Microsoft Azure Cosmos DB is Microsoft's Big Data analysis platform. It is a NoSQL database service and is a replacement for the earlier DocumentDB NoSQL database.
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OpenText Vertica
Score 10.0 out of 10
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The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by OpenText.
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Pricing
Azure Cosmos DB
OpenText Vertica
Editions & Modules
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Pricing Offerings
Azure Cosmos DB
OpenText Vertica
Free Trial
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Free/Freemium Version
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Premium Consulting/Integration Services
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Entry-level Setup Fee
No setup fee
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Community Pulse
Azure Cosmos DB
OpenText Vertica
Features
Azure Cosmos DB
OpenText Vertica
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
Like any NoSQL database, whether it's MongoDB or not, it's best suited for unstructured data. It's also well suited for storing raw data before processing it and performing any type of ETL on the data.
Vertica as a data warehouse to deliver analytics in-house and even to your client base on scale is not rivaled anywhere in the market. Frankly, in my experience it is not even close to equaled. Because it is such a powerful data warehouse, some people attempt to use it as a transactional database. It certainly is not one of those. Individual row inserts are slow and do not perform well. Deletes are a whole other story. RDBMS it is definitely not. OLAP it rocks.
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Quick access and response to data queries due to high speed in reading and writing data
Create a powerful digital experience for your customers with real-time offers and agile access to DB with super-fast analysis and comparison for best recommendation
We had a thought time migrating from traditional DBs to Cosmos. Azure should provide a seamless platform for the migration of data from on-premises to cloud.
Could use some work on better integrating with cloud providers and open source technologies. For AWS you will find an AMI in the marketplace and recently a connector for loading data from S3 directly was created. With last release, integration with Kafka was added that can help.
Managing large workloads (concurrent queries) is a bit challenging.
Having a way to provide an estimate on the duration for currently executing queries / etc. can be helpful. Vertica provides some counters for the query execution engine that are helpful but some may find confusing.
Unloading data over JDBC is very slow. We've had to come up with alternatives based on vsql, etc. Not a very clean, official on how to unload data.
It's efficient, easy to scale, and works. We do have to do a bit of administration, but less now than when we started with this a couple of years ago. Microsoft continues to improve its self-management capability.
It has very good compatibility and adaptability with other APIs and developers can safely create new apps because it is compatible with various tools and can be easily managed and run under the cloud, and in terms of security, it is one of the best of its kind, which is very powerful and excellent.
Microsoft is the best when it comes to after-sales support. They have a well-structured training and knowledge base portal that anyone can use. They are usually quick to respond to cases and are on point for on-call support. I have no complaints from a support standpoint. Pretty happy with the support.
I haven't had any recent opportunity to reach out to Vertica support. From what I remember, I believe whenever I reached out to them the experience was smooth.
Cosmos DB is unique in the industry as a true multi-model, cloud-native database engine that comes with solutions for geo-redundancy, multi-master writes, (globally!) low latency, and cost-effective hosting built in. I've yet to see anything else that even comes close to the power that Cosmos DB packs into its solution. The simplicity and tooling support are nice bonus features as well.
Vertica performs well when the query has good stats and is tuned well. Options for GUI clients are ugly and outdated. IO optimized: it's a columnar store with no indexing structures to maintain like traditional databases. The indexing is achieved by storing the data sorted on disk, which itself is run transparently as a background process.
