Microsoft Azure Synapse Analytics Reviews

I deal with Microsoft Azure Synapse Analytics, Log Analytics, which is a centralized log management solution for Azure itself from where all the devices are collecting the logs, Azure Monitor, and the complete governance of Azure, Azure Backup, Azure Site Recovery, Kubernetes, and some parts of Azure DevOps. I also work with Azure Firewall. I work with most infrastructure services and platform as services such as Azure SQL Managed Instances or Azure SQL DB, CosmosDB, and PostgreSQL.

I usually recommend Microsoft Azure Synapse Analytics to companies who have a minimum of 20 VMs plus on Azure. This makes sense because there is a significant cost associated with Azure.

I would rate their technical support performance as nine out of ten.

The features I find most valuable in Microsoft Azure Synapse Analytics are comprehensive because they have combined almost five to six components into one single bundle. This is interesting because earlier these were separate services, but now they are all together in one bundle. The deployment and operational parts are more convenient and easy.

The integration capabilities of Microsoft Azure Synapse Analytics are extensive. Synapse Analytics has multiple components, including Data Factory, which was previously Azure Data Factory, and Data Warehouse Management, which was formerly SQL Data Warehouse. From an integration perspective, Synapse Analytics has APIs that can easily integrate. Databricks is supported by default. For integrating other third-party solutions for reporting, AI, or machine learning, the integration process is straightforward. Microsoft provides numerous connectors for integration. Even if default connectors are not available, they provide APIs to build customized solutions and integrate custom apps.

Microsoft Azure Synapse Analytics could improve their pricing structure. They currently have two separate pricing tiers: full log and partial log. I suggest they implement a single price and reduce charges on logs. They could implement a cap with some free tier or price tier based on the GBs of log collected. This would help customers use Azure more effectively. Microsoft Azure Synapse Analytics is an excellent product because it includes both SIEM and orchestration capabilities with playbooks, but the log management part is very costly.

Regarding future improvements, I would appreciate more application connectors for integration, particularly for on-premise based solutions. Many customers still maintain on-premise infrastructure, so having more integration possibilities for these environments would be beneficial.

I have been working with Microsoft Azure Synapse Analytics for almost five years.

Performance and stability are absolutely fine because Microsoft Azure Synapse Analytics is a PaaS service. It is not the customer's responsibility to manage performance as it is the vendor's responsibility. It is quite a stable product.

I don't have experience with other products as I am a Microsoft Partner. I am a system integrator who proposes solutions to customers.

I would generally recommend Microsoft Azure Synapse Analytics to others.

I work as an implementer of Amazon and Microsoft Azure. In Microsoft Azure, I work with all their products. For third-party products, I work with firewall solutions from Palo Alto, Fortinet, and Cisco. I also work with Fortinet WAF solution. I use products from CodeTwo for email security, email signature solution, email backup solution, and MSP360. As a principal solution architect, I use numerous products daily and have developed my own products for cloud platform: cloud resiliency and cybersecurity.

I have over 13 years of experience in cloud field and more than 15 years of overall experience. I started my career as a network architect with knowledge of networking, switching, routing, and server administration. For the past 13 years, I have been working exclusively with cloud environments.

Native data lake support in Microsoft Azure Synapse Analytics is beneficial because the native components are tightly integrated. Some customers have different scenarios with existing Hadoop databases or third-party data lake solutions but want the front-end engine, connector part, transformation part, and AI part. If customers don't have pre-existing solutions, the native data lake is absolutely fine and recommended.

The effectiveness of Microsoft Azure Synapse Analytics's built-in data integration capabilities for ETL processes depends on proper configuration and optimization. The transformation process requires optimizing the solution, selecting appropriate fields, and drawing the transformation flow. Microsoft provides the technology, but effectiveness comes from proper configuration and defining appropriate transformation rules.

I rate this solution 8 out of 10.

We are from the software development wing where we basically program different products of Microsoft and then deliver the solution.

We work with Microsoft Azure Synapse Analytics.

As a consultant and partner of Microsoft, not as a user, we were building data warehouses and integrated data warehouse on Microsoft Azure Synapse Analytics.

The ability to handle large data stands out for Microsoft Azure Synapse Analytics. As a services delivery provider, we have moved on from Microsoft Azure Synapse Analytics and now we are embracing Fabric, and we suggest Fabric to all our prospective customers.

In Microsoft Azure Synapse Analytics, if you look at the overall cost, Fabric is much better compared to Microsoft Azure Synapse Analytics because you can properly separate out data and compute. You have flexibility to change the workloads and different options for different environments.

