Microsoft Azure Synapse Analytics Reviews

We use Microsoft Azure Synapse Analytics in several scenarios, we require access to high-performance computing capabilities. This is where the solution proves to be a valuable asset as it offers a node-based solution for computing needs. In numerous cases, we have to undertake intricate data processing operations using the Python programming language, and that's where the solution comes in as an advantageous tool. These are my primary use cases for Synapse. Whenever we need to handle complex data engineering tasks or require significant computing power to accelerate processes, the solution provides the necessary functionality.

The most valuable features of Microsoft Azure Synapse Analytics are its serverless flexibility and complete power have allowed me to explore various different use cases. While I am not an expert in the product, my experience in programming in Databricks has shown me that Microsoft's investments in Synapse could potentially lead to it becoming a complete replacement for Databricks in the future.

In the future, Microsoft Azure Synapse Analytics has the potential to enhance its capabilities by expanding its connectors, specifically with regard to Oracle solutions, such as operating systems. This would involve a comprehensive approach to adding more connectors for both data input and consumption purposes. By doing so, Microsoft Azure Synapse Analytics would be better equipped to meet the diverse needs of its users and achieve greater efficiency in its performance. The provision of more connectors is definitely a crucial area that needs improvement.

I have been using Microsoft Azure Synapse Analytics for approximately two years.

I have not had any stability or performance issues.

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

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

Before adopting Microsoft Azure Synapse Analytics as my solution of choice, I delved into several other options. One of these alternatives is Snowflake, which I must admit, is a superior choice compared to Microsoft Azure Synapse Analytics. However, it is essential to take into consideration the consumption side when evaluating these solutions. If the consumption side involves Oracle solutions, then an autonomous warehouse would perform better than this solution. On the other hand, if the consumption is within the Azure platform, this solution presents itself as a commendable solution.

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

We have a team that does the implementation.

The solution is worth the cost for our use case, it is worth the money. Despite the fact that there may be room for further optimization of its complete power, the cost-to-flexibility ratio of Synapse is quite favorable. I do not have any specific metrics to support my statement, however, I can assure you that Synapse is not inadequate or disappointing in any manner.

Microsoft Azure Synapse Analytics can be costly, however, a cost-effective approach would be to purchase it in advance through reservation for either one or three years. This will significantly reduce the overall expenses incurred.

After thoroughly evaluating various data management platforms, such as Oracle Autonomous Warehouse, Databricks, Microsoft Azure Synapse Analytics, and Snowflake over the course of the past year and a half, I have come to the conclusion that flexibility is crucial when it comes to choosing the right tool. Microsoft Azure Synapse Analytics stands out as a strong contender, as it offers a unique combination of both traditional warehouse capabilities and modern technological advancements. This combination of features makes Microsoft Azure Synapse Analytics a valuable option and is impressive.

My advice to others is to start with a reserved instance in order to test the waters and make sure it fits their needs before fully committing to using the solution. If, after experimentation, they decide to proceed with using Microsoft Azure Synapse Analytic over its competitors, then I would highly recommend going for a reserved instance to make the most cost-effective and efficient choice.

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

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.

Our company uses the solution to provide data warehousing for customers. Most of our customers are mid-sized. 

Data can be stored any way you want in the data warehouse. Some customers want monolayers which we add with no issues.

The solution is very fast. 

It is reasonably easy to work within the solution. 

The solution should offer a serverless model like Snowflake so you don't have to manage the hyper or operating system layers. 

There are sometimes problems when connecting to the database. Most issues are solved by the community but Microsoft should act more quickly and participate in the process. They should not wait for the community to build a solution before embracing it. 

I have been using the solution for one year. 

The solution is stable. 

The solution is scalable to a customer's needs. A few customers have written scripts to scale how they want, but most customers use the out-of-the-box database.  

We have not used technical support in awhile but it was okay. 

I previously used Redshift and Snowflake. 

The setup was complex the first few times but we figured out the best way to do it for our mid-sized customers. The setup could be made easier. 

We implement the solution for our customers. 

The pricing is quite reasonable in comparison to other products. Of course, most companies would like the price to be even cheaper. 

Pricing depends on setup but generally ranges from 28,000 to 35,000 Euros per year for a mid-sized company. 

The solution's speed is better than Redshift and Snowflake. 

The solution does take some management. Mid-sized companies would like to work on data instead of hardware or operating system layers. 

It is important to have skilled Azure data engineers to manage the solution. 

I rate the solution an eight out of ten. The solution could improve its rating with an easier setup and more readily-available data connectors. 

We are a marketing company so we help our customers with marketing strategies. The marketing is based on the final customers, and we use Microsoft Azure Synapse Analytics for CDP.

The most valuable feature of Microsoft Azure Synapse Analytics is the pipeline that is the ETL tool. It's very well designed and is overall very good. We usually don't use the ETL tool in Databricks, but we use the ETL tool in this solution.

