"MongoDB is scalable and stable. The initial setup is very easy, and deployment and maintenance can be done by one person."
"The clustering is very good. It allows us to have high availability."
"like its performance and the stability. It's very stable and, performance-wise, it's really great."
"The solution's most important aspect is its seamless database."
"One of the most valuable features of MongoDB is it is Its open source."
"The solution is user-friendly with a good object retrieval feature."
"It stores historical data with ease. For example, if you are a healthcare member, then you will have multiple records of visits to the doctors. To store such data in Oracle Database, you have to create many records. You might also have duplication problems because your records are going in again and again, because of which the data warehouse and the maintenance cost will be huge. MongoDB is comparatively lightweight. It is a JSON extract. Once you define a schema and extract it, you can push all the relationships in any way you want. It is easier to define and get different types of transactions into MongoDB. It is also easier to set it up as compared to other solutions. MongoDB is a NoSQL database, which means it is a document DB in which you can store documents that you created in BSON. It is pretty fast in response. It is faster than relational databases because it does not define any primary keys, secondary keys, tertiary keys, and all those kinds of things."
"It is easy to use."
"MemSQL supports the MySQL protocol, and many functions are similar, so the learning curve is very short."
"It would be much more useful if I have an admin user and a password."
"A normal Oracle or database tester will take some time to gear up to MongoDB because the way of writing queries is different in MongoDB. There should be some kind of midway where a person who is coming from an Oracle background can write a query and get a response by using something like a select * statement or other such things. There should be some way for MongoDB to interpret these commands rather than making a person learn MongoDB commands and writing them. I struggled while writing these MongoDB commands. I had not seen such queries before. It was pretty difficult to get them. This is one of the areas where it would help from the improvement standpoint."
"The performance can be improved."
"MongoDB can improve large-size video or media frame operations. There are a lot of customers who want to upload media frames and video games but there is some difficulty. In MongoDB, we are looking out for solutions that are for large-size media files that can be saved and navigated efficiently."
"We'd like information about client onboarding experience and success stories. It would help to have something to show to internal stakeholders."
"The performance could be faster."
"The solution should have better integration."
"Our program developer finds it to be a little unstable, development-wise."
"There should be more pipelines available because I think that if MemSQL can connect to other services, that would be great."
MongoDB is ranked 1st in NoSQL Databases with 34 reviews while SingleStore is ranked 6th in Database as a Service with 1 review. MongoDB is rated 8.2, while SingleStore is rated 10.0. The top reviewer of MongoDB writes "Good pricing and very fast but needs to showcase more use cases". On the other hand, the top reviewer of SingleStore writes "MySQL Big Brother with built-in data pipeline, high concurrency, and blazing fast analytical queries". MongoDB is most compared with Couchbase, InfluxDB, Cassandra, Oracle NoSQL and Scylla, whereas SingleStore is most compared with MySQL, CockroachDB, Cloudera Distribution for Hadoop, SQL Server and Oracle Database In-Memory.
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SQreamDB is a GPU DB. It is not suitable for real-time oltp of course.
Cassandra is best suited for OLTP database use cases, when you need a scalable database (instead of SQL server, Postgres)
SQream is a GPU database suited for OLAP purposes. It's the best suite for a very large data warehouse, very large queries needed mass parallel activity since GPU is great in massive parallel workload.
Also, SQream is quite cheap since we need only one server with a GPU card, the best GPU card the better since we will have more CPU activity. It's only for a very big data warehouse, not for small ones.
Your best DB for 40+ TB is Apache Spark, Drill and the Hadoop stack, in the cloud.
Use the public cloud provider's elastic store (S3, Azure BLOB, google drive) and then stand up Apache Spark on a cluster sized to run your queries within 20 minutes. Based on my experience (Azure BLOB store, Databricks, PySpark) you may need around 500 32GB nodes for reading 40 TB of data.
Costs can be contained by running your own clusters but Databricks manage clusters for you.
I would recommend optimizing your 40TB data store into the Databricks delta format after an initial parse.