Microsoft Azure Cosmos DB Reviews

My company developed an anti-money laundering compliance platform specifically for the gaming industry. This multitenant platform utilizes Microsoft Azure Cosmos DB as its core operational database.

We have a high-throughput, large-volume data ingestion process, with substantial data flowing into our environment. We needed to solve two problems: ultra-high volume, requiring speedy reads and writes of hundreds of gigabytes of data per day, and the ability to distribute our platform geographically. Azure Cosmos DB's geo-replication features and the ability to host and scale our database across multiple regions, keeping data close to our customers, were primary deciding factors.

Azure Cosmos DB is quick to adopt with a shallow learning curve. The average user can be operational within hours or days, handling small to medium data volumes. However, optimizing for ultra-high throughput scenarios involves a steeper learning curve, requiring substantial knowledge to master Azure Cosmos DB. Nonetheless, most users can leverage it as their operational data store with minimal effort.

Our platform boasts several extensive language model features, particularly around summarization capabilities. We use vector searching in Azure Cosmos DB to facilitate the retrieval of an augmented generation model with our LLM implementation. It's a standard RAG implementation using Azure Cosmos DB. Compared to other options, a key advantage of vector indexing in Azure Cosmos DB is the ability to query documents alongside vectors. This pinpoints the precise information required for RAG in our LLM solution, granting us greater flexibility than vector searching in other Azure services.

We integrated the vector database with the Azure OpenAI service for our LLM solution.

The Azure AI services were simple to integrate with the vector database. There was a slight learning curve, especially as we were on the private preview of vector searching. This led to some hiccups with our existing database configurations, specifically regarding continuous backup. We couldn't enable continuous backup and vector searching simultaneously. However, this was solely due to our participation in the preview, and I'm confident this issue won't persist in the general availability release.

Azure Cosmos DB is fantastic for searching large amounts of data when the data is within a single partition. Over the last two weekends, we ingested over 400 gigabytes of data into our Azure Cosmos DB database and saw no change in querying performance compared to when our database was only 20 gigabytes in size. This is impressive and powerful, but the scope is limited to those partition queries.

The first benefit we've seen is increased developer productivity. Azure Cosmos DB is an easy database to work with. Its schema-less nature allows us to iterate quickly on our platform, develop new features, and store the associated data in Azure. Developers find it easy to use, eliminating the need for object-relational mapping tools and other overhead. Geographic replication and the ability to scale geographically is another advantage. This is challenging with other databases, even other NoSQL databases, but Azure Cosmos DB makes it easy. Cost optimization is a major benefit as well. We've been able to run our platform at a fraction of the infrastructure cost our customers incur when integrating with us. This allows us to focus resources on feature development and platform building rather than infrastructure maintenance.

Azure Cosmos DB helped reduce the total cost of ownership. We don't need DBAs, system administrators, or typical IT staff to run the infrastructure because we can use Azure Cosmos DB as a platform or a software-as-a-service data storage solution. This makes the total cost of ownership significantly lower than any comparable solution using relational databases or other NoSQL solutions like MongoDB.

We enable auto-scaling on all of our Azure Cosmos DB resources, which helps us achieve cost optimizations.

The querying language and the SDKs they've provided over the years have been phenomenal, giving us a significant advantage. Being a NoSQL database with a schema-less design allows us to optimize costs and reduce the infrastructure we need to manage. While we don't utilize every feature, auto-scaling has been invaluable for optimizing both cost and performance on our platform daily.

Azure Cosmos DB could be better for business intelligence and analytical queries. While it excels at high-throughput data ingestion and point reads with low latency, querying within partitions is smooth. Complex cross-partition querying, and BI/analytical tasks often necessitate moving data to other solutions like Fabric and Azure AI Search.

I have been using Microsoft Azure Cosmos DB for nine years.

Over the four years we have been building, Azure Cosmos DB has not caused us one second of downtime.

The latency and availability of the Azure Cosmos DB is excellent. We have single millisecond latency point reads, which is the majority of the queries that we run on our platform. So, it's an ultra-high performance, ultra-high throughput platform for managing data.

We are confident in Azure Cosmos DB's ability to scale and meet our needs even with massive data volumes. We ingest hundreds of gigabytes of data into Azure Cosmos DB daily, reinforcing our confidence in its scaling capabilities.

Our experience with technical support has always been great. We've been lucky enough to connect with the product team resources at Microsoft around Azure Cosmos DB, and we've received fantastic support from the Azure Cosmos DB team.

I've used MongoDB extensively, both self-hosted and through their SaaS solution, Mongo Atlas. I've also worked with relational databases like PostgreSQL, SQL Server, and MySQL. Additionally, I've experimented with non-document data stores like Cassandra, but not in a production environment. All the other databases have been used in deployed production applications.

Azure Cosmos DB offers a minimal total cost of ownership. No database administration or dedicated teams are needed to manage it, which increases developer productivity compared to relational database solutions. Azure Cosmos DB also provides higher levels of data consistency without the traditional data migration problems of relational databases, and its schema-less nature makes versioning easier. However, there is a steep learning curve for building large-scale applications and optimizing throughput with Azure Cosmos DB. Additionally, its analytical capabilities are not as strong as other solutions.

The deployment was straightforward to initiate. While numerous configuration items are needed for production workloads, particularly key management, getting started remains simple and accessible.

