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.

Our data is available on-premises, so we are extracting the data from our private on-premises servers and Azure databases.

I like SQL post, which is for storage and distributed computing. Another good feature is the copy activity.

Synapse Analytics needs to develop an automation framework because now you have to build a cache yourself. You have to build a pipeline in WhereScape, which does end-to-end pipeline automation well. 

Microsoft should come up with a framework to save people time. If they developed a tool like WhereScape, it would dramatically reduce development time.

I'm waiting for Microsoft to implement a feature that automatically detects and processes the copy activity. That feature is available in Databricks, but I hope they will add it to Synapse Analytics' data factory integration run-time.

I've been using Synapse Analytics for two or three years.

Synapse Analytics is stable because the scope is so broad. Anything you do in it is like 1 percent of the total solution.

Scalability is built in. You don't need to do anything. If you're working with a terabyte of data on one day, you can go up to a petabyte the next day. There's no need to worry about scalability in terms of performance.

Azure support is excellent.

Installation is simple. I handled the installation alone, and we performed the entire deployment in DevOps. You need at least a five-person team to manage and maintain the solution 

There's no consultant involved in this as far as the platform architecture is concerned because I own that architecture end to end.

Our license is about $5,000 per month, but it's $5,000 to $10,000 per month when you factor in services and everything else. 

I rate Azure Synapse Analytics eight out of 10. No competitors provide the entire solution to one place like Synapse. For example, a database just focuses moving and manipulating data, etc. But Synapse is like an all-inclusive workspace. 

I advise other people to go with Databricks Notebook if you need a computation engine. It has a solid SQL storage procedure. 

Suppose you are dealing with complex transformation logic and manipulation of run-time data flows. In that case, it's better to use Databricks than any Microsoft ADF. DataBricks looks more promising in terms of computing in memory, so we integrated Databricks in Synapse.

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.

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 have implemented it for two solutions - data warehousing and BI Synapse (Business Intelligence). We also use it for merchandising, where based on our booking patterns, Synapse is used to analyze and target customers with more products. We use it for virtualizing, and BI is for analyzing business strategies.

The best thing about it is that it has integration at multiple places. It can talk to more than 90 types of data sources, which is one good thing about it.

There are some limitations based on the fact that it is a new platform. Some of our users have recommended switching to a different platform they are more familiar with. However, from an infrastructure standpoint, I believe it can be more secure in terms of availability.

Currently, even though we have locked down public access, the Synapse can still be viewed with the URL without VPN access. It only allows viewing the configuration and not any sensitive data or pipelines. We are working with Microsoft to address this issue, but it seems like they have recommended that it be open, and there is currently no solution. Perhaps there will be improvements in other aspects in the future.

We have been implementing and working with it for about a year now.

It's quite stable. So far, we haven't experienced any failures. If any failures occur, it will be due to the type of data or changes in it. But apart from that, the pipeline runs fine. We are getting our data reports that the team runs from Power BI. So everything seems to be stable so far.

We are premium customers. So, we have access to their top-tier team. I can say it's pretty good.

Positive

It was very complex, but we have grown out of it now. We have at least six environments only on Synapse.

This is the first solution that we are trying out. But if we plan to compare it with another solution called Snowflake, it will open up more dimensions for our thoughts to work. 

However, based on what we have achieved so far, I can rate it as an eight out of ten overall.

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.

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.