We use ADF for data integration, and as part of Microsoft Azure Synapse Analytics Studio, we use ADF pipelines. Currently, we are using mostly Fabric flows which are very effective integration tools.

The main areas for improvement are cost and integration with different toolsets within one package. It has shared storage across multiple components of Fabric, which is a unique feature compared to Snowflake or any other solution.

We have used the solution for roughly around three years.

We have never integrated Microsoft Azure Synapse Analytics with Databricks, but we have mostly pulled data from on-premises systems into Azure Databricks. We used MLflow to build and deploy ML models, but not necessarily from Microsoft Azure Synapse Analytics to Databricks integration.

I don't see much scalability issues.

We have used Snowflake and Redshift. I cannot compare Microsoft Azure Synapse Analytics with Snowflake, but Snowflake has some advantages. However, when comparing Fabric versus Snowflake, Fabric wins.

Pricing only.

Our IT wing takes care of testing, as I deal only with development and the data part.

I use Microsoft Azure Synapse Analytics for data warehousing, and in Fabric, we create Fabric data warehouse. I have not heard about Parallel Data Warehouse.

We also use Azure SQL and Power BI.

On a scale of 1-10, I rate Microsoft Azure Synapse Analytics a 9 out of 10.

I use Mendix software to integrate Microsoft Azure Synapse Analytics with Azure Databricks and other products. I assess the effectiveness of Synapse's built-in data integration capabilities for ETL processes as very convenient, and I also use that to do the interface within SAP.

What I actually appreciate in Microsoft Azure Synapse Analytics is that it is easy and intuitive, providing a straightforward configuration experience for my purposes.

Microsoft Azure Synapse Analytics impacts my workflow as I track and integrate data, and by using this tool, I ask engineering to cooperate together.

Microsoft Azure Synapse Analytics helps me drive data-driven insights by supporting data ownership because I need to support the company in places such as Taiwan, the US, and Mexico, and I need to integrate all of the data owners, as the local regions require different customizations for each individual.

I think Microsoft Azure Synapse Analytics' native data lake support is beneficial; I also use Microsoft support in Hong Kong.

I am hoping that in the future Microsoft Azure Synapse Analytics may have AI support.

I have been using Microsoft Azure Synapse Analytics for about three years.

Someone helped me to deploy Microsoft Azure Synapse Analytics, and that involved three or four people.

I think the licensing cost for Microsoft Azure Synapse Analytics is very high, but I have a local system to support that, so I don't consider that in the near future.

Microsoft Azure Synapse Analytics is easy to use, and it is very easy to deploy. I spend one hour per day on deployment of Microsoft Azure Synapse Analytics. I don't use the Microsoft Azure Synapse Analytics on-demand SQL query feature. I would rate this product an 8 overall.

My customers' main use cases for Microsoft Azure Synapse Analytics included building a number of data lakes. They were using Microsoft Azure Synapse Analytics or components within it to do data orchestration, build data warehouses, and create reporting using Power BI.

One of the most valuable features in Microsoft Azure Synapse Analytics is the ability to write your own ETL code using Azure Data Factory, which is a component within Synapse. Additionally, the capability to perform reporting and create Power BI reports directly within Microsoft Azure Synapse Analytics environment is also very useful.

I have worked with the Azure Synapse On-demand SQL query feature, and it is good if the amount of workloads you have is manageable. Being on-demand, Microsoft Azure Synapse instance only starts up when I write queries against it, which is beneficial for cutting costs. There is an initial performance hit because it usually takes a few minutes for the on-demand system to be provisioned whenever I write a query, but the cost-saving is significant. Microsoft is pushing everyone to use their new data analytics environment in Fabric while still having Azure Synapse for legacy workloads.

The built-in data integration for ETL processes in Microsoft Azure Synapse Analytics is good, but it depends on the use case for what I would use it. The ETL toolset within Microsoft Azure Synapse Analytics is robust with many features. Initially, my preference was Databricks for building data lakes due to certain issues around runtime values that were not up to date within Microsoft Azure Synapse Analytics. Both Databricks and Microsoft Azure Synapse Analytics run off a runtime engine defined at build time, but Databricks had more recent runtime engines and better built-in features and security.

There are a few challenges to be aware of with Microsoft Azure Synapse Analytics, as having a Synapse environment is not cheap. The scalability of Microsoft Azure Synapse Analytics is a concern, depending on the size of the data warehouse to be built. For instance, while working for a major enterprise, we noticed that there was no way to scale concurrent querying in too much detail, as all jobs get submitted to Microsoft Azure Synapse Analytics in sequence.