We deployed our infrastructure as code, initially using Azure CLI scripts to deploy AzureCosmos DB alongside other infrastructure components. We have since transitioned to Terraform for this purpose. From an implementation strategy perspective, we are a multi-tenant platform using logical tenancy, meaning a single Azure Cosmos DB database accommodates multiple tenants. We have a relatively large number of collections, approximately 40, within our Azure Cosmos DB database. To optimize cost, we share throughput where feasible and provide dedicated throughput for containers with high read or write volumes.

One person is enough for the deployment.

For the first three years of our company, we were able to run a production environment while spending less than $10,000 a month on our database. In contrast, our customers pay tens of thousands of dollars for the systems we integrate. Therefore, Azure Cosmos DB is a highly cost-optimized solution when used correctly.

I rate Microsoft Azure Cosmos DB ten out of ten.

We use Azure Cosmos DB extensively for searching alongside Azure AI Search, which offers full-text Lucene syntax-compatible querying. While a significant portion of our searches leverage these dedicated search indexes, we still conduct a fair amount directly in Azure Cosmos DB. Although it might not be entirely fair to say that searching isn't Azure Cosmos DB's strong suit, it's worth noting that its capabilities are constrained by partitioning requirements. This limitation places a ceiling on its overall effectiveness for specific scenarios. While Azure Cosmos DB can be extremely valuable for querying within partitions, alternative solutions are often better suited for queries spanning multiple partitions.

I've built tools around the Azure Cosmos DB SDKs to make them incredibly easy to use. My team had no learning curve and could leverage our shared libraries. It took me less than a week to achieve a production-quality implementation for accessing and saving data within a platform.

We have 20 people in the organization who interact with Azure Cosmos DB, consisting of 15 engineers and five others.

Azure Cosmos DB typically requires minimal maintenance, but if data partitioning is not done correctly, some overhead may be incurred due to the need to replicate containers and move data. Thus, while generally low maintenance, some maintenance can be required in certain situations.

For anyone thinking about implementing Azure Cosmos DB, first, understand your data and invest time in understanding the partitioning in Azure Cosmos DB. If you get your head wrapped around the partitioning, everything else will be straightforward.

Our primary use case for Cosmos DB is unstructured data. We utilize it to spin up databases quickly.

We previously used on-premises databases. Switching to the cloud significantly improved scalability, flexibility, and uptime. It also addressed our uptime issues and greatly benefited our organization. We've never had issues searching through any amount of data; it's more than capable of searching large amounts of data. 

The connectors, such as the MongoDB connector and the integration with SQL, are incredibly valuable.

There's a little bit of a learning curve because I was new to Azure. But once you learn the tool, it's pretty straightforward.

I have been using the solution for about three years now.

We haven't noticed any significant issues with latency, but we don't have many applications. The availability is excellent, and we have multiple availability zones, so nothing goes down. 

The solution can scale very well both up and down, although we predominantly work with smaller databases. We haven't needed extensive scalability yet, but it seems very capable.

We previously used MongoDB. The shift to Cosmos made sense as we wanted to move to the cloud and benefit from its MongoDB API connection.

The setup was straightforward. Team members could start using the tool within a few hours, although not at an expert level.

As far as I know, it's cheaper compared to running on-prem, although comparing costs exactly can be challenging.

I personally don't deal much with budgets, but our financial analyst hasn't raised any complaints. The pricing aligns well with budget expectations.

I would rate the product an eight or nine out of ten. We are very happy with it as it runs smoothly right out of the box.

I am building an extension app for DocuSign. One of the ways for me to demonstrate this is by using a third-party database. I read and write data from Cosmos DB using DocuSign tools.

We wanted to use Azure function apps and Cosmos DB because Cosmos is serverless and non-relational, so it's easy to set up and simple to scale up and down. Overall, it was a good fit. 

I like the way you can create and delete records. You pass a JSON, and then it creates a record. It is easy to use because you don't need to know much about Cosmos DB or have prior experience. 

Cosmos DB does a pretty good job of searching. I've never had trouble as long as I search for a unique key or value I'm looking for. If my query is right, it returns the value.

Once you create a database, it calls the container, and then items show up. A better description and more guidance would help because the first time I created it, I didn't understand that a container is similar to a table in SQL. 

I have been using it for six months.

My use case is like a proof of concept, so the data set is not extremely large, but I know from reading about it that it can scale up well. It should do a good job on large amounts of data. 

The initial setup was simple the first time I used Cosmos DB. It took just a few hours for my small technical team to get used to how Cosmos DB works.

The pricing model has aligned with our expectations. In Azure, setting it as consumption-based or serverless keeps the cost low, but we had instances where automation increased the cost significantly. It was more of a configuration problem, where options to keep it minimal are still present.

We wanted to go with Azure function apps and Cosmos DB to keep it serverless and non-relational, making it easy to set up, scale up, and down.

I rate the product as eight out of ten.

We develop SaaS applications for our products and external clients, utilizing Cosmos DB as the data storage layer for all semi-structured data.

We chose Cosmos DB because it addressed our need to store and query data with varying structures. While object storage is cost-effective for large datasets, it lacks querying capabilities. Traditional relational databases like SQL Server or Oracle are expensive and inflexible, posing challenges for our dynamic data models and frequent changes. Cosmos DB provided a solution with its dynamic data model and efficient querying capabilities, allowing us to accommodate diverse customer needs and evolving data structures.

