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    <title>InfoQ - Time Series Data - Articles</title>
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      <title>Article: Time-Series Storage: Design Choices That Shape Cost and Performance</title>
      <link>https://www.infoq.com/articles/time-series-storage-design/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Time+Series+Data-articles</link>
      <description>&lt;img src="https://res.infoq.com/articles/time-series-storage-design/en/headerimage/Time-Series-Storage-Design-Choices-That-Shape-Cost-and-Performance-header-1778155792101.jpg"/&gt;&lt;p&gt;Every time-series database makes a set of storage design decisions: how to lay out rows, when to compress, what to partition on. These decisions determine cost and query performance more than the choice of database itself. This article works through those fundamentals from first principles, using widely available tools like PostgreSQL and Apache Parquet to make each trade-off measurable.&lt;/p&gt; &lt;i&gt;By Nirmesh Khandelwal&lt;/i&gt;</description>
      <category>Big Data</category>
      <category>Time Series Data</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>article</category>
      <pubDate>Tue, 12 May 2026 09:00:00 GMT</pubDate>
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      <dc:creator>Nirmesh Khandelwal</dc:creator>
      <dc:date>2026-05-12T09:00:00Z</dc:date>
      <dc:identifier>/articles/time-series-storage-design/en</dc:identifier>
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