Microsoft Azure Synapse Analytics Reviews

We are service providers for our clients.

My clients primarily come from the enterprise segments, such as manufacturing, retail, pharmaceutical, and industrial. Most are supported by a large ERP solution like SAP.

Some of our clients are mid-size and others are large enterprise companies, some of the largest in India.

Some of the most valuable features would include Familiarity for practitioners like us and also familiarity for the businesses. SQL servers have been around for several decades and there are many who are familiar with SQL servers, such as SSIS and SSRS.

They are available on the Cloud, and the platform is very intuitive.

It integrates very well with Power BI, which I would rate as the best self-service BI. All the data warehouses eventually end up in BI or Machine Learning. 

Instant Apps that are more integrated rather than desperate components. You need to get different components engineered together, and with SQL everything comes together.

Instant Apps is a combination of your Spark compute platform, your SQL data platform, and your object storage. It's a platform that is integrated all together. 

It's easy to work with a faster turnaround time on my project deliveries.

Also, the integration with Power BI self-service is much, much easier and familiar.

You can have several SQL servers, they have large .NET artifacts that combine various components together very well.

What I would like to see more and more of in Azure is its support for IoT and streaming media information.

It needs strong support for social media, internet data, and native support for NoSQL. At this time, it works very well with the structured data

Stability could be improved.

Technical support is very good, but could also be improved.

My company has been working with SQL Data Warehouse for approximately three and a half years, and I have been working with this solution for one and a half years.

In the last six months, there have been a couple of incidents. We have experienced som outages. One of our clients was in a blackout for a couple of hours. Most of the clients in India have experienced outages also. They were restored, so I refer to them as glitches.

In principle, it's a limitless, serverless solution. As long as your region has resources, you can go on expanding. We have not had any problems with scalability.

Microsoft Azure comes integrated with Databricks as a Machine Learning platform, it's highly scalable.

Technicals support engages very well. We have account managers in place who correspond with technical support. 

The technical support team has a good relationship with our Account managers.

If we have an issue, we advise our account managers and present the ticket, they contact Technical support and resolve the issue very quickly.

If however, I have to contact Microsoft technical support at the backend, they are not very knowledgable. They have a good understanding of their products, but most of the time they struggle with that.

The initial setup is easy. The learning curve for Microsoft platforms is very low. An engineer with very little experience would be able to complete the initial set up with ease.

It has a quick turnaround time. We have completed the deployment in weeks.

For a BI project scheduled for six months, within the first month we would have completed the data warehouse installation, and the remaining five months would be for building the content, the data pipelines from the sources, the semantic models, and the BI.

When you compare it with other solutions that take months to get your sandbox running or to get your development running.

They are cost aggressive, and it integrates well with other Microsoft tools. For example Downstream and Teams.

We use the serverless platform as a service, that is only available with Microsoft Azure.

You don't have to hire experienced engineers to set up and implement the solution, saving you money, because it is very intuitive and the turnaround time is very low compared to other data warehouses. I would put it in par with solutions such as Redshift and BigQuery.

It's very modern, easy, and intuitive.

Most of the services are cheaper than other vendors.

This definitely falls into one of the top three or top four solutions and platforms in the world. I would definitely recommend it. If someone should ask me if they should use Microsoft Azure SQL Data Warehouse, I would say yes.

Personally, I don't see one great solution that exceeds in all aspects. Every solution has some good points and I think they have very beautifully found their strength and they're building upon it.

When asked what would best suit me, I would assess their environment requirements and could easily recommend any of the top four, being AWS Redshift, Google, Big Query, and Microsoft Azure.

I would rate this solution an eight out of ten.

There are around 10 people using this solution within our organization and we have plans to increase the usage. 

Microsoft Azure uses the Massively Parallel Processing concept for data warehousing. The speed is great and the architecture is excellent. 

Microsoft should develop an interface to make it easier to shift from on-premise to the cloud.

There needs to be a lift and shift tool to move on-premise, IMS high-volume data to synopsis sequence data on the cloud.

I have been using this solution for two years.

The stability is okay, but it could be improved. I believe Microsoft is working on fixing this at the moment.