Our focus is primarily on data filtering and querying rather than extensive text-based searches. While we previously utilized alternative products, we now predominantly rely on Cosmos DB for these tasks due to its ease of use and management. This allows us to quickly onboard customers, especially startups and smaller businesses with evolving needs, as Cosmos DB's flexibility enables rapid data model modifications without significant data management concerns.

For basic queries, Cosmos DB is a very fast and efficient option.

The main benefit of Cosmos DB is its flexibility. It's easier for our developers to work with than previous solutions, and it's better for our customers because we can quickly change and optimize the data structure as needed, eliminating the need to explain limitations. We've also observed that Cosmos DB has improved over time.

Leveraging our prior experience with similar systems, we immediately recognized the advantages of Cosmos DB. Its quick setup streamlined our implementation process, and after addressing a few minor challenges, we could utilize it effectively and efficiently.

Cosmos DB has significantly reduced our total cost of ownership compared to the more expensive alternative we previously used. It's a much cheaper and more efficient solution for our use cases. However, as some of our customers grow, we need to be more strategic with our implementation, requiring additional engineering and planning to keep costs down. While Cosmos DB is initially inexpensive, managing costs at scale requires proactive measures.

Cosmos DB is easy to learn if you're familiar with NoSQL databases, as the concepts are essentially the same. You'll quickly grasp it if you understand NoSQL principles. However, if you're unfamiliar with NoSQL, it might take longer, as Cosmos DB differs from traditional databases. This learning curve applies to all NoSQL products, as understanding the fundamental differences between NoSQL and traditional databases is crucial.

Cosmos DB's greatest strengths are its easy setup and affordability, especially for those who understand its usage. Compared to products like Oracle's Autonomous JSON, Cosmos DB offers greater driver and code support, making it significantly easier to learn and use.

Cosmos DB is quick at searching through basic datasets, though its indexing capabilities may not be as robust as some competitive solutions. This can make it challenging to perform complex queries on large datasets.

The current data analytics of Cosmos DB is inefficient for large-scale queries due to its transactional design. While tools like Synapse can transfer data to an analytical store, there's no effective way to utilize that data. Cosmos, in particular, is not optimized for analytical queries, especially with large datasets, and lacks the cost-effectiveness of solutions like Oracle Autonomous JSON, which seamlessly integrates analytical capabilities. Although Microsoft offers various tools, a comprehensive solution for efficient analytical queries within this system remains elusive, short of implementing a full-blown SQL Server.

I have been using Microsoft Azure Cosmos DB for two to three years.

The latency and availability of Cosmos DB have been excellent. I haven't had to worry about it; the latency has been consistently low, and I can't recall the last time we experienced an outage or performance issue.

Cosmos DB has only been down once the entire time we've used it, and the outage lasted only two hours.

I would rate the stability of Cosmos DB nine out of ten.

Cosmos DB offers scalability when implemented correctly, often in conjunction with other tools to achieve specific scaling requirements.

Cosmos DB's scalability has reduced our overhead by allowing us to efficiently manage costs across different environments, from minimally used development settings to high-demand production environments. Its robust architecture supports diverse applications, including medical and government settings, with sensitive data, ensuring reliable performance and scalability. While proactively addressing potential scaling challenges, we maintain a strategic approach by selectively utilizing Cosmos DB for optimal data management.

We have a good relationship with Microsoft engineers in our region, and some Cosmos leads in Australia. They have responded to any problems we have had with the system.

Positive

We previously used Oracle Autonomous JSON Database, a NoSQL version of Oracle database, and MySQLX, a variation of MySQL supporting unstructured data. Choosing between MySQLX and Cosmos DB is a tough decision. While MySQLX offers powerful SQL querying and analytics that Cosmos DB lacks, Cosmos's distributed nature eliminates concerns about backups and disaster recovery, unlike MySQLX.

Deploying Cosmos DB was remarkably easy. It's the one Azure product I install without worries, as it consistently performs reliably.

Cosmos DB is a valuable resource for our customers because it eliminates the need to set up and manage complex database systems. Its ease of use and global distribution capabilities make it a cost-effective solution for our SaaS product. Unlike alternatives such as SQL Server, which require dedicated database administrators, Cosmos DB allows us to achieve the same results without extensive engineering resources.

Cosmos DB is cost-effective when starting but requires careful management. In my experience with customers, I emphasize that improper use can lead to significant expenses, while correct implementation ensures cost-effectiveness.

I would rate Cosmos DB seven out of ten. If they can fix the analytics issues, Cosmos DB would be a great product.

Cosmos DB presents a trade-off: it's easy to start using and deploying systems, but optimization can be challenging and expensive without proper indexing and data modeling. Efficient querying, especially with large datasets, requires careful planning due to the platform's architecture.

Our organization has a few engineers who directly use Cosmos DB. We have a diverse client base, ranging from small to large enterprise organizations, who utilize our products built on various technologies, including Cosmos DB.

Our comprehensive suite of DevOps tools enables seamless transitions between databases as needed. This streamlined process allows most of our engineers to quickly onboard to Cosmos DB within a week.

If you're using Azure and need a simple, easy-to-start database, and NoSQL makes sense for your needs, Cosmos DB is a good choice. It's a solid product with excellent Microsoft SDKs and enterprise-focused support. While Mongo might offer more developer-focused features, Cosmos DB prioritizes security and scalability, making it easier for enterprises to get started quickly.

