# Who do practitioners in similar industries trust most for time series databases, based on user reviews?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I'm looking into which <a class="a a--md" elv="true" href="https://www.g2.com/categories/time-series-databases">time series databases</a> providers practitioners in similar, data-intensive industries actually trust once a system is running in production and can't afford to fail quietly, rather than which ones just score well in a general feature comparison.</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/tdengine"><strong>TDengine</strong></a> (4.7 stars, 15 reviews) earns trust specifically in industrial IoT contexts, with reviewers in manufacturing and hardware pointing to its purpose-built design for handling billions of sensor readings per day as the reason they moved away from more general-purpose databases for this workload.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/aerospike"><strong>Aerospike</strong></a> (4.4 stars, 83 reviews) has earned trust at genuinely enormous scale, with one team in the packaging industry using it to track nearly trillions of records in real time across a global infrastructure, crediting its low administrative overhead for large clusters as what made that scale manageable without a proportionally larger operations team.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/influxdb"><strong>InfluxDB</strong></a> (4.5 stars, 102 reviews) shows up across a wide range of industries specifically for IoT and application monitoring use cases, with its 300-plus Telegraf plugins cited as reducing the integration work needed to start collecting data from an existing tech stack.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Trust in this category seems to track fairly closely with whichever industry's specific scale and reliability demands a database was originally built to survive.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Would it be fair to say the "best" choice here depends more on matching a database's origin use case to your own industry than on overall review scores? And for anyone in manufacturing or industrial settings specifically, has TDengine's smaller community been a real obstacle when troubleshooting something unusual?</p>

##### Post Metadata
- Posted at: 3 days ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;I think the origin-use-case point is worth testing, but I wouldn’t stop at industry similarity. I’d replay your own workload profile: ingestion rate, cardinality, retention window, query patterns, downsampling, and failure recovery, then deliberately push it beyond normal load. A database built for industrial telemetry may look naturally aligned with manufacturing, but what matters is whether its architecture still behaves predictably under the specific shape of time-series data you actually generate.&lt;/p&gt;

##### Comment Metadata
- Posted at: 3 days ago
- Author title: Writer





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