I recommend that one of the improvements for Azure Synapse Analytics should focus on allowing for multiple concurrent queries to get back data at scale. Additionally, the engine that runs the notebook queries should be updated to match what Databricks provides, achieving some feature parity.

I have been working with it for at least a couple of years, if not more.

The scalability of Microsoft Azure Synapse Analytics is a concern, depending on the size of the data warehouse to be built. For instance, while working for a major enterprise, we noticed that there was no way to scale concurrent querying in too much detail, as all jobs get submitted to Microsoft Azure Synapse Analytics in sequence.

My impression of the support provided by Microsoft for Microsoft Azure Synapse Analytics is mixed, but by and large, I believe they did a good job in helping us troubleshoot problems.

I would rate the support for Microsoft Azure Synapse Analytics as an eight out of ten.

Positive

It is straightforward enough to set up Microsoft Azure Synapse Analytics, provided one has some Azure knowledge. However, the effort to implement the data warehouse and load data is considerable, requiring extensive prep work in terms of ETL, data quality, reporting, and management.

Some of my customers have indeed seen a return on investment with Microsoft Azure Synapse Analytics as they used it for analytics to drive decision-making, improving their processes or increasing revenue.

Azure Synapse Analytics operates on a usage-based product model rather than being license-based. The cheapest tier costs about $4,000 to $4,700 a year, while the most expensive tier can reach up to $300,000 a year. For small to medium enterprises, I would not recommend Microsoft Azure Synapse Analytics due to its cost and would advise them to look into SQL Server until their capacity justifies a move to Synapse.

Microsoft Azure Synapse Analytics is mainly suitable for medium to large enterprises, particularly those with substantial data needs.

Microsoft Azure Synapse Analytics is a standalone product, so if I'm writing ETL code using Data Factory for example, it's its own unique product. Azure Databricks plays a major role in ETL, but I cannot write Azure Databricks code specifically within Microsoft Azure Synapse Analytics. However, I can write notebook commands within Microsoft Azure Synapse Analytics. It has multiple features, including SQL and Data Lake which includes Databricks and notebooks. From a product perspective, I find that Databricks has more robust features for data lakes compared to Microsoft Azure Synapse Analytics.

I recommend that those considering Microsoft Azure Synapse Analytics analyze their workloads to determine if it is a right fit for their business. They should also explore if Azure Fabric could be a more suitable option based on their budget and the potential return on investment.

Overall, I would rate Microsoft Azure Synapse Analytics as an eight out of ten as a solution. It is a useful product, but the power of Microsoft Azure Synapse Analytics stems from the effort exerted to get data into the environment, which ultimately determines the return it provides.

My use case for Microsoft Azure Synapse Analytics is because it's embedded in the Azure Cloud, and as a consequence, if you decide to work with Azure Cloud as my company does, this is the best choice because it's fully integrated with all the Azure Cloud tools.

For Microsoft Azure Synapse Analytics, the integration is the most valuable feature, meaning that whatever you need is fast and easy to use. There is no specific feature or tool that stands out as better than others, but because it's fully integrated, it results in very easy and efficient usage.

The initial setup of Microsoft Azure Synapse Analytics is not complex because it's fully integrated.

If we make a comparison between Microsoft Azure Synapse Analytics and Databricks, though they're not the same, many capabilities are overlapping. Databricks is a very rich solution, with numerous open sources and capabilities in terms of extract, transform, load, database query, and so forth. It has very powerful programming languages that Microsoft Azure Synapse Analytics doesn't have. From my point of view, Microsoft Azure Synapse Analytics is very well integrated and easy to use, but it remains a collection of capabilities.

Databricks makes me feel more comfortable in terms of integration between different pieces composing the solution, but that's only my opinion.

As IT Director, I have more than four years of experience working with Microsoft Azure Synapse Analytics.

I rate the stability of Microsoft Azure Synapse Analytics as acceptable; it's good enough, but not very high.

For the scalability of Microsoft Azure Synapse Analytics, I would rate it a 10 until you remain in the Azure Cloud scalability framework. Outside is a different discussion; if you have a hybrid cloud solution, we should open another discussion for this.

When it comes to technical support from Microsoft, I am a user and when I have a problem, I open tickets to our operations team who manage it. Considering the speed they solve the problem, I think it is acceptable, but not at the required level. For very deep and high-impact problems, they react. However, for problems that they consider not so important, such as Power BI not working or Microsoft Azure Synapse Analytics not working, they are not so reactive.

