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javacodegeeks.com /1 week ago

The Hidden Complexity of Schema Evolution: How Data Outlives the Code That Created It

A theoretical framework for thinking about backward and forward compatibility across Avro, Protobuf, JSON, and relational schemas — before a breaking change reaches production. Cod...

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aws.amazon.com /2 days ago

Provisioning DMS Schema Conversion via AWS CloudFormation

AWS Database Migration Service Schema Conversion (DMS SC) converts database objects across heterogeneous systems such as Oracle or SQL Server to PostgreSQL or MySQL. In this post,...

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databricks.com /1 month ago

Enabling Evolutionary Database Development: database branching with Lakebase, continued

This series revisits the methodolgy of Evolutionary Database Design, twenty years...

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databricks.com /1 month ago

Enabling Evolutionary Database Development: database branching with Lakebase

Why this series existsThe methodology described in Evolutionary Database Design and...

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databricks.com /1 week ago

Automatic Upgrades: best practice features for your lakehouse tables

Your Unity Catalog (UC) managed tables now get better on their own. Automatic (Auto)...

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javacodegeeks.com /1 month ago

The Database Migration Risk That Hibernate Silently Creates

Why hbm2ddl.auto=update is still in production codebases — and what to do about it Somewhere in a production codebase right now, Hibernate is quietly rewriting a database schema ba...

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databricks.com /1 month ago

Enabling Evolutionary Database Development: Database branching with Lakebase, the conclusion

The methodology described in Evolutionary Database Design and operationalized in Refactoring Databases: ...

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dzone.com /1 month ago

Liquibase: Database Change Management and Automated Deployments

Database schema management is one of the most challenging aspect for modern Devops practices. Liquibase gives an open-source, database-solution for tracking, versioning, and deploy...

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dev.to /2 weeks ago

D1 Schema Migrations with AI Agents: The DDL-in-Transaction Trap That Kills Zero-Downtime Deploys

Running an AI agent to execute your D1 migrations will silently wreck your database — unless you explicitly forbid it from wrapping DDL in a transaction. Claude Code, when handed...

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blogs.vmware.com /4 weeks ago

Embracing Schema Drift: Seamless Parquet Querying in Tanzu Greenplum, No Matter How Your Data Changes

<div><img width="300" height="157" src="https://blogs.vmware.com/wp-content/uploads/2026/06/vmw-blogtile-tanzu-greenplum-v2_fzmxrz.png" class...

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aws.amazon.com /6 days ago

Accelerate database modernization with agentic AI in AWS DMS Schema Conversion

Starting today, you can use AI agents to orchestrate entire AWS DMS Schema Conversion (DMS SC) workflows through natural language. An AI agent manages the full lifecycle, including...

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aws.amazon.com /1 month ago

How to migrate from Oracle to Amazon Aurora PostgreSQL using AWS CloudFormation (Part 1)

In this post, you learn how to use AWS DMS Schema Conversion to migrate Oracle schemas to PostgreSQL. AWS DMS Schema Conversion converts database schemas and code objects to format...

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infoq.com /1 month ago

Article: The Schema Proliferation Problem in Kafka and Flink Pipelines: How to Solve It

Schema proliferation builds slowly and gets expensive fast. One schema per event type feels right until there are ten tables, union queries spanning all of them, and a single field...

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dev.to /1 month ago

REDB inside, part 1 — the 13 tables the whole engine runs on (with the actual SQL, and why it's not EAV)

A couple of weeks ago I published the redb.Core intro post — what RedBase is at the API level, why I wrote it, what production looks like, the LINQ surface, what generated SQL look...

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dev.to /1 month ago

Versioned Schema Contracts in a Go CLI: How obs.v0.1 Prevents Silent Breaks

How embedding schema versions in every data file — observations, controls, output, baselines — enables forward compatibility, fail-fast loading, and contract testing without extern...

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dzone.com /1 month ago

Scaling Cloud Data Automation: A Practical Guide to Open Table Formats

When we talk about data analytics the way we set up our tables is really important. This is because it can make a difference, in how well our systems work and how fast they can gro...

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dev.to /1 month ago

Stop loading tables you never use: lazy-alchemy v2 for SQLAlchemy 2, async, Pydantic, and SQLModel

You have a database with 120 tables. Your application only ever touches 15 of them. SQLAlchemy doesn't care. At startup, it reflects all 120 вЂ” loading column metadata, constrain...

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