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Google Cloud explains SQL Server to PostgreSQL migration
Tech Optimizer
August 5, 2026

Google Cloud explains SQL Server to PostgreSQL migration

Google Cloud has introduced its Database Migration Service, which focuses on converting SQL Server stored procedures with multiple result sets into PostgreSQL code. The service uses an automated decision-making process to determine if a SQL Server procedure should be translated into a PostgreSQL stored procedure or function, based on the number of result sets and the presence of a scalar return value. Procedures with a single result set or scalar return value are converted into PostgreSQL stored procedures, while those with multiple result sets are transformed into functions returning a SETOF refcursor. In a healthcare reporting example, a master procedure retrieves patient details and may return multiple result sets along with a status integer. The PostgreSQL translation involves simpler child procedures as standard procedures and more complex routines as functions that open cursors sequentially. Scalar return values are handled by placing them in a dedicated cursor at the end of execution, changing how applications interact with the outputs. Testing of these translated objects must occur within an explicit transaction block due to PostgreSQL cursor lifecycle constraints. The user executes the function to generate cursor references and fetches data sequentially. Google Cloud's internal analysis classifies stored procedures by scanning for direct result sets and considers conditional logic and loops that may complicate result set counts. A directed graph of procedure calls is constructed to determine the result set classification. Database migrations often face challenges due to legacy application logic, especially with stored procedures designed to minimize database round trips. Successful code conversion is only part of the task, as teams must also adjust test harnesses and application data access layers for PostgreSQL's cursor management. Google Cloud's service categorizes SQL Server procedures into three types: no result sets, a single result set, or multiple/dynamic result sets.
Someone turned PostgreSQL into a city you can walk around in
Tech Optimizer
July 31, 2026

Someone turned PostgreSQL into a city you can walk around in

Nik Samokhvalov created PGSimCity, a visualizer for the open-source database PostgreSQL, inspired by the game SimCity. It allows users to navigate a city-like environment using gaming controls, where database transactions are represented as animations. The project originated from a prompt to Claude and involved 3.86 billion tokens of AI-assisted coding. PGSimCity is open source and has potential for adaptation to explain other complex technologies.
Generating Referentially Consistent Synthetic Test Data for PostgreSQL in .NET Application Modernization
Tech Optimizer
July 30, 2026

Generating Referentially Consistent Synthetic Test Data for PostgreSQL in .NET Application Modernization

Satyajit Gokhale, Aiswaryaa Venugopalan, Ruchi Agarwal, Nits Jeganathan, Khurram Khawaja, Vijay Mandadi, and Luke Huan contributed to this article. AWS Transform offers a .NET application modernization service that allows enterprise customers to migrate from Microsoft SQL Server to Amazon Aurora PostgreSQL Compatible Edition. This migration reduces licensing costs and enhances scalability while utilizing an open-source ecosystem. Schema conversion tools can assist in translating DDL to PostgreSQL-compatible definitions, but correctness must be verified by teams to ensure performance under real-world workloads. Traditional testing methods, such as using production data or manual checks, are not scalable and raise privacy concerns. Therefore, there is a need for realistic synthetic data generated at scale that adheres to the migrated schema's constraints. AWS Transform for SQL Server modernization employs an AI agent to generate constraint-respecting synthetic data at scale during the migration workflow. This process involves loading the PostgreSQL schema into a live database, constructing a dependency graph for data generation, and distributing tasks across multiple sub-agents. The agent validates each generated INSERT against the live database, ensuring compliance with constraints and providing error diagnostics for any violations. The synthetic data generation workflow consists of four stages: schema analysis, establishing generation order, distributing tasks across sub-agents, and validation with error recovery. The agent extracts metadata from the PostgreSQL schema, determines a valid table generation sequence, parallelizes data generation while maintaining referential integrity, and generates and validates data row-by-row against the live database. Each successful insertion enriches the context for subsequent generations, while errors are handled through structured diagnostics and iterative regeneration. The process ensures referential integrity and preserves the generation state, allowing for recovery from interruptions without data loss.
After rewriting SQLite in Rust, Turso turns its sights on Postgres
Tech Optimizer
July 29, 2026

After rewriting SQLite in Rust, Turso turns its sights on Postgres

Cloud database provider Turso is developing a Postgres-compatible implementation based on its SQLite-compatible database, which was created from scratch in Rust. CEO Glauber Costa believes Postgres can benefit from modernization for cloud-native applications. Turso's SQLite reimplementation, now called Turso, uses a virtual machine architecture that may eventually support other database frontends, including MySQL and Redis. The company initially forked SQLite into libSQL but later pivoted to a cloud service named Turso. They focused on a virtual machine architecture for their rewrite, which translates SQL queries into a custom bytecode language. Turso has developed a Postgres-compatible prototype named pgmicro, which aims to run existing applications with minimal modifications. The company is also working on a database-as-a-cloud service that will allow customers to use various database types on the Turso platform.
EDB Postgres® AI Outperforms Vector Databases, Lakehouses, and Document Stores for Agentic AI on Speed, Accuracy, and Cost
Tech Optimizer
July 29, 2026