We are satisfied with the technical support.

We used to use an AWF product, but Microsoft Azure is a better cloud-based solution compared to AWF.

Our license is very expensive.

I would recommend this solution to other enterprises.

On a scale from one to ten, I would give this solution a rating of seven.

The primary use case is for the fast retrieval of data, which is in the data warehouse on-premises and migrating it to the cloud.

The most valuable feature is performance gains.

The scalable nature of the data warehouse on the cloud, and the flexibility that you get, and important features.

I would like to see them provide the ingestion of images.

I have been using this solution for more than three years.

It's a production that keeps changing and we are always using the latest version.

This solution is quite stable. It's in the cloud and we have not faced any issues.

This solution is scalable.

Technical support was not needed because of all of the documentation available.

The initial setup is straightforward.

All of the prices are available online.

My advice would be to move your on-premises data warehouse to the cloud. Move it there faster, as you can get more performance gains and it's also a robust infrastructure.

I would rate this solution an eight out of ten.

We primarily use this solution for source analytics and procurement.

The most valuable feature of the solution is the analytics and that it can connect with Power BI. I found it quite interesting and very compelling in terms of detail within a single stack.

I would like my team to be able to build pipelines that integrate with the Azure Data Factory. Some of the best-in-class ETL products that are available in the market, such as Informatica ETL, are far more mature than this solution.

We started implementing projects with Microsoft Azure SQL Data Warehouse about six months ago.

This is a stable product.

Scalability is good and we haven't had any issues.

We have approximately 150 users. We are stable at the moment but we will expand our usage as we get more data.

We have contacted Microsoft support twice, but I do not have much experience with them.

In addition to Azure SQL Data Warehouse, we work with the version that is on-premises.

We are also working with Oracle Data Warehouse and we plan to continue using both of these solutions.

The initial setup is straightforward.

We implement this solution for our customers.

If you are using Microsoft products, such as Office 365, then this solution is good because it can take the data from multiple sources. 

This is a good product, but when you compare to Oracle Data Warehouse there are differences in scalability, high availability, and implementation.

I would rate this solution a seven out of ten.

We are developing our SQL Data Warehouse locally, using SQL Server 2016 on our private cloud, and we will be migrating it to Azure once it is ready.

The company for which I am working deals with products in the leisure industry, such as museums and swimming pools. They use the data warehouse solution for big data reporting. Currently, we are developing a multi-platform data warehouse that can support the reporting requirements for all of the customers. Whatever analytics they want to perform will be done with the data warehouse, and not the SQL database application.

My role is to design the data warehouse, and once that is complete, we need to build the data pipelines so that the data can be moved from the application databases into the warehouse. Then we will design the OLAP servers and OLAP cubes, vertically, to give us a presentation layer on top of the data warehouse. This layer is what the customers will be using.

The most valuable feature is the incremental load because we do not need to refresh the entire data on a daily basis. We have a nightly load where all of the data is updated, and it is a very good feature.

The transaction log shipping gives us the ability to replicate the database onto different servers for backup and synchronization.

The major concern is that I have a hard time with having to version control the data warehouse all the time. As it is now, I have to open all of the ports and push everything onto the server. I would like to see version control implemented into the data warehouse. This would make the tool perfect. 

I have been using this Microsoft Azure SQL Data Warehouse for about two and a half years.

The version that we have been using, 2016, is stable. We have not used the 2019 version in production yet, but there is a team that is evaluating that version right now. I'm not sure of the reason for it not being in production, whether it is related to stability or not.

The scalability is good. I think we are already using a high availability cluster for 2016, so I assume that it will be the same with the 2019 version. We are also using it with our data recovery system, so it is not an issue.

We have approximately twelve to fifteen developers and approx. 900+ customers who will be using the data warehouse. We are acquiring customers on a monthly or quarterly basis, so it is a pretty big user base and we expect that our usage will continue to increase.

We don't have too many technical issues, so overall, I think that it's good enough.

I began with Microsoft SQL Server and have been on the Microsoft platform ever since.

The initial setup is pretty straightforward. It has been a long time since we first implemented this solution, but since that point, we have created a custom handbook that we can follow in order to set it up.

