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PostgreSQL Extensions Are an Architecture Decision, Not an Afterthought
Tech Optimizer
July 8, 2026

PostgreSQL Extensions Are an Architecture Decision, Not an Afterthought

A common issue in database migrations is the unplanned accumulation of extensions in PostgreSQL, leading to extension sprawl. Teams