We use it for different companies and different clients. We have Fortune 500, startups, and mid-sized companies as our clients. They are in healthcare, finance, fintech, tech, manufacturing, construction, real estate, telecom, and a lot of other industries. They all love it.

It is the best in terms of performance and scale, and it can do both SQL and NoSQL workloads, so it is pretty impressive. One of the least understood use cases happens to be cost and caching. It has a pretty amazing caching engine, and its cost is amazingly low. Especially with the strategies we have designed, we can show a cost reduction of 99% in certain cases. The request charge reduction is anywhere between 75% to 99%. It has been pretty amazing to get cost, quality, and time. We can get all three with it. It is one of the very few databases that can even get there.

We use the built-in vector database capability. It is pretty fascinating. You get recalls that are pretty high. In the competitive landscape of databases, it is surprisingly better in terms of p95 latency and also requests per second, which is something that every customer wants but does not easily get by default. You can also use HNSW, for example, a lot cheaper than you would otherwise because of the DiskANN technology. It is similar to HNSW, but it is on the disk, so it is cheaper. You are not going to the memory. That saves you a lot of money, which is important because when you are running workloads that are getting to terabytes and terabytes, the cost is a huge concern, especially to support the underlying business. That is pretty amazing in terms of DiskANN, which is a Microsoft technology that is very well implemented in Azure Cosmos DB.

Usually, the vector database is integrated with a bunch of other applications. It could be a CRM system behind the scenes, or it could be any LLM-based application. The interoperability with other solutions is fairly simple because, at the end of the day, it is just an API. You can make it work with anything.

We use a lot of different models including Azure OpenAI and some open-source ones through Azure AI Studio. They are very easy to use with it because it is just an API.

It is as fast as what you would find elsewhere. It scales, and they do that part for you. Performance and scale are the things that Azure Cosmos DB got right. That is definitely a positive. If you do not use the vector database, you may get into hallucination issues. Things might slow down. Such issues do not happen if you are using the vector database correctly. Your LLM is now supported with the rack pattern, which is done very well by DiskANN and Azure Cosmos DB.

DiskANN does a great job with recalls. We can decide how high we want them to be. That is the best it gets. If you are using vector searches, it does a great job. I do not usually use Azure Cosmos to compete with a regular or classic search engine.

In terms of Azure Cosmos DB’s ability to search through large amounts of data, currently, the maximum we have on it is in terabytes, but a lot of that depends on how you do a lot of things. That includes data modeling and partitioning, and then your entire vector strategy, which is what we specialize in. We have seen great results. You get the best of all worlds. For example, there is a higher recall at pretty amazing requests per second, which in some cases is 10x to 15x of what you would get for the same recall with another engine. Your latency is also a lot lower. In some cases, it is incredibly low. For example, it is 10x to 15x lower than others. This combination is very hard to get with other databases that we have tried, so from that angle, Azure Cosmos DB has done a terrific job.

A few years ago, we put out a report that took Azure Cosmos DB as it is and compared it with other databases out there, and it was 92% cheaper on reads and 20% cheaper on writes. After that, we used our optimization, and we were able to further reduce that by another 75% to 99% in different cases. We have an online talk about it where we partially show how to get there. Those are not full solutions. It was a conference where I had 15 minutes, and I ended up doing a demo. 

It is pretty fascinating because it is very hard for other databases to come anything close to it in terms of the cost given the fact that you have pretty amazing performance and scale. A lot of people can beat you on Azure Cosmos DB, but they do not give you the right performance and scale you need for business, so those cost savings are meaningless. For our customers, it has got to be the best of all worlds, and fortunately, Azure Cosmos DB has that.

Pretty much all of the features are valuable. Change feed is a pretty amazing feature. Once you make the changes, they are quickly read for you, and then you also have geo-replication. You can do a lot of things in your region, and the same regions can be replicated all over the world. There are different geographies. I can have my servers pretty much anywhere in the world. The data could be within the country or continent when there is a data restriction policy and things like that. Security is big. There are a lot of very good features.

It is very easy and very simple now given all the improvements, but it is also designed very well. Especially because we specialize in it, it is the easiest thing on our side. Data modeling happens to be a lot easier than SQL and others. The learning curve is a lot smaller than a typical RDBMS. It is very like code, and that is another benefit. Developers love it because you do not have to learn something new. You can use the same object that you are using in your code, and you can write stored procedures in JavaScript if you want to. If you want to do anything else, you could use the SQL API or NoSQL API, or you could use MongoDB API. It supports a lot of different APIs. You do not have to learn anything new, so the learning curve is way smaller than pretty much anything out there.

The best part of Azure Cosmos DB is that you barely have any maintenance. This is what I liked about it in the first place. 

In the long run, there should be an addition of more features, especially because this space is evolving quickly. It all boils down to how many more features you are adding, how many integrations you are supporting, and how many more APIs you have that are standard APIs. The team is already doing a great job. They are already doing all that is needed, but the more features we have, the easier it is for us and our clients.

