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  <title><![CDATA[Vault Labs StreamNex]]></title>
  <link>https://vaultlabstreamnex.com/</link>
  <description><![CDATA[Vault Labs StreamNex builds and manages Kafka streaming infrastructure for engineering teams in North America and Europe. Based in Austin, TX. Since 2019.]]></description>
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    <title><![CDATA[Why we stopped offering hourly advisory calls]]></title>
    <link>https://vaultlabstreamnex.com/</link>
    <description><![CDATA[In 2023 we tried offering one-hour advisory calls for teams that were not ready for a full engagement. The calls were popular. They were also, in our view, not very useful. Here is what we learned and what we do instead.]]></description>
    <pubDate>2026-06-30</pubDate>
  </item>
  <item>
    <title><![CDATA[What we found in six Kafka audits this year]]></title>
    <link>https://vaultlabstreamnex.com/</link>
    <description><![CDATA[We completed six pipeline audits in the first four months of 2026. The same three issues appeared in five of them. This is not a coincidence. Here is what they were and why they keep showing up.]]></description>
    <pubDate>2026-05-14</pubDate>
  </item>
  <item>
    <title><![CDATA[Kafka consumer lag: what it means and when to worry]]></title>
    <link>https://vaultlabstreamnex.com/notes/kafka-consumer-lag-explained.html</link>
    <guid>https://vaultlabstreamnex.com/notes/kafka-consumer-lag-explained.html</guid>
    <description><![CDATA[Consumer lag is one of the most-watched metrics in any Kafka deployment, and one of the most misread. A lag number on its own tells you almost nothing. What matters is whether that lag is growing, stable, or shrinking, and at what rate. This guide covers how to read consumer lag accurately, what causes it to grow, and how to set thresholds that page you when something is actually wrong.]]></description>
    <pubDate>2026-04-10</pubDate>
  </item>
  <item>
    <title><![CDATA[Dead-letter queue design for Kafka pipelines]]></title>
    <link>https://vaultlabstreamnex.com/notes/dead-letter-queue-design-kafka.html</link>
    <guid>https://vaultlabstreamnex.com/notes/dead-letter-queue-design-kafka.html</guid>
    <description><![CDATA[A dead-letter queue is where messages go when your consumer cannot process them. Done well, it gives you a recoverable record of every failure. Done badly, it becomes a black hole where data disappears quietly. This guide covers the decisions that matter when designing a dead-letter queue for a Kafka pipeline.]]></description>
    <pubDate>2026-02-18</pubDate>
  </item>
  <item>
    <title><![CDATA[Moving from batch ETL to streaming: what to expect]]></title>
    <link>https://vaultlabstreamnex.com/notes/batch-to-streaming-migration-steps.html</link>
    <guid>https://vaultlabstreamnex.com/notes/batch-to-streaming-migration-steps.html</guid>
    <description><![CDATA[Moving from batch ETL to a streaming architecture is not a refactor. It is a rethink of how your system handles time, ordering, and failure. We have run six of these migrations since 2019. The technical work is rarely the hard part. The hard part is the assumptions your existing system makes that nobody has written down.]]></description>
    <pubDate>2026-01-25</pubDate>
  </item>
  <item>
    <title><![CDATA[Kafka partition count: how to choose and when to change it]]></title>
    <link>https://vaultlabstreamnex.com/notes/kafka-partition-tuning-guide.html</link>
    <guid>https://vaultlabstreamnex.com/notes/kafka-partition-tuning-guide.html</guid>
    <description><![CDATA[Partition count is one of the first decisions you make when creating a Kafka topic, and one of the hardest to change later. The conventional advice is to partition generously because you can always add partitions but you cannot remove them. That advice is not wrong, but it is incomplete. Over-partitioned topics have real costs that accumulate quietly.]]></description>
    <pubDate>2026-03-05</pubDate>
  </item>
  <item>
    <title><![CDATA[When to build a custom Kafka Connect connector]]></title>
    <link>https://vaultlabstreamnex.com/notes/kafka-connect-custom-connector-when-to-build.html</link>
    <guid>https://vaultlabstreamnex.com/notes/kafka-connect-custom-connector-when-to-build.html</guid>
    <description><![CDATA[The Confluent Hub lists over 200 connectors. For common sources like PostgreSQL, S3, and Salesforce, the off-the-shelf options are mature and well-maintained. For everything else, the picture is more complicated. This guide covers how to evaluate an existing connector before committing to it, and what to expect if you decide to build your own.]]></description>
    <pubDate>2026-05-02</pubDate>
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