# What's the fastest wide column database for time-series data?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Hi G2 community. I'm looking into wide column databases for time-series workloads, and rather than crown a "fastest" one (reviews don't carry head-to-head benchmarks), I'll go by which platforms reviewers actually run for high-write, time-ordered data with low latency, within the <a class="a a--md" elv="true" href="https://www.g2.com/categories/wide-column-database">Wide Column Database category</a>.</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 specifically name time-series and event-ingestion among its sweet spots, citing predictable low latency and high write throughput at scale on fewer nodes. Data-model design (partition and clustering keys) is what makes or breaks time-series performance here.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/cassandra/reviews"><strong>Cassandra</strong></a><strong>:</strong> Its exceptionally fast writes and append-friendly model make it a long-standing choice for time-ordered data, with reviewers praising write throughput. The trade-offs are read performance and careful key design.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/hbase/reviews"><strong>HBase</strong></a><strong>:</strong> Reviewers use it for real-time read/write on very large datasets and near-real-time analytics, which fits time-series at volume; just note that aggregation/OLAP queries are slower, and it leans on the Hadoop stack.</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> Single-digit-ms latency and instant scaling make it viable for time-series if you're on Azure, with the usual caveat that RU/s cost needs planning for high-ingest workloads.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For time-series specifically, what mattered more in practice: raw write speed, or how well the partition/clustering design held up as data grew? Curious which of these stayed fast once the series got long.</p>

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


## Comments
### Comment 1

&lt;p&gt;We went with Cassandra for time-series a few years back and it held up fine for ingestion, but reads got painful as the series got longer and our access patterns changed. The append-friendly model is great until you need to query across a range you didn&#39;t model for. I&#39;m curious whether ScyllaDB actually helps with that or if you&#39;re still dealing with the same modeling constraints.&lt;/p&gt;

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





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