There are certain design concepts that can be used to design a data warehouse. My advice to anybody who is implementing this solution is to first study the user's needs, and then go with that approach. The design will be specific to the needs.

I would rate this solution an eight out of ten.

We are a software development company and this is one of the solutions that we recommend to our clients. The applications that we develop make use of this technology.

Using Azure SQL Data Warehouse means that you don't have to worry about big servers.

The best part is the easy and user-friendly administration and handling of the data.

The configuration for things like high-availability could be more user-friendly for non-technical users.

We have been using Azure SQL Data Warehouse for almost twelve years.

This is a stable solution with no issues.

Scalability is the best part of going with a cloud-based solution. I don't have to worry about server space. Azure elasticity allows us to scale as much as we want, and it is pay-as-you-go, so we can scale up as we need to. What we propose to the people who are implementing this solution is to start with the minimum requirements and then scale up based on demand.

We have not seen any problems with technical support. We have Microsoft certification, so we very rarely contact them for any help. Normally, we handle the support for our clients.

The initial setup is straightforward. The requirements are very clear, and there is not much of a problem when you're doing it.

One of the recent upgrades we have done that included high-availability, loading the data, and setting everything up, took about a month and a half.

The licensing fees for this solution are on a pay-per-use basis, and not very expensive.

The core strength of Microsoft technologies is ease of use and administration, regardless of the type of product.

This is a good solution to go with, the cost is reasonable, and I recommend it.

I would rate this solution an eight out of ten.

We haven't really implemented any SSIS. I haven't been in a project where it was implemented as part of the data we have been building.

The features we've found most valuable for data warehouses is extracting data, SSIS packages, and the DBs.

So far, with the current SQL that I'm using compared to what we've previously used, I'm happy with it. I wouldn't necessarily point out anything that needs improvement.

From my point of view from what we have implemented recently, I'm happy with the solution, I'm happy with the performance. I'm not entirely happy with the billing model. I'm not entirely happy with how the enterprise services are pretty expensive, but that's about it.

I have been using this solution for around 18 months.

It is very stable.

I think the architectural design is very scalable.

I never contacted Microsoft customer support. 

We do have access to it, but we haven't needed to.

I've used Tableau, I've used SQL DB, I've used SAP DB.

The initial setup wasn't complex. It was okay. It wasn't complex at all.

To set it up within full compliance with our cloud infrastructure team, I'd say maybe two weeks after beginning our testing. I'd say the team wasn't a hundred percent devoted to setting it up as they had other things to do. So I'd say I'll give it two weeks to be realistic for a large organization.

It depends on whether you have those resources internally or not. If you have internal resources you wouldn't need to outsource, if you don't have, you would. So it's not a matter of how good that application is but a question of whether you have the capability in house.

The enterprise services are pretty expensive.

We are a solution provider and we provide different tools for our clients. Microsoft Azure SQL Data Warehouse is one of the solutions that we have implemented.

The use cases vary depending on the client. We have a couple of clients who are moving their data from on-premises to Azure databases, and they are using this solution primarily for reporting analytics. They also use visualization tools on top of the reporting.

The main advantage of using this solution is its ability to scale and handle very large amounts of data, in the petabyte range.

When we are not using this solution we can simply shut it down saving us costs, which is a nice advantage.

With respect to what needs to be improved, concurrent connectivity has some limitations. 

We have been using Microsoft Azure SQL Data Warehouse for about one year.

This solution is stable.

We are satisfied with the scalability.

A lot of people use the analytics and I would estimate it to be between 50 and 100 users. Most of them are business users, although some are power users who built the system for them.

It is good technical support in general, we find. We were in contact with them when we were doing a migration from on-premises to the cloud, and it was very nice.

The initial setup is straightforward.

This solution is being changed from day-to-day. Now it has become Synapse Analytics and a lot of things are being added to it. I'm curious to see how that turns out. They have a lot of new integrations and features, including predictive analysis and big data integration. They may have solved the issues with concurrent connectivity, so I'm curious to put my hands on it.

The product is being upgraded and updated to support a lot of analytic features, and it is a great tool to use.

I would rate this solution a nine out of ten.