Microsoft Azure Synapse Analytics Reviews

I primarily use Microsoft Azure Synapse Analytics for data pipelining.

I like the openness and flexibility of Synapse Analytics, which allows for integration of various data sources. It supports using Azure functions to connect data sources and can utilize Python and Jupyter Notebooks. 

The integration within Azure allows for efficient data handling, using data lakes, and storage accounts. 

Overall, the product provides a seamless and adaptable approach to data processing and analytics.

One area that could use improvement is the possibility for offline capabilities. If Microsoft Azure Synapse Analytics could run on a laptop as a Docker image, it would allow for work without needing an online connection.

I have been using Microsoft Azure Synapse Analytics for one year.

The stability of Microsoft Azure Synapse Analytics is very high. I rate it a ten out of ten.

In terms of scalability, I rate Microsoft Azure Synapse Analytics an eight out of ten. However, this rating is only a feeling as I have no experience with scaling this product.

The product is so good that I haven't had a need for technical support. Given my extensive experience in software development, I find the similarities between Azure and other Microsoft products beneficial.

Currently, I only work with Microsoft Azure as that's what my customer selected.

I learned everything myself and did the deployment without any external help.

The financial aspect, including pricing and cost reduction, is not something I focus on.

Overall, I would recommend Microsoft Azure Synapse Analytics to others. I'd rate the solution eight out of ten.

We are ingesting data from different sources and using Power BI for reporting and decision-making for analytics.

It helps centralize our data and create one source of truth, helping us become a data-driven organization.

The fact that we can ingest from different types of sources, whether they are internal systems or external sources. It also offers insights and proposes what we can use with that data.

I wish the data governance feature could be incorporated without requiring an additional license.

We have been working with it for two to three years.

The stability of Synapse Analytics is rated as ten. It is very stable.

The scalability of Synapse Analytics is also rated as ten. It is meant for big data and is scalable based on how much we want to use.

If they have allocated a resource, like a task team to assist, then they do a good job. However, if you log a general call, it will take longer. Dealing directly with the account manager is quicker.

Neutral

We used products from other vendors before, but now we primarily use Microsoft.

It took about twelve months because of the complexities related to ingesting data from different sources.

Microsoft offered us a FastTrack team that guided us. From our side, six engineers took part in the deployment.

At this point, I cannot put the financial benefit.

It is not a cheap solution. That said, it is reasonable compared to its peers.

Overall, Microsoft Azure Synapse Analytics offers good integration, is user-friendly for data analysts, and helps in centralizing data.

I'd rate the solution ten out of ten.

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.

Neutral

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.