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    <title>topic Databricks Lake base - Modern Medical Inventory Management with Databricks Apps &amp;amp; Lake base in Community Articles</title>
    <link>https://community.databricks.com/t5/community-articles/databricks-lake-base-modern-medical-inventory-management-with/m-p/153899#M1147</link>
    <description>&lt;P&gt;Databricks Lake base is becoming the real time&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;operational database&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;for large enterprises demanding&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;sub second latency, strict consistency&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;and&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;high concurrency&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;workloads&lt;STRONG&gt;.&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;It stands as a purpose-built high speed transactional &amp;amp; serving layer based on open format in storage that unifies high throughput ingestion, low latency retrieval with rich transactional integrity supporting various cases for&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;Enterprise Apps &amp;amp; Agents.&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;Lake base eliminates the traditional compromise between scale and speed delivering a system that ingests and serves data with deterministic performance.&lt;/P&gt;&lt;P&gt;&lt;U&gt;&lt;STRONG&gt;Medical Device Inventory Management&lt;/STRONG&gt;&lt;/U&gt;&lt;/P&gt;&lt;P&gt;Used it for a leading&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;Medical Device Manufacturing&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;Enterprise that faced a critical bottleneck in its order inventory management system&lt;STRONG&gt;.&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;Regulatory traceability,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;SLA commitments&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;and supply chain volatility required real time processing of thousands of concurrent inventory updates and fulfilment checks with 200ms response times. Legacy traditional architectures relied on fragmented microservices causing stale stock counts, reconciliation delays and frequent over stocking leading to challenges.&lt;/P&gt;&lt;P&gt;Implemented Lake base &amp;amp; Databricks Apps to replace the multi system legacy patchwork with the optimized Apps &amp;amp; OLTP plane. Several capabilities made this transformation possible such as&lt;STRONG&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;Apps Integration, Sync, Instant Branching &amp;amp; Serverless features.&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;The enterprise processed&amp;nbsp;&lt;STRONG&gt;250 concurrent orders&amp;nbsp;&lt;/STRONG&gt;per second while maintaining real time inventory reconciliation across&amp;nbsp;&lt;STRONG&gt;48 distribution centers.&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;Each order triggers a transactional stock check &amp;amp; reservation with a single query call eliminating round trips. Inventory drift dropped to near zero and cycle times improved by 70%.&lt;/P&gt;&lt;P&gt;&lt;U&gt;&lt;STRONG&gt;Order Management Strategy&lt;/STRONG&gt;&lt;/U&gt;&lt;/P&gt;&lt;UL&gt;&lt;LI&gt;&lt;STRONG&gt;Medical Store Managers&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;connected to the Databricks Apps that served as the application layer interacting with Lake base in Real Time. Lake base served as the operational &amp;amp; transactional data plane handling&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;Order Management Processes&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;(Idempotent medical device order inserts, updates &amp;amp; deletes with exactly once semantics) for device inventory reservations &amp;amp; Lakehouse handled the Analytics Plane.&lt;/LI&gt;&lt;LI&gt;&lt;U&gt;&lt;STRONG&gt;Lake base to Lakehouse Sync&lt;BR /&gt;&lt;/STRONG&gt;&lt;/U&gt;&lt;STRONG&gt;Change Capture&lt;/STRONG&gt;: Every change in Lake base emitted a structured event to the Lakehouse for Analytics. Lakehouse Sync enabled continuous &amp;amp; low latency replication of Lake base tables into Unity Catalog managed Delta tables by capturing row level changes and writing them as SCD Type 2 data. Each change is appended as a new row with full history of row changes over time. No extra pipelines or jobs is necessary as its a native feature.&lt;U&gt;&lt;BR /&gt;&lt;/U&gt;&lt;STRONG&gt;Unified Governance&lt;/STRONG&gt;: Unity Catalog enforces row/column level security and audit lineage ensuring compliance.&lt;/LI&gt;&lt;/UL&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="balajij8_0-1775736870895.png" style="width: 400px;"&gt;&lt;img src="https://community.databricks.com/t5/image/serverpage/image-id/25844iCED0C9AA0AB15DD9/image-size/medium?v=v2&amp;amp;px=400" role="button" title="balajij8_0-1775736870895.png" alt="balajij8_0-1775736870895.png" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&lt;EM&gt;Lake base handled the high-velocity &amp;amp; low-latency transactional workloads while the Lakehouse used the same data for deep analytics under a single plane.&lt;/EM&gt;&lt;/P&gt;</description>
    <pubDate>Thu, 09 Apr 2026 12:16:09 GMT</pubDate>
    <dc:creator>balajij8</dc:creator>
    <dc:date>2026-04-09T12:16:09Z</dc:date>
    <item>
      <title>Databricks Lake base - Modern Medical Inventory Management with Databricks Apps &amp; Lake base</title>