This is an underestimation of the real impact because we use big data also to monitor the network and the customer. Not monitoring data results often in a big impact on customer services and customer perception. I rate it an eight for cloud virtual instances infrastructure problems and a six minus when we come to big data tools that they offer as cloud services.

Positive

Before using Microsoft Azure Synapse Analytics, I used many standalone, open-source solutions. I switched from those tools to Microsoft Azure Synapse Analytics because when we moved to the cloud infrastructure and selected Azure, it was natural to consider Databricks and Microsoft Azure Synapse Analytics as solutions along with Power BI.

The initial setup of Microsoft Azure Synapse Analytics is not complex because it's fully integrated.

I think the price of Microsoft Azure Synapse Analytics is very expensive, but that's not only for Microsoft Azure Synapse Analytics—it's for the cloud in general. The cloud is not cost-effective. It is very cost-effective if you need to speed up the first use of the solution because you want to test if the market is welcoming to these solutions. You can experiment very quickly with new solutions and develop new prototypes. It's very aligned to an agile way of developing new things. However, when you scale the solution, the cloud doesn't work anymore in terms of cost.

I would recommend Microsoft Azure Synapse Analytics to others, but they are forced to use it; it's not a free choice. If you go for Microsoft Cloud, you are forced to use Microsoft Azure Synapse Analytics. To explain further, if we compare Microsoft Azure Synapse Analytics with Databricks in Microsoft Cloud, Databricks is an open-source solution that Microsoft took inside the Microsoft Cloud farm, and this is the first option. The second option is Microsoft Azure Synapse Analytics.

When you compare both of them, if you don't need a very good development environment that Databricks offers, after making a back-to-back capability comparison, you see that when you mount Power BI on top of Databricks and the amount of data is huge, you are not able to work because it's too slow. So you are forced to put Microsoft Azure Synapse Analytics on top, and the solution is very fast.

Microsoft has optimized the connector only for Microsoft Azure Synapse Analytics and not for Databricks. For that reason, you have no option; it's not because it's better. I believe that Databricks is much better than Microsoft Azure Synapse Analytics, but because the connectors are not optimized and because Databricks is not so well integrated as Microsoft Azure Synapse Analytics, at the end, you have the perception that Microsoft Azure Synapse Analytics is much better.

There is no reason to use Microsoft Azure Synapse Analytics and to pay so much for it, but because of the integration of Microsoft Azure Synapse Analytics in the cloud since it's a Microsoft native tool, you can't say the same for Databricks, which is an open-source tool that Microsoft took inside.

I would rate Microsoft Azure Synapse Analytics overall as a seven; it's not the best.

I have been working in the data engineering field for some time now, specifically in relation to Microsoft Azure Synapse Analytics.

In my organization, Hexaware, we deploy multiple ETL pipelines using Microsoft Azure Synapse Analytics for our clients.

When building the ETL pipeline with Microsoft Azure Synapse Analytics, it helps make the build process much easier.

The best feature of Microsoft Azure Synapse Analytics is the notebook functionality; it provides a very good interface, and we can easily do our experiments, POCs, and check things before migration or deployment to higher environments such as from development to SIT and then production. The deployment process is also much smoother.

Driving data-driven insights in my organization depends upon the preparation of the data. If your data is not good, then insights won't be good, and Microsoft Azure Synapse Analytics helps in preparing your data, so it is very helpful.

I find Microsoft Azure Synapse Analytics' native data lake support beneficial.

I think data ingestion is a weak point in Microsoft Azure Synapse Analytics currently; it is something that we have to do with ADF, preferably. If it becomes part of Microsoft Azure Synapse Analytics, then it will be much easier.

The reasons I don't rate Microsoft Azure Synapse Analytics higher include data integration and tech support being two main issues, and sometimes there are complicated scenarios with scalability. Scalability is a little laggy or jittery. Sometimes, when jobs are running in production, it might show some faults, and recovering from such scenarios becomes a bit problematic or time-consuming.

I have been using Microsoft Azure Synapse Analytics for three years.

There are complicated scenarios with scalability where it becomes a little laggy or jittery. When jobs are running in production, it might show some faults, and recovering from such scenarios becomes a bit problematic or time-consuming.

I would say the technical support for Microsoft Azure Synapse Analytics rates around six; they are friendly but there is a gap in knowledge, which makes it a little difficult to deal with.

Neutral

The deployment of Microsoft Azure Synapse Analytics is straightforward and, with time, it has evolved and become much more user-friendly.