For example, when you have these vectors, it requires us to know a little bit about the configuration behind things such as HNSW. When it comes to the MongoDB vCore piece of Azure Cosmos DB, people like us know how to get to higher recalls easily but a regular user may not. If they have a feature that provides an easy way to get to a certain recall you need, and that is a configuration by default, that would be great. Currently, the flexibility is amazing, and we love that. The competition is usually not providing that. The competition sometimes gives you a recall of 80%, but they are taking away the latency and requests per second. Azure Cosmos DB does not do that. It is a better solution. If Azure Cosmos DB has configurations and a feature allowing us to pick any of the use cases we want, it would be great. For example, if I have an application that I am okay with, and my application does not require a huge recall that is 80% but needs one that is 60%, for us, it is very easy to take HNSW and do it that way and reduce the requests per second because that might not be a concern. If there is a feature that allows people to pick out of five different permutations and combinations, it would be very easy for anyone else to do it. However, keep in mind that competition does not even have that flexibility, so competition is lagging behind on that, at least in the case of the ones we have tried. If Azure Cosmos DB has such a feature, it will be easy for more people to take advantage of the things we are taking advantage of.

I started using it when the first version of DocumentDB came out, and then a couple of years later, it was renamed to Azure Cosmos DB. I have been working with it even before it was called Azure Cosmos DB. I am still using it, and it is my favorite database.

In 2020, we put out a report. It is on our website. This was the only one out of all the major databases out there that had a linear throughput increase for hundreds of servers. What is crazy is that everyone else said that they do it, but you could literally see that after a certain number, they would slow down. One of them was a pretty majorly known, multibillion-dollar NoSQL database, but after 50 servers, it would just slow down completely. You could literally say, "Wow, it is taking time to go to the next level." Azure Cosmos DB was the only one that had linear throughput. Our team thinks that the underlying infrastructure of Azure Cosmos DB is procured in advance, and that is why they can have a linear scale for as much as you want to go. It totally depends on how big, literally, Azure is. The competition is running on someone else's cloud mostly. They probably procure machines as they go, and maybe that is why they are slow. This is an interesting thing that made us love Azure Cosmos DB.

Latency is pretty straightforward. They guarantee 10 milliseconds read and write times now, which used to be 10 and 15 earlier. That is one thing that is pretty incredible. A few things that we have shared with customers is that there is always a wrong way of doing things. You may know the right thing, and it is easy for you to get the latencies, but you may completely mess up the data modeling as well as your code and hundreds of different things. You may be making five calls when you need to make one. You may be doing a lot of other things that are not necessarily best practices. In some cases, we have seen people having a latency of more than 200 milliseconds, which we brought down to 10 milliseconds.

Azure Cosmos DB’s dynamic scaling decreases an organization’s overhead costs big time. This is where it stands out. A lot of our clients who were previously using RDBMS kind of solutions found them to be slow. It would take forever. They would not scale beyond a certain point, and they would get extremely costly after some time because you have huge machines. Some of them were paying millions of dollars for one machine, whereas now, they just pay and go and they can scale. They can go all the way up, and if they want to go down, they go down. For example, on Thanksgiving, they may be scaling to hundreds of nodes behind the scenes, and the next day, they could be scaling back to three nodes. Imagine the cost savings when you never had to procure those servers or anything else. This is the genius of this whole thing. They are being able to take advantage of a scale that they could have never had as an organization. This is not just for startups. Even big corporations can take advantage of the same exact thing. It works for every single one out there.

I never had to contact support. If you know what you are doing, then it is really good. I do not even know of anyone calling Azure Cosmos DB support for anything.

I have worked with almost all of the competitor solutions. I cannot think of any disadvantages of Azure Cosmos DB unless the competition is for a specific use case. For example, in the beginning, Redis was great for caching. It had a great Pub/Sub Over a period of time, Cosmos DB got there, but Azure Cosmos DB is a lot more than just that use case. Today, it is easy for me to pick Azure Cosmos DB as a caching engine. I would not have done that five or six years ago.

We do not do much with the on-premises version. We only work with the cloud version.

Its deployment is pretty easy. I have been doing it for a long while, so it is easy for us and our clients. I do not know about others.

Our implementation strategy depends on what kind of project it is. We do greenfield, brownfield, and all of the projects. We do integration projects. We also do projects where they are only doing an addition of LLM. We start with understanding the client's needs and then figuring out what is currently there. If there is nothing, we data model the whole thing from scratch and go with the best practices. A lot of times, if it is brownfield, a bunch of work is already done, so we are not going and figuring out what is the most optimum way to do it within client constraints. We create a strategy based on that and implement it based on their tech stack because everyone has a different one. Once we get the approval on that, we move forward with the implementation.

The number of people required and the time required depend on the client and their workload. If you have a small app, you could be onboarded on day one. If you have a big app with petabytes of data, it is usually a month of work. It totally depends on what we are looking at or the use case.

In terms of the learning curve, Azure Cosmos DB is one of the simplest ones out there. It is on the easier side. There are a couple of them that are pretty easy. 

To new users, I would advise understanding different propositions. Start with understanding what kind of data set you have and every single thing. Also, know the tech stack you have and pick your strategy accordingly. What you do not want to do is go with the flow without understanding what a NoSQL database is supposed to be like and make changes down the line.

