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.

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.

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.

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.