MongoDB vs. Oracle Data Masking and Subsetting

If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.

Incentivized

The tool is excellent when you need to provide all the details about your clients, yet hide their identity - all while maintaining the referential integrity of the data (so child-records of the masked parent record and maintain the same fake ID of the parent).

Incentivized

• Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
• You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
• Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.

Incentivized

• It offers several ways in which you can mask your data; for example, you can choose to replace all names with "real fake names", or choose to replace all SSNs with existing SSNs, but randomly assigned. You control the algorithm.
• It works on non-Oracle databases as well (in our case, we use it for both Oracle and SQL/Server).
• The overhead is minimal (it doesn't take long to run, and it doesn't consume too many system resources.

Incentivized

• An aggregate pipeline can be a bit overwhelming as a newcomer.
• There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
• Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.

Incentivized

• The learning curve was not trivial - but we got through it.
• Cost - this product is not cheap.

Incentivized

I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.

Incentivized

NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.

Incentivized

Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.

Incentivized

While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.

Incentivized

We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.

We also looked at Delphix: the tool was quite powerful, easy to use, and competitive from a cost standpoint. However, since our entire data warehouse environment is built on the Oracle technology stack, it made sense to us to use the Oracle product here, as it integrates very well with other Oracle database and ETL products.

Incentivized

• Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
• You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB

• We have many compliance regulations we need to adhere to. Without this tool, we were always taking a risk of exposing client information, and get penalized by the State of the Feds (the financial consequences are significant).
• So while the tool doesn't save us money directly, it greatly reduces the risk we had been taking all these years. To some degree, this is much like an insurance policy.
• Given the above, it also allows us to share information with other departments/agencies, in situations where before we simply couldn't take the risk of exposing client information.

Incentivized