A lot of people with an SQL background, unfortunately, start using any NoSQL databases, not just Azure Cosmos DB, in a way that is not very good for them because the patterns that usually are the best patterns for SQL may not be the best patterns for NoSQL. For example, there is a reason we do normalization in SQL. That takes away duplicate data, which is perfectly okay, but in the case of NoSQL or Azure Cosmos DB, we can scale and have duplicate data in places if we have a different kind of use case. If I want to make different kinds of searches available, I can have three different kinds of searches available for similar kinds of parameters. I will not be worried about doing that in a NoSQL environment because I can scale out pretty easily, so data does not hold me back. In an RDBMS environment, I might be doing two or three joins to make sure that I am making it fully normalized because if my data increases drastically, that will create a scale up situation. Scale-up is the only thing you can literally do with RDBMSs. Mostly, scale-out is not that easy unless you are on the cloud and you are using the scale of the cloud, and then you have performance issues. In those kinds of different scenarios, the DBAs or people with an RDBMS background need to come up with an open mind and understand what this is.

It is not that you have to learn a lot about Azure Cosmos DB. You will have to learn about this new paradigm. It is not very new. It has been going on for more than a decade. We have been doing it for more than a decade, but we see a lot of people coming from an RDBMS background and getting it wrong, and then you're paying people like us a lot more money to fix it. It is easier to work with someone like us in the beginning or do a little bit more due diligence and learn that paradigm before you get started. That will save you a lot of money and time, and hopefully, you will not need our services at that point of time. That is definitely my advice.

I would rate Azure Cosmos DB a ten out of ten at this time.

We use Cosmos DB to store the concept of data and how it is entered by the user.

Cosmos database has helped improve search result quality, allowing more results. We implemented the ASR service to gather data from users. Cosmos database does an excellent job of searching through large amounts of data. The speed is impressive, and integrating our power-up database with Kafka was an improvement.

The most valuable features of the Cosmos DB include its ease of use and optimization and its seamless integration with code. We do not use the built-in vector database capability, but its interoperability with Azure AI services is noteworthy.

One area of improvement for Cosmos database is the auto-scaling of RUs during high loads. It would be beneficial if the database could automatically scale resources rather than requiring manual adjustments.

I have been using Cosmos DB for two years.

To scale workloads effectively with Cosmos database, we must manually increase the RUs. During the initial implementation phases, we encountered issues with scaling, but it appears to have been resolved.

We replaced our SQL database with Cosmos and Kafka, resulting in an improvement in operational performance.

The initial setup was straightforward and did not take much time.

I rate Azure Cosmos DB eight out of 10. The system itself is effective for our current use cases.

Microsoft Azure Cosmos DB has helped improve the search result quality of our customers' organization. The customer gave us the feedback that they are able to easily find the data they are looking for. It is very quick. That is the best feedback. They have a large amount of data, and they can find it quickly.

Our customer is very satisfied with it. Our branch does not use it yet. We used it for a customer, and the customer is very satisfied so far.

There are no particular factors that need improvement. There is a little bit of a learning curve with scaling workloads, but it works smoothly.

I am still learning it and have only used it for one application so far. 

No issues have been reported regarding the stability. It is very fast.

There was a little bit of a learning curve with scaling workloads, but overall, it went smoothly.

I have not had any challenges. However, the configuration done in a session at Microsoft Ignite looked complicated. It would be a bit challenging to do the same configuration. 

There is a little bit of a learning curve. The onboarding process for the team took about two weeks.

The only feedback the customer gave us was that it was way cheaper than they expected. 

The customer had a high budget, but it turned out to be a little bit cheaper than what they expected. I am not sure how much they have spent so far, but they are satisfied with the pricing.

I would recommend this product. I would like my organization to develop and explore it further. I would rate Microsoft Azure Cosmos DB an eight out of ten.

We use Cosmos DB in a multifaceted manner, primarily as an operational data store. Additionally, it serves as an analytics and reporting synchronization platform through Synapse Link, Azure's connective data warehousing solution. By connecting directly to Cosmos using the change feed, we project data into our data warehouse and data lake, facilitating both operational functions and analytical reporting needs.

Cosmos DB significantly enhanced our search result quality due to its impressive performance and reliability, bolstering the overall quality of our service offerings.

Realizing the full benefits of Cosmos DB took time, roughly one to three months. This was mainly due to the transition from a relational data store and the need to restructure our data to fully leverage Cosmos DB's capabilities, such as change feed and other features. While the immediate benefit was eliminating infrastructure maintenance and reducing DevOps/SRE overhead, achieving the total value we sought required optimizing our data structure for this new environment.

We primarily use Cosmos DB because it's a managed platform service, eliminating concerns about hosting and reliability. Its geo-redundancy feature allows us to share data globally across three data centers in US Central, East US, and West US, with US Central as the primary write region and the others for reading. Additionally, we leverage Cosmos DB's auto-scaling features, including burst capacity, database-level, and collection-based auto-scaling, and dynamic scaling per region/partition, to accommodate our fluctuating workloads throughout the day.

Cosmos DB has a couple of areas for improvement. Firstly, the lack of multi-collection joins is a significant limitation. Secondly, Azure Synapse Link, their data warehousing and synchronization feature, is unreliable and still feels like a preview feature. Improved reliability in this area would be greatly appreciated. Additionally, while Microsoft provides helpful internal monitoring tools, the managed nature of Cosmos DB can sometimes hinder visibility and make it difficult to troubleshoot issues, leaving us unsure whether the problem lies with our implementation or the service itself. Overall, we are satisfied with most aspects of Cosmos DB, but addressing these issues would significantly enhance its usability.

