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. 

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.

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.

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.