license needed to use it. My company has an enterprise license.

I rate the price of Microsoft Azure Synapse Analytics

We have four DevOp engineers that do the maintenance of the solution.

If your data models are not accurate, and if you are not well-versed in data transformation and model security, the process will be extremely challenging. Thorough planning is essential for the data to be properly managed. You cannot simply execute or implement the process by reading about it. A significant amount of planning is required, including how the data flows will access the data, data security measures, data flow processes, and transformation requirements. All of these aspects must be carefully examined and planned beforehand to avoid complications later on.

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

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.