Microsoft Azure Synapse Analytics Reviews

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.

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.

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. 

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.

We are using Microsoft Azure Synapse Analytics to find some evidence or outlier in our analysis about the application of the public health service and public money in the sector in Brazil. We are a public institution that is responsible for auditing other public institutions. We use these kinds of tools to find some evidence and outliers to audit with a little bit more documents or evidence.

The most valuable features of Microsoft Azure Synapse Analytics are the user experience because it is easy to make analyses, load different databases, and different resources. Our end users can do the analysis by themselves without IT supervising. This is a great aspect of the solution, it's better for our environment.

The interface of the solution is good.

Microsoft Azure Synapse Analytics can improve by increasing the size of the files that we can load on the platform. We have some files that are too large to be loaded and it would be a benefit to us if the limit was increased. Additionally, the way we use the tool for generating reports can be made better. They should add some drag-and-drop rules without the need of programming these rules using some programming language. It would be helpful if we did not need someone that was technically advanced to be able to do it with, such as someone with no IT background. Having a reporting tool without code would be great.

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

We're using this solution for general data integration. We are customers of Microsoft Azure Synapse Analytics and I'm a data specialist. 

I like the solution because it's very interactive and provides flexibility. It's mostly a drag-and-drop procedure. It also provides a lot of design choices.

We've only recently starting testing this solution and it would be helpful if the documentation was better or if the company could provide some kind of presentation to help us understand how the tool works and what's required to optimize use of the solution.

We've been testing this solution for a couple of months. 

I haven't seen any issues with stability or scalability. We have an infrastructure team of around 40 people. 

The company chose Synapse Analytics because the price is fair and I understand it's cheaper than Snowflake.

We're also hoping to evaluate Snowflake which is an advanced version of both Azure and AWS together. Snowflake has advantages, especially in terms of scalability which can also be implemented separately at the storage level and also at the computer level. 

I think Synapse Analytics is definitely better than the traditional high solution data solutions that we've previously used. It's definitely worth considering and I haven't come across anything negative, only advantages for now.

I would rate this solution an eight out of 10. 

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.

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.