# What are the best wide column database use cases for IoT platforms that need to handle real-time analytics reliably at scale?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">IoT platforms push a lot of high-velocity data and expect real-time access, which is a natural fit for wide column databases. I've been researching the <a class="a a--md" elv="true" href="https://www.g2.com/categories/wide-column-database">Wide Column Database category</a> for which ones reviewers actually run for high-ingest, real-time workloads at scale, framing "reliably" around what the reviews support rather than as an absolute guarantee.</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/scylladb/reviews"><strong>ScyllaDB</strong></a><strong>:</strong> Reviewers cite event ingestion, real-time use cases, and time-series or key-value access at high throughput with predictable latency, which maps closely to IoT telemetry. Careful data modeling is the prerequisite for that reliability.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/hbase/reviews"><strong>HBase</strong></a><strong>:</strong> Used for near-real-time read/write on billions of rows and feeding ML models (reviewers cite fraud detection on streaming data), which fits IoT analytics at volume. It leans on Hadoop and is stronger for OLTP-style access than heavy aggregation.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/cassandra/reviews"><strong>Cassandra</strong></a><strong>:</strong> Its high availability, no single point of failure, and fast writes make it a durable choice for always-on IoT ingestion. Reviewers note read performance and data modeling as the areas to plan around.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/microsoft-azure-cosmos-db/reviews"><strong>Microsoft Azure Cosmos DB</strong></a><strong>:</strong> Global distribution, instant scaling, and low latency suit geographically spread IoT fleets on Azure, with RU/s cost planning as the main caveat for constant high ingest.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For IoT specifically, did you separate hot ingestion from analytics, or lean on one database for both? And which of these actually held up as device counts and event rates climbed?</p>

##### Post Metadata
- Posted at: 2 months ago
- Author title: Marketing
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;I’d separate hot ingestion from heavier analytics once event volume gets serious. Cassandra or ScyllaDB make sense for always-on writes, while a downstream analytics layer keeps aggregation workloads from competing with the ingestion path.&lt;/p&gt;

##### Comment Metadata
- Posted at: 19 days ago
- Author title: Marketer



### Comment 2

&lt;p&gt;The ingestion-versus-analytics split seems to become clearer as the workload grows. I’d be interested in where teams actually draw that boundary. Do you keep recent telemetry in the wide column database for operational queries and move older data elsewhere for deeper analytics, or does the second system appear much earlier once query patterns become more complex?&lt;/p&gt;

##### Comment Metadata
- Posted at: 19 days ago
- Author title: Writer



### Comment 3

&lt;p&gt;On separating ingestion from analytics, most IoT setups end up doing it eventually, because the access patterns pull in opposite directions. Writes want wide partitioning to spread load, while analytical queries want data grouped so they can be read in one place. Deciding that split at design time is far easier than retrofitting it once device counts climb.&lt;/p&gt;

##### Comment Metadata
- Posted at: 20 days ago
- Author title: Tech Consultant



### Comment 4

&lt;p&gt;Curious how teams are handling the split between ingestion and analytics in IoT setups. Like, do you actually run one wide column database for both the real-time writes and the historical queries, or does it always end up being two separate systems? It seems like the ingestion side is a natural fit but the analytics side almost always gets offloaded somewhere else.&lt;/p&gt;

##### Comment Metadata
- Posted at: 2 months ago
- Author title: SEO Content Specialist





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