MySQL Heatwave vs. Oracle MySQL Cloud Service

If you need a highly performant database, which is much faster than a conventional RDBMS, without worrying about infrastructure scaling, performance tuning, and query optimization, go for MySQL Heatwave. This is the best in the market. It is also very secure and reliable. Regular backups ensure that your data is safe and consistent always.

Incentivized

It is very scalable and it gives us backup for everything. Because of this feature, we (as a developer) can do any R&D if required. It is very stable so we can get any type of help. It has a recovery feature also which we used recently for my project. It is very easy to recover.

• Data mining
• Data analysis
• Parallel computing

Incentivized

• Oracle database has ability to store large scale of data.
• It can support any kind of scenario with any label of system.
• Encryption and security is the key features of Oracle database 12c.

• Pricing can be a concern if not configured properly
• Not available as a standalone or on premise offering. Only available through OCI
• No Jobs interface like that in Databricks with Spark and Delta Lake

Incentivized

• It has some issues [when] performing local unit testing in personal edition.
• It is costly if we will go for cloud.
• Need license for some features.

We have not used other platforms. So, after a market survey on price, support and facilities, we have decided to go for MySQL Heatwave platform. Which integrates many technologies and provide best support, service and pricing. Also, it provides access to database. The speed of the execution is very high.

Incentivized

It has the most stable database. It smoothly supports [a] huge [amount of] data. Security-wise it is best among them. It is a very large community and has well-defined documentation, which can support [during the] implementation of the Oracle database. We can easily scale up servers whenever [we] change our requirements. Oracle12c is best for large-scale projects like banking and retails.

• We no longer have to write data pipelines in Spark anymore for executing ML/Analytics Workloads
• Execution time of the analytics platforms has reduced considerably
• Overall time to market has gone down drastically

Incentivized

• It has ability to handle [a] huge [amount of] data and give [the] best performance
• Easy to write PL/SQA queries
• Secure and encryption option