InfluxDB vs. MySQL Heatwave

InfluxDB is very good at storing monitoring metrics (e.g. performance data). InfluxDB is not the right choice if you need to store other data types (like plain text, data relations etc.).

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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.

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• Perfect handling telemetry data.
• Low latency, near real time.
• SQL-like language makes it easier to query.

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• Data mining
• Data analysis
• Parallel computing

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• GUI based administrator console

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• 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

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InfluxDB is a near perfect product for time series database engines. The relatively small list of cons are heavily outweighed by it's ability to just work and be a very flexible and powerful database engine. The community and support provided by the corporation are the only areas I have little experience.

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We have worked with the InfluxDB support team a few times so far and it has been positive. Issues submitted are worked on promptly and we have good feedback.

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To be honest, I didn't look at alternatives since InfluxDB performs very well if you can oversee the lack of security and HA features. But for all challenges, there is an easy solution which brings you forward (e.g. read load balancing can be achieved by using a common HTTPS load balancer).

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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.

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• Provided us a time series DB.
• With the SQL-like language, it is very easy to learn.
• Empowered us to keep track of our events.

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• 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

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