InfluxDB vs. Tanzu Data Services (Greenplum, GemFire, RabbitMQ, Tanzu SQL)

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 to execute ml algorithms, learning techniques, or mathematical calculations on large amounts of heterogeneous data, VMware Tanzu Data Services will be ideal. It will be really simple to set up, particularly if you choose AWS as your integrated cloud provider. However, if you're working with lower data amounts, such as gigabytes, it can be superfluous.

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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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• Apache MADlib provides popular machine learning functionality.
• Allows you to query terabytes of data databases.
• Interoperability for AWS S3 is effortless.

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• GUI based administrator console

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• Running on Azure is a little more difficult.
• Synchronization with Kafka may be a little easier.

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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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They were very helpful. We needed support for initial implementation.

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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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• 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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• There was a noticeable reduction in system reliability.
• Saw a reduction in unsuccessful analytics operations.

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