The SQL features in the solution are very good.

Microsoft Azure Synapse Analytics could improve the section in the solution where you can implement the Python Spark pipelines, it's not the same as in Databricks which would be better.

The data visualization in Microsoft Azure is provided by Power BI, it's not needed to have something in Synapse. The data governance tool is outside Synapse, but there is a data governance tool that is called Purview in Microsoft Azure. They need to improve the Spark part of the solution then it would be complete. 

I have been using Microsoft Azure Synapse Analytics for approximately two years.

Microsoft Azure Synapse Analytics is highly stable because it's based on SQL Server technology. It's a very old technology, which is solid.

Microsoft Azure Synapse Analytics is scalable.

I did not use another solution similar to Microsoft Azure Synapse Analytics.

The initial setup of Microsoft Azure Synapse Analytics is simple. It's a managed service in Microsoft Azure, you only need to search for it and install it.

You have to be very careful with one specific service inside Microsoft Azure Synapse Analytics which is called the Sequel Data Warehouse Dedicated. It is very reliable and performs well, but it's expensive. You need to define the tier well because you can choose between several tiers and you have to define which suits your needs and not overperform the tier because it's quite expensive.

Once a service is managed as a service in a cloud platform, the maintenance is very easy. You have to monitor it, but the maintenance infrastructure is extremely easy.

This solution is very easy once you have a Microsoft Azure subscription to go straight to Microsoft Azure Synapse Analytics because it's native. However, there are other kinds of technologies one can use. I would suggest before using this solution, look at other solutions, such as Databricks or Snowflake, and not stop at the first solution that you can receive in Microsoft Azure.

I rate Microsoft Azure Synapse Analytics an eight 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.

We primarily use the solution for our cloud data warehouse. 

The usability has been excellent. 

It offers good integration. 

The product works well with other Azure products.

The initial setup is very convenient.

It is scalable.

We have found the solution to be stable. 

The security is fine.

We'd like the solution to have more analytics capability.

We would like more detail on reports so that they are easy to understand in terms of usage of data and services. It could be improved.

I've been using the solution for one year. 

It's a stable product. It is reliable. The performance is good. It doesn't crash or freeze. There aren't bugs or glitches. 

We can scale the product up and down according to our needs.

We have around 100 people using the solution right now. 

My customer uses it on a daily basis.

We are satisfied with technical support. I haven't used them too much.

Neutral

I've also used Snowflake. 

Azure Synapse is a more integrated platform. With Snowflake, you need to buy it separately and run it off Microsoft. Snowflake, however, has better cost control. Snowflake is comparatively cost-efficient.

It is a straightforward setup. It's easy and not overly complex. I have not measured how long it actually takes; however, I can say the implementation is convenient. My administrator handled the setup. I did not handle it directly myself. 

The solution can get expensive, and it's hard to monitor the costs. It does depend on how you utilize it. You can configure it in a certain way.

I'd rate the solution seven out of ten. 

Microsoft Azure Synapse Analytics is used for analytics.

The most valuable features of Microsoft Azure Synapse Analytics are the interface and the agility of the on-cloud platform.

Microsoft Azure Synapse Analytics's overall integration within the Azure ecosystem could improve. The native Microsoft solution versus another solution, such as Databricks, there are areas where there could be some improvements.

I have been using Microsoft Azure Synapse Analytics for approximately two years.

Microsoft Azure Synapse Analytics is stable.

The scalability of Microsoft Azure Synapse Analytics is very good.

We have hundreds of people using this solution.

Compared to the traditional data center approach, the cloud has pushed forward well for maintenance reduction. There is a 60 to 80 percent reduction.

My advice to others is this solution is not meant for small databases.

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

Our company includes 50 analysts who use the solution on a daily basis to predict customer behavior. 

The solution's best feature is its predictive analytics. 

Integration with other vendors has limitations and could be improved. 

Integration of process analytics would be a nice addition that allows us to analyze efficiency. 

I have been using the solution for six months. 

The solution is stable. 

The solution is a web data warehousing and analytical platform built for enterprise environments, so it is definitely scalable. 

Technical support is very responsive and knowledgeable. 

I rate support an eight out of ten. 

Positive

We do not use other solutions because we operate in a Microsoft environment that is not expected to go to AWS.

The initial setup is complex and I rate it a four out of ten. 

The solution was implemented through a third-party vendor team. 

The solution has helped to predict sales and focus attention on enhancements that have improved the bottom line. 

I rate the ROI a six out of ten. 

I don't know specifics about pricing but I hear that it is quite expensive. For example, Power BI is not as expensive as this solution.  

The solution has superior modeling and predictive analytical abilities in comparison to Power BI. There isn't really an expectation that you would get these superior abilities in Power BI because the solutions have different purposes and use cases. 

I rate the solution an eight out of ten. 

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 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.