One of the primary challenges with Cosmos DB as a non-relational data store is the careful data modeling required due to the lack of collection-level joins when using the SQL API. While joins are possible within a single document, joining across documents or collections is not supported with this API. Although the Mongo API and Gremlin API on Cosmos DB allows for cross-collection joins this limitation in the SQL API remains a significant drawback.

I have been using Microsoft Azure Cosmos DB for ten years.

While Cosmos DB has experienced occasional stability issues in previous years, its performance has been consistently reliable over the past 12 months.

The scalability of Cosmos DB is very good. It is one of the best capable offerings for scaling workloads.

We pay for unified response support, and generally, the support for Cosmos DB is good. However, without top-tier support, it can take a while and might not always be the most helpful.

Neutral

We extensively use Google Bigtable and, to a lesser extent, MongoDB. Additionally, we are increasingly utilizing Redis for various NoSQL use cases.

Cosmos DB and MongoDB differ in several key areas. While Cosmos DB excels in scalability, reliability, especially on Azure, and change feed capabilities, MongoDB offers a superior developer experience with local development options and more complex NoSQL use cases like multi-collection joins and advanced store procedures. However, MongoDB's query workloads are generally more capable, and it offers a wider range of indexing options and document size limits. Ultimately, the choice depends on specific needs and priorities, with Cosmos DB favouring cloud-based applications and MongoDB providing greater flexibility for complex database operations.

The initial setup was straightforward; one person completed it within one week.

We handled the deployment all in-house without any external integrator or consultant.

Cosmos DB is a managed offering, so its cost is understandably higher. However, the value it provides aligns with its price, especially considering the discounts we receive. By purchasing reserved units for three years, we secure a significant discount, making the cost justifiable for our needs. Without this discount, the list price might be prohibitive for certain use cases.

I would rate Microsoft Azure Cosmos DB eight out of ten.

It took us three months to be fully onboarded with Cosmos DB.

The learning curve for Cosmos DB is certainly different from SQL databases. While most developers become proficient with basic functionality within a week or two, achieving true expertise in Cosmos DB requires a considerably longer time investment due to its unique architecture and features.

The maintenance is handled by Microsoft.

Be very careful with your partition keys when using Cosmos DB.

We use Cosmos DB as our primary data store for all the different software services we offer.

Using Cosmos DB requires a shift in mindset due to the inherent differences between NoSQL and traditional relational databases. Those familiar with SQL Server, for example, will need to adjust to the fundamental differences between the systems. While there's an initial learning curve, Cosmos DB ultimately offers significant flexibility.

Cosmos DB offers several immediate and long-term benefits. First, it eliminates the need for extensive upfront database design, as its flexible schema allows for easy adjustments to data management. Second, it significantly reduces maintenance requirements compared to relational databases, which demand constant attention to tasks like index management and addressing issues arising from large table sizes. Cosmos DB avoids these issues, resulting in substantial cost savings over time.

Cosmos DB significantly reduced our total cost of ownership by approximately 40 percent due to recent design changes, specifically a feature called partition-based scaling.

The onboarding process for Cosmos DB is quick, due to the comprehensive documentation.

As a NoSQL database, it offers schema flexibility which simplifies design and reduces initial engineering overhead. This allows for rapid product delivery, especially for initial versions. The change feed is instrumental to our design and handling of data changes. Recent additions like partition-based scaling have significantly reduced costs of over $25,000 per month with minimal effort. These are just a few of the top features we appreciate about Cosmos DB.

We encountered an issue with Cosmos DB's recently introduced hierarchical partition feature. After enabling it, we discovered that the web-based Cosmos Explorer tool sometimes fails when hierarchical partitioning is disabled. Although it usually works, we occasionally encountered situations where we couldn't query and manually inspect data in the Cosmos Data Explorer within Azure. We believe this is a significant issue and anticipate a fix will be released soon, although it may already be resolved.

I have been using Cosmos DB for ten years.

Cosmos DB has demonstrated excellent stability with no issues encountered.

Cosmos DB is extremely easy to scale up, and changes usually take just a few seconds to a few minutes.

The customer service was quick and to the point during our limited interaction. The correct information was conveyed without the typical pain associated with support systems, where someone with little product knowledge addresses issues.

The deployment is straightforward and generally takes about fifteen minutes to complete. Initial database provisioning requires five to ten minutes, followed by a minute or two to create the necessary tables and containers.

The deployment process is automated using ARM templates, allowing developers to initiate deployments by clicking buttons within the Azure DevOps tools.

Using partition-based scaling, we achieved about 40 percent savings in costs for Cosmos DB.

The Cosmos DB pricing model, initially quite complicated, became clear after consulting with Azure Advisor, allowing us to proceed with confidence.

I rate Cosmos DB ten out of ten.

While there is a learning curve when transitioning from traditional SQL databases to NoSQL databases, this is not specific to Cosmos DB. Regardless of the provider, understanding NoSQL requires a shift in approaching data storage and retrieval, particularly for those familiar with relational databases. This inherent learning curve stems from the fundamental differences between the two database types and necessitates learning new concepts and techniques for effectively working with NoSQL databases.

No maintenance is required.

For newcomers, it is crucial to understand that despite the SQL API, Cosmos DB is not a traditional SQL database. Many issues arise when trying to apply relational database principles. Understanding NoSQL databases' limitations and adapting to the mindset required is essential.

We mainly use it as the database for our platform, which is an application that users use as an interface for their IoT products. I work in the IoT chapter, and we developed an application where customers can manage their IoT devices and have a holistic view of their deployment. All data is aggregated in our database, cleaned up with ETLs, and stored in Cosmos DB.