I did take part in the deployment process of Microsoft Azure Synapse Analytics.

Deployment of Microsoft Azure Synapse Analytics actually involved three members.

In most of the clients using Microsoft Azure Synapse Analytics, they are satisfied, so the ROI is better.

I find the pricing of Microsoft Azure Synapse Analytics reasonable; compared to AWS Glue or Databricks, it is reasonable.

Before using Microsoft Azure Synapse Analytics, I worked with AWS Glue and also used Databricks notebooks.

I am a service provider, so I didn't switch from AWS Glue to Microsoft Azure Synapse Analytics.

I have not worked with Azure Stream Analytics, which is for real-time data handling.

I do not use Microsoft Azure Synapse Analytics' on-demand SQL query feature.

For machine learning operations, we use Azure Databricks features, and it is very helpful.

On a scale of one to ten, I rate Microsoft Azure Synapse Analytics a seven.

In my organization, different people utilize Microsoft Azure Synapse Analytics in various ways. Personally, I am assisting others but have not engaged much in deep analysis with it.

Microsoft Azure Synapse Analytics offers significant visibility, which helps us understand our usage more clearly. It allows us to effectively manage costs and utilize a variety of features efficiently.

There is a need for better documentation, particularly for customized tasks with Microsoft Azure Synapse Analytics. The documentation can be difficult to understand. Additionally, technical support has room for improvement. Many support staff lack the necessary skills to assist with our customized requests. There is a need for more expertise among the support team to guide us effectively.

I have been working with Microsoft Azure Synapse Analytics for almost two years.

I find the service stable as I have not encountered many issues. However, I have not engaged in advanced functionalities yet. Our organization is planning to explore more advanced options soon.

Microsoft Azure Synapse Analytics is scalable, offering numerous opportunities for scalability. I would rate its scalability around eight on a scale of one to ten.

Customer service and support for Microsoft Azure Synapse Analytics need improvement. They are slow to respond and not very knowledgeable. There is a lack of proactive actions, and we often have to figure out solutions ourselves. An expert team is needed to provide better guidance.

Neutral

Our company works on the Azure platform exclusively, and we are not utilizing multi-cloud services.

The initial setup can be complex, especially due to documentation gaps. Deployment typically takes a day with a team of four to five engineers.

We handle maintenance for Microsoft Azure Synapse Analytics both in-house and with third-party services. Maintenance is conducted quarterly.

Since implementing Microsoft Azure Synapse Analytics, we observe a gradual reduction in costs. At present, we achieve ten to fifteen percent savings.

We use an enterprise-level subscription for Microsoft Azure Synapse Analytics. I would rate our satisfaction with the price around five, as I have not focused much on cost aspects presently.

I recommend Microsoft Azure Synapse Analytics due to its visibility, cost management, and ease of utilization of various features. I rate it around seven on a scale of one to ten.

We run our finance and supply chain jobs using SQL BW, and we visualize our data loads on Power BI. We are using SQL Data Warehouse for data warehousing.

In terms of cost savings, we don't need specialization in skills as advanced as Teradata. We used to pay extensive licensing fees for Teradata, which we no longer pay. Azure Synapse has provided scalability and ease of use, especially when looking at performance tuning and optimization.

The biggest benefits are scalability and ease of use when looking at performance tuning and optimization. SQL Data Warehouse is very scalable. Microsoft Fabric brings a lot of value.

We will start looking at Microsoft Fabric for further improvement as it brings more value. Also, managing workloads and data transformation using Databricks should be a focus.

I have been using SQL Data Warehouse for the last three years. We completed our final migration to SQL Data Warehouse last October or November.

We haven’t faced many issues. Stability has been great, so I would rate it about a nine out of ten.

I would rate the scalability of the solution ten out of ten. It is very scalable.

They have been very responsive. We have a company in South Africa that reaches out to us, and if they can't resolve the issue, they hand us off to Microsoft. Customer support's responsiveness and quality are rated as a seven.

Neutral

We moved away from Teradata about a year ago. We used to pay extensive licensing fees for Teradata.

The setup was very easy. I would rate it as a ten out of ten for ease of setup.

We have seen a return on investment because it’s not as specialized as Teradata, and we no longer pay extensive licensing fees.

The cost is reasonable for our company. There is no license cost; we pay only for Azure Compute's costs. It is important to manage the cost efficiently on a daily basis.

Definitely, it's the right approach. Always monitor your costs and optimize your database's performance.

I'd rate the solution eight out of ten.

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.

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.