# AWS Database Migration Service AWS cloud service for migrating and replicating databases and other data stores. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## We Cut Cloud Waste Before Touching Cluster Sizes: Lessons from Running a Data Platform DevFeed: [We Cut Cloud Waste Before Touching Cluster Sizes: Lessons from Running a Data Platform]() Original publisher: [Read original article]() Author: Deepika Saini Published: 2026-09-07T06:33:31Z Content type: article Language: en Sources: [Housing.com]() Topics: [BigQuery](), [databricks](), [Optimization](), [AWS Database Migration Service](), [data-platforms](), [Platform Engineering](), [migration](), [Cloud]() Tags: [bigquery](), [cloud-computing](), [cost](), [cost-optimization](), [data-engineering](), [data-platforms](), [databricks](), [delta-lake](), [finops](), [infrastructure](), [migration](), [optimization](), [platform-engineering]() ### AI overview This article explains how a data platform team reduced cloud costs by removing obsolete BigQuery data, adjusting Delta Lake retention, right-sizing DMS infrastructure, identifying unmonitored Databricks jobs, and standardizing pipeline onboarding and cost alerts. It reports that DMS costs were cut by over 50% and that retention was reduced from 90 days to 7 days for appropriate workloads after operational validation. ### Source excerpt How orphaned BigQuery storage, Delta retention, DMS right-sizing, and Databricks System Tables became our biggest cloud cost wins. The biggest cloud cost optimization we made wasn't shrinking clusters.It was deleting data we'd forgotten we were paying for.Like most teams, our first instinct was to tune infrastructure first. Instead, we discovered a treasure trove of hidden costs: orphaned BigQuery datasets, 90-day Delta retention, 24-hour jobs no one monitored, and DMS infrastructure that no longer matched business needs.We stopped treating cloud bills as a finance problem and started treating them as a platform engineering problem.30-second takeaway Why deleting forgotten data saved more than shrinking clusters. How we cut DMS costs by over 50%. How Databricks System Tables exposed hidden 24-hour jobs. How config.metadata standardized pipeline onboarding. How weekly Slack alerts turned cost optimization into a habit. Section 1: Storage Was Our Biggest Leak -- We Were Paying to Store Data Nobody Used This is the most overlooked cost on many data platforms. Storage duplication across platforms We had already migrated several workloads from BigQuery to Databricks. Large datasets were still sitting in BigQuery long after they had stopped serving production workloads - quietly generating storage costs month after month. Nothing failed. No alerts fired. Every month, we paid for storage that no longer served production workloads.A migration isn't complete until the old storage is decommissioned.The hidden cost of long retention The next surprise came from Delta Lake retention settings. Our workspace was configured to retain deleted table data and transaction history for 90 days to support time travel. Time travel is incredibly useful. But did every table need three months of historical recovery? Not really. We reduced retention to 7 days for appropriate workloads after validating operational needs. What changed immediately: Less storage tied up in deleted data. Faster clea ## SQL Server to Aurora PostgreSQL conversion with AI agents for AWS DMS DevFeed: [SQL Server to Aurora PostgreSQL conversion with AI agents for AWS DMS]() Original publisher: [Read original article]() Author: Mayur Aggarwal Published: 2026-09-01T15:19:13Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [AWS Database Migration Service](), [sql-server](), [Database Migration](), [Model Context Protocol](), [Databases]() Tags: [advanced-300](), [ai-agents](), [amazon-aurora](), [aws-database-migration-service](), [database-migration](), [how-to](), [mcp](), [postgresql](), [technical-how-to]() ### AI overview Tutorial on using AI agents with AWS DMS Schema Conversion to convert SQL Server schemas to Aurora PostgreSQL through natural-language workflows. ### Source excerpt Learn how to use AI agents with AWS DMS Schema Conversion to orchestrate SQL Server to Amazon Aurora PostgreSQL schema conversion through natural language. See how the conversion engine processes T-SQL, how the agent drives the workflow, and how to interpret and resolve CRITICAL action items using decision frameworks for common incompatibilities. ## Migrate Oracle Materialized Views with AWS DMS and Fast Refresh DevFeed: [Migrate Oracle Materialized Views with AWS DMS and Fast Refresh]() Original publisher: [Read original article]() Author: Abhilash Negi Published: 2026-08-18T16:36:11Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [AWS Database Migration Service](), [migration]() Tags: [advanced-300](), [analytics](), [architecture](), [aws](), [aws-database-migration-service](), [cloud](), [data](), [database](), [database-migration](), [datasets](), [how-to](), [latency](), [logs](), [migration](), [oracle](), [performance](), [real-time](), [replication](), [technical-how-to](), [testing](), [validation](), [workflow]() ### AI overview A tutorial for configuring Oracle materialized views with Fast Refresh and materialized view logs so AWS DMS can replicate incremental CDC changes instead of complete refreshes. ### Source excerpt Learn how to configure Oracle Materialized Views with Fast Refresh and materialized view logs to enable efficient, incremental AWS DMS change data capture (CDC) replication. This approach eliminates full-reload overhead and achieves near real-time migration of large, multi-table joined views to AWS. ## Rustic Witcher: Reimagining data anonymization DevFeed: [Rustic Witcher: Reimagining data anonymization]() Original publisher: [Read original article]() Author: Pavlos Petros Tournaris Published: 2024-07-01T11:10:44Z Content type: article Language: en Sources: [Blueground Engineering blog]() Topics: [data](), [pii](), [AWS Database Migration Service](), [Rust](), [datasets](), [Amazon S3](), [Kafka](), [Microservices](), [Testing]() Tags: [aws-database-migration-service](), [data](), [datasets](), [debezium](), [kafka](), [microservices](), [pii](), [rust](), [s3](), [testing]() ### AI overview Blueground describes replacing an increasingly slow production-data de-identification pipeline with Rustic Witcher, an internal Rust tool designed to support growing data volumes and database counts. The existing process used Debezium, Kafka, temporary Postgres storage, and S3, while the proposed approach considers AWS Database Migration Service exports as its input. ### Source excerpt At Blueground, the majority of our services require data for testing end-to-end user journeys, whether client-facing or operational. Consequently, we need to de-identify PII from production data to provide our engineers with realistic datasets. This approach ensures our pre-production systems mirror the behavior of production