When dealing with IoT products, we encounter massive amounts of data, unlike in commerce, where traffic and data fluctuate. IoT devices, especially ours, generate constant data streams every five minutes, necessitating robust handling. We chose Azure Cosmos DB, specifically the PostgreSQL version, for its ability to store massive amounts of data without performance degradation, thanks to its columnar storage feature. This allows us to compress older data, such as telemetry data older than two years, which is crucial for managing the ever-growing volume of information. Even with compression, we maintain fast access to the data, ensuring optimal application performance.

I had prior experience with MongoDB on Azure, a platform developed by Microsoft. Since we already used Azure, integration with Cosmos DB, Azure's native NoSQL database, was significantly faster than a standalone MongoDB instance. While Azure offers integration with MongoDB, utilizing Cosmos DB simplified the process due to the readily available APIs. Similarly, Azure PostgreSQL also streamlined integration because it is a Microsoft product, eliminating the need to work with a third-party vendor.

As the only database I've used extensively, particularly with Spark, I recently re-architected our application to identify performance bottlenecks. Surprisingly, Azure Cosmos DB consistently demonstrated exceptional speed, executing complex queries in under 100-200 milliseconds. This contradicted our initial hypothesis that the database was the primary cause of slowdowns. It proved to be one of the most efficient components, requiring minimal optimization. Therefore, Cosmos DB has proven optimal for searching through our organization's large datasets.

We have only used Azure Cosmos DB, so there isn't much reference to compare. However, within our chapter, when dealing with other chapters, there is a noticeable difference in performance in our application. The biggest differentiator in performance and speed for applications is typically the database, and having a speedy database solves a lot of performance issues.

Cosmos DB has provided excellent latency and availability. We have not experienced any database inaccessibility, downtime, crashes, or unexpected bills due to data spikes, even with the massive amounts of data we handle.

A single PostgreSQL node can handle a massive workload of telemetry data, eliminating the need for horizontal scaling in our case. Its impressive capacity and resilience ensure smooth operation even during spikes or large influxes of data.

The standout features are its ability to do data compression easily and the ability to scale horizontally. We initially used Azure Cosmos DB NoSQL, a document-based database, but as our application grew, we realized the relationships between entities were becoming more complex and NoSQL was no longer suitable. To address this, we migrated most of our data to Azure Cosmos DB for PostgreSQL, a relational database, while retaining the original NoSQL database for telemetry data. This approach offers two key benefits: simplified data compression, thanks to seamless integration with our ORM, Prisma, and horizontal scalability, providing the flexibility to expand our database capacity as needed quickly.

Azure Cosmos DB for NoSQL has a less developed interface and fewer SQL commands than MongoDB, and its community support is also smaller. Additionally, Azure Cosmos DB for PostgreSQL users face the challenge of not having a portal for running queries.

Microsoft could improve its pricing, and the way request units are purchased. The current system requires users to pre-purchase an estimated amount of requested units, often leading to unused units and unnecessary costs. This pre-purchase model is inefficient and inconvenient for users. Overall, the pricing structure must be more flexible and transparent to align with actual usage.

I've used Cosmos DB for three years now at Spark New Zealand, and even before that, I worked on Cosmos DB inconsistently until my current company exclusively used it.

I would rate Cosmos DB's stability eight out of ten. We haven't experienced any significant stability issues or downtime.

Scalability for Cosmos DB PostgreSQL is rated around eight point five out of ten. The single node is capable of handling massive loads, and we haven't needed to scale horizontally yet.

In the three years of using Azure Cosmos DB, we never needed to contact support, indicating its reliability.

Positive

I have used MongoDB previously. The integration with Microsoft Azure and its products is faster and easier compared to MongoDB.

Deploying the NoSQL database was simple, but the PostgreSQL deployment proved more complex. Initially, it was particularly challenging due to limited resources; it was around when Microsoft acquired Citus, and comprehensive materials were scarce. The lack of a dedicated portal further complicated the process, making tasks like running queries more difficult than the user-friendly Azure portal available for NoSQL.

The Cosmos DB PostgreSQL deployment, including investigation and testing, took one week, while the deployment itself only required two days.

There were around ten to twelve people involved in building the application using Cosmos DB. Other teams within our organization might also use it.

Pricing is mid- to high-end. The way request units are purchased is atypical, as they must be bought ahead of time based on expected usage, which can be inefficient.

I would rate Cosmos DB eight out of ten.

Cosmos DB, particularly the PostgreSQL setup, can be relatively maintenance-free. While the service itself requires no active maintenance, optimizing for cost-efficiency may involve implementing scripts to compress older data, as demonstrated in the PostgreSQL example. This proactive approach minimizes the need for ongoing maintenance, ensuring the application remains hassle-free.

Our Cosmos DB, deployed in a single region, primarily serves businesses and establishments rather than individual users. Each customer typically has only a few users on the app. Our primary concern isn't the number of users but the volume of telemetry data generated by devices at each establishment. These devices transmit data every five minutes, resulting in a constant influx of information 24/7, 365 days a year.

Within my chapter, around 15 people are using Cosmos DB.

For NoSQL, I would recommend it if you are already using Azure. For PostgreSQL, the lack of a query portal is a downside, but the features it offers can justify its slightly higher price.