      <link>https://community.databricks.com/t5/community-articles/databricks-lake-base-modern-medical-inventory-management-with/m-p/153899#M1147</link>
      <description>&lt;P&gt;Databricks Lake base is becoming the real time&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;operational database&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;for large enterprises demanding&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;sub second latency, strict consistency&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;and&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;high concurrency&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;workloads&lt;STRONG&gt;.&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;It stands as a purpose-built high speed transactional &amp;amp; serving layer based on open format in storage that unifies high throughput ingestion, low latency retrieval with rich transactional integrity supporting various cases for&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;Enterprise Apps &amp;amp; Agents.&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;Lake base eliminates the traditional compromise between scale and speed delivering a system that ingests and serves data with deterministic performance.&lt;/P&gt;&lt;P&gt;&lt;U&gt;&lt;STRONG&gt;Medical Device Inventory Management&lt;/STRONG&gt;&lt;/U&gt;&lt;/P&gt;&lt;P&gt;Used it for a leading&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;Medical Device Manufacturing&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;Enterprise that faced a critical bottleneck in its order inventory management system&lt;STRONG&gt;.&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;Regulatory traceability,&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;SLA commitments&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;and supply chain volatility required real time processing of thousands of concurrent inventory updates and fulfilment checks with 200ms response times. Legacy traditional architectures relied on fragmented microservices causing stale stock counts, reconciliation delays and frequent over stocking leading to challenges.&lt;/P&gt;&lt;P&gt;Implemented Lake base &amp;amp; Databricks Apps to replace the multi system legacy patchwork with the optimized Apps &amp;amp; OLTP plane. Several capabilities made this transformation possible such as&lt;STRONG&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;Apps Integration, Sync, Instant Branching &amp;amp; Serverless features.&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;The enterprise processed&amp;nbsp;&lt;STRONG&gt;250 concurrent orders&amp;nbsp;&lt;/STRONG&gt;per second while maintaining real time inventory reconciliation across&amp;nbsp;&lt;STRONG&gt;48 distribution centers.&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;Each order triggers a transactional stock check &amp;amp; reservation with a single query call eliminating round trips. Inventory drift dropped to near zero and cycle times improved by 70%.&lt;/P&gt;&lt;P&gt;&lt;U&gt;&lt;STRONG&gt;Order Management Strategy&lt;/STRONG&gt;&lt;/U&gt;&lt;/P&gt;&lt;UL&gt;&lt;LI&gt;&lt;STRONG&gt;Medical Store Managers&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;/STRONG&gt;connected to the Databricks Apps that served as the application layer interacting with Lake base in Real Time. Lake base served as the operational &amp;amp; transactional data plane handling&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;&lt;STRONG&gt;Order Management Processes&lt;/STRONG&gt;&lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;(Idempotent medical device order inserts, updates &amp;amp; deletes with exactly once semantics) for device inventory reservations &amp;amp; Lakehouse handled the Analytics Plane.&lt;/LI&gt;&lt;LI&gt;&lt;U&gt;&lt;STRONG&gt;Lake base to Lakehouse Sync&lt;BR /&gt;&lt;/STRONG&gt;&lt;/U&gt;&lt;STRONG&gt;Change Capture&lt;/STRONG&gt;: Every change in Lake base emitted a structured event to the Lakehouse for Analytics. Lakehouse Sync enabled continuous &amp;amp; low latency replication of Lake base tables into Unity Catalog managed Delta tables by capturing row level changes and writing them as SCD Type 2 data. Each change is appended as a new row with full history of row changes over time. No extra pipelines or jobs is necessary as its a native feature.&lt;U&gt;&lt;BR /&gt;&lt;/U&gt;&lt;STRONG&gt;Unified Governance&lt;/STRONG&gt;: Unity Catalog enforces row/column level security and audit lineage ensuring compliance.&lt;/LI&gt;&lt;/UL&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="balajij8_0-1775736870895.png" style="width: 400px;"&gt;&lt;img src="https://community.databricks.com/t5/image/serverpage/image-id/25844iCED0C9AA0AB15DD9/image-size/medium?v=v2&amp;amp;px=400" role="button" title="balajij8_0-1775736870895.png" alt="balajij8_0-1775736870895.png" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&lt;EM&gt;Lake base handled the high-velocity &amp;amp; low-latency transactional workloads while the Lakehouse used the same data for deep analytics under a single plane.&lt;/EM&gt;&lt;/P&gt;</description>
      <pubDate>Thu, 09 Apr 2026 12:16:09 GMT</pubDate>
      <guid>https://community.databricks.com/t5/community-articles/databricks-lake-base-modern-medical-inventory-management-with/m-p/153899#M1147</guid>
      <dc:creator>balajij8</dc:creator>
      <dc:date>2026-04-09T12:16:09Z</dc:date>
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