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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.
Optimizing PostgreSQL on Azure directly in Visual Studio Code
Tech Optimizer
July 23, 2026

Optimizing PostgreSQL on Azure directly in Visual Studio Code

Poor database performance can lead to missed SLAs, delayed releases, customer dissatisfaction, and lost revenue. Microsoft has enhanced PostgreSQL on Azure, transforming it into a fully managed platform that meets enterprise demands. Azure Database for PostgreSQL and the new Azure HorizonDB offer significant performance improvements, with HorizonDB being three times faster than self-managed PostgreSQL. The PostgreSQL extension for Visual Studio Code integrates performance management into development workflows, providing tools for managing PostgreSQL throughout its lifecycle. Key features include a server metrics dashboard that displays performance indicators, Azure Advisor recommendations for actionable insights, improved query plan visualization, and AI-assisted query analysis. The extension also enhances schema design and query formulation experiences, ensuring secure and governed interactions with PostgreSQL. Azure HorizonDB is in public preview, designed for AI-native workloads. These advancements aim to reduce friction, enhance clarity, and enable faster actions for enterprise teams managing PostgreSQL at scale.
Migrating mission-critical payments at Nubank to Amazon Aurora PostgreSQL
Tech Optimizer
July 22, 2026

Migrating mission-critical payments at Nubank to Amazon Aurora PostgreSQL

Nubank, a digital banking platform with approximately 135 million customers in Brazil, Mexico, and Colombia, faced challenges with its payment infrastructure due to inefficiencies in managing 7.5 TB of self-hosted PostgreSQL databases. Queries took over 13 minutes to execute, and replication lags exceeded four minutes during peak times. To address these issues, Nubank evaluated database solutions based on developer productivity, operational efficiency, performance reliability, and scalability. They selected Amazon Aurora PostgreSQL-Compatible Edition for its compatibility, performance improvements, and automated features. The migration to Aurora was facilitated by AWS Database Migration Service (AWS DMS), which minimized downtime and allowed for efficient transition. Post-migration, query performance improved significantly, with some queries executing up to 1,900 times faster, and overall end-to-end service latency decreased, enhancing customer experience. The migration resulted in a 25 percent cost reduction and ensured compliance with regulatory requirements.
Google boosts AlloyDB vector search with HNSW preview
Tech Optimizer
July 22, 2026

Google boosts AlloyDB vector search with HNSW preview

Google has introduced a preview of its columnar engine-accelerated HNSW feature for AlloyDB, enhancing vector search throughput by up to four times for users of the pgvector extension in PostgreSQL. This improvement is aimed at approximate nearest neighbor searches using HNSW across large datasets. AlloyDB utilizes a columnar engine that allows HNSW indexes to reside in memory, improving throughput and recall metrics. In benchmark tests on the GloVe 100 Angular dataset, queries per second increased by approximately 4.2x to 4.9x, with recall improving from about 0.78 to over 0.94 at a throughput of around 350 queries per second. The columnar engine's design bypasses traditional PostgreSQL buffer management bottlenecks, enhancing efficiency during graph traversal. The memory architecture is distinct from basic caching techniques, and the feature requires no changes to application code. Users must enable the columnar engine and index caching flags to utilize this feature. Benchmarks were conducted on an AlloyDB C4A machine with 16 virtual CPUs.
I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer
Tech Optimizer
July 21, 2026

I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer

The author transitioned from a data analyst role to a data engineer, creating a 12-month self-study roadmap focused on learning by building projects. The first project was a GitHub ETL pipeline, which evolved from a simple Python script to a more complex system using SQLite and GitHub Actions for automation. The author realized that the challenges of data engineering lay in system design and orchestration rather than just writing ETL logic. For the second project, the author built an automated RSS ingestion pipeline to explore engineering decisions in creating a reliable data pipeline. The project emphasized the importance of separating application responsibilities from orchestration tasks, leading to the use of Docker for consistent execution environments and Kestra for orchestration. The author validated each component of the project incrementally: first the Python ETL, then PostgreSQL, followed by Docker, and finally Kestra. This approach ensured that each layer functioned correctly before adding complexity. The final architecture included distinct responsibilities for each component: Python for data processing, PostgreSQL for data storage, Docker for execution, and Kestra for orchestration. The author learned that effective engineering involves considering system reliability, error handling, and observability, shifting the focus from merely writing code to designing entire systems. The experience transformed the author's mindset, emphasizing the importance of incremental building and the separation of execution and orchestration responsibilities.
Google Cloud boosts AlloyDB search for CJK text with Gemini
Tech Optimizer
July 16, 2026

Google Cloud boosts AlloyDB search for CJK text with Gemini

Google Cloud has enhanced AlloyDB by integrating AlloyDB AI Functions with Gemini models to improve search indexing for logographic languages like Chinese, Japanese, and Korean. This update addresses challenges in database search functionality, particularly for languages written in continuous scripts, where traditional PostgreSQL text parsing struggles. Developers can now invoke Gemini models directly from SQL to facilitate word segmentation and stop-word removal before creating full-text indexes, offering a more efficient alternative to traditional methods. The architecture maintains raw text, segmented text, search vectors, and embeddings in a single table, with generated columns ensuring automatic updates to search vectors and embeddings. A batching process using a PL/pgSQL stored procedure allows for efficient processing of extensive document collections by aggregating rows into arrays for model requests, thus reducing row-locking issues. Google Cloud provides tailored text search configurations for different datasets, suggesting a simple configuration for solely Chinese text and an English configuration for bilingual datasets. This preprocessing logic can also be applied during query execution, allowing user searches to be directed to Gemini for text segmentation and stop-word elimination. AlloyDB supports RUM indexes on search vectors, which store lexeme positions for computing relevance and word distance, enabling rapid query responses. Additionally, a hybrid search methodology combines full-text and vector search using a ScaNN index on embeddings, merging results through Reciprocal Rank Fusion. This development allows for robust multilingual search and vector retrieval solutions within the database environment, eliminating the need for external microservices.
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