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.

Positive

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.

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. 

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.

We use the product to transform the data from the on-premise environment to the cloud. It is a flexible platform and integrates with multiple data sources.

The platform has multiple valuable use cases. They include performance, compatibility, flexibility, and cost.

Microsoft Azure Synapse Analytics's pricing could be reduced.

We have been using Microsoft Azure Synapse Analytics for five years.

I rate the platform's stability a seven or eight out of ten. It could be improved.

We manage four to five Microsoft Azure Synapse Analytics customers in our environment. It is a scalable software.

The initial setup is easy using the infrastructure code. The deployment takes a maximum of a day to complete as we conduct a POC enrollment before the setup. It helps us understand the process. Later, we develop it in the production environment.

We need assistance from a third-party vendor for product implementation.

I am evaluating Oracle GoldenGate. However, it is a licensed software that induces extra costs. Instead, I want to utilize Microsoft cloud services to their full potential and avoid additional licensing costs. By doing so, I aim to reduce reliance on Oracle software throughout the migration journey, opting for cloud-native solutions that align with the goals of cost-effectiveness.

Many customers prefer using Microsoft Azure Synapse Analytics. It allows them to develop applications in Java and .NET. It provides security, flexibility, and efficient UI as well. It enables the ease of configuring APIs and overall configurability. As a certified cloud specialist, I advise others to consider use cases and requirements while purchasing.

I rate the product an eight 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 Synapse to build views for our data warehouse. It is the place where we get our reports and views. We ingest data into our SQL Servers and Azure SQL, then build all our reporting views within Synapse.

I'm the cloud architect, and I have a two-person DevOps team plus another enterprise architect. Five people are using the interface at any given time. 

Synapse makes it easy to integrate and onboard data from other Microsoft and Azure sources. The interface is familiar because we were using Azure Data Factory before Synapse. It made the transition even easier because the Synapse interface is exactly the same. 

Cost is always the primary pain point. However, we've gotten to the stage where we can set aside finding, and the price is reasonable for our project. 

We have used Synapse Analytics for about a year.

I rate Synapse nine out of 10 for stability. We've had some issues in the past that needed to be addressed, but those were related to how we did our transformations. We needed to learn from our mistakes and figure out how to properly schedule jobs, imports, etc. 

I rate Synapse 10 out of 10 for scalability. Scaling up Synapse is quick and easy. That's one thing I appreciate the most about it. You can scale with a click 

I rate Microsoft support 10 out of 10. We have direct professional support, and they always come through for you if you run into any issues. Our tickets are never open for more than two days. 

Positive

We didn't have anything in place because we implemented Synapse for a specific project. We formulated our use case, considered our options, and decided on Synapse because ware primarily an Azure shop. Using Synapse made sense because it's easier to manage and support. 

I rate Synapse Analytics eight out of 10 for ease of setup. I've set up Synapse so many times in various environments that it's seamless. I have no issues with the product at this point.

We did extensive research and a POC before implementing Synapse Analytics. It took us a couple of days to deploy, including the POC. We had to separate our groups and stuff like that. The second step was to leverage templates for creating the environment. Everything is automated now. If want to do this again, I don't have to go to the portal. I can run it from a template. 

I rate Synapse Analytics seven out of 10. We had to learn a lot before we could get costs under control and budget appropriately. There were a lot of unknowns initially. 

I rate Azure Synapse Analytics eight out of 10. I would recommend Synapse depending on the needs you're trying to address. You need to properly build out your users and see that the technology is going to address your use case. After that, you can make an informed decision about what's best for your company. 

Companies come in all shapes and sizes. Synapse may not be a good fit for one company, but it can work well for others. Try it out first and see if it's appropriate for you.

We are using the solution to process a large volume of data. We are taking advantage of the MTPP functionality feature in Azure Synapse Analytics and connecting our reporting dashboard to its dedicated support tool. These are the two primary use cases we are currently leveraging.

Generally, people have differing opinions on whether to use Azure Synapse Analytics or Azure Databricks. These are two different services from Microsoft, and customers often struggle to decide which is best suited for their particular use case. Both services have some features in common and some that are exclusive to one or the other. It is a trade-off and ultimately depends on the use case that best serves the organization. Incorporating the features of both Azure Synapse Analytics and Azure Databricks would meet most use-case scenarios.

The cost of the solution has room for improvement.

Azure Database has a very good Unity Catalog functionality, which is a form of governance within Azure Databricks. Similarly, for Azure Analytics, having a governance product would be great. However, Microsoft already has Azure Purview, which is meant for governance, but it would be nice to have that functionality built into Azure Synapse Analytics.

I have been using the solution for seven years.

The solution is stable.

The solution is scalable. Generally, the development team of around 20 people uses Azure Synapse Analytics directly. Otherwise, a Power BI dashboard accesses the Azure Synapse Analytics database, and the end user uses the Power BI reports, not being directly exposed to Azure Synapse Analytics. Only the development and software development teams use Azure Synapse Analytics.

We are always in contact with the Microsoft team. We keep up to date with our Microsoft reference architecture and other related matters through our communication with the Microsoft team members.

Positive

The initial setup is straightforward. We can have the dedicated SQL up and running within 15 minutes.

The implementation is completed in-house.

The cost of the solution depends on the type of license we choose, such as pay-as-you-go, one-year reserve, or three-year reserve. The cost varies depending on the options selected and can be very expensive.

I give the solution a nine out of ten.

Depending on the organization's use case, there are multiple options available. For example, if the workload is medium, there are different products available such as Azure SQL Database for small or medium-sized data, and Azure Synapse Analytics for extremely large volumes of data. Ultimately, it is the use case of the organization that will determine the need for these particular services.