EDB Postgres® AI Outperforms Vector Databases, Lakehouses, and Document Stores for Agentic AI on Speed, Accuracy, and Cost

EDB Postgres AI is a pioneering solution that integrates intelligence and data on a unified sovereign foundation, eliminating the need for ETL processes, data duplication, and separate vector stores. Independent benchmarks show that EDB Postgres AI outperforms competing platforms in speed, accuracy, and cost-effectiveness. A study by McKnight Consulting Group indicates that EDB Postgres AI excels in key performance metrics, including query latency, accuracy, cost, and data freshness, outperforming specialized vector databases and other managed Postgres platforms. EDB Postgres AI achieves median query latencies of 50 milliseconds at a scale of 50 million vectors, making it 80x faster than Databricks, 21x faster than MongoDB Atlas, and up to 2x faster than alternatives like Aurora and Crunchy Bridge. It also delivers the highest recall rates for core vector searches, surpassing competitors like MongoDB and Databricks. In tests involving concurrent retrievals, EDB Postgres AI completed a three-arm agent loop in 27 milliseconds, significantly faster than other platforms. In terms of cost efficiency, EDB Postgres AI offers 76x better price performance than Databricks, 34x better than MongoDB, and 23%–28% better than the nearest managed Postgres competitors when considering query speed. The architecture of EDB Postgres AI allows for the integration of vector, filtered, and full-text retrieval in a single query path, ensuring real-time data access and eliminating the need for separate systems.
'PGSimCity' shows how the PostgreSQL database actually works internally in a SimCity-like 3D environment.
Tech Optimizer
July 28, 2026

‘PGSimCity’ shows how the PostgreSQL database actually works internally in a SimCity-like 3D environment.

PGSimCity is a web application that provides a 3D visualization of PostgreSQL's architecture, allowing users to explore its internal operations during SQL query execution. The application features an interactive interface that guides users through the connection process to PostgreSQL, illustrating how client connections reach the Postmaster and backend processes. It highlights the significance of connection pooling and the impact of increasing connections on backend processes. In the 'Query Lab' section, SQL statements are transformed into execution plans, showcasing processes like Parser, Rewriter, Planner, and Executor, along with execution paths such as Seq Scan and Index Scan. The shared_buffers area represents PostgreSQL's buffer cache, optimizing performance by reading data in 8KiB pages. Visualizations depict data pages stored on disk, with pathways showing the movement of pages to shared_buffers. The Write-Ahead Log (WAL) mechanism records changes for recovery, and transaction commits wait for WAL records to be flushed. Checkpointing writes dirty pages to storage in batches, which can cause latency spikes. PostgreSQL uses Multi-Version Concurrency Control (MVCC) to manage updated rows and dead tuples, which are cleaned up by autovacuum processes. The application also illustrates replication, where WAL from the primary server is sent to the standby server to maintain data consistency. Overall, PGSimCity provides a comprehensive view of PostgreSQL's components and potential performance issues related to caching, WAL management, checkpointing, vacuuming, and replication.
How we pushed CDC into Postgres, and turned replication into clockwork
Tech Optimizer
July 24, 2026

How we pushed CDC into Postgres, and turned replication into clockwork

Making data from transactional databases accessible to analytical databases is essential in modern data architecture, but it faces challenges such as fragile tooling, high costs, and complex operations. Snowflake has developed a Postgres service that addresses these issues by reimagining Postgres replication. Postgres is a strong operational database, but its change data capture (CDC) capabilities need improvement. Many data pipelines are fragile due to complexities in managing continuous data flow, schema changes, snapshots, and failures. To enhance user experience with Snowflake Postgres, a complete reinvention of the replication process was necessary. A new feature called data mirroring is in public preview, allowing resilient data replication into Snowflake with low cost, minimal lag, and transactional consistency. This feature directly pushes changes from Postgres into Apache Iceberg™ tables in transactional batches, which are then automatically applied to Snowflake tables without extra infrastructure. Change data capture records changes from a transactional database for replay on another system, primarily using logical decoding in Postgres. This method decodes WAL records into logical operations, but the client bears the responsibility for subsequent steps like backfilling, managing schema changes, and handling failures. Built-in logical replication in Postgres only addresses some complexities. A limitation of logical decoding is that the external system does not recognize the state of Postgres, making it difficult to detect schema changes or correlate snapshots with changes. The solution is to push changes from Postgres into a data lake, specifically into Iceberg tables using compressed Parquet format, with object stores like Amazon S3 as the destination. The mirroring process uses a new Postgres extension called snowflake_cdc, which continuously pushes batches of changes into per-table change logs and a meta log. This extension understands the Postgres environment, coordinating schema changes and complex transactions while ensuring alignment between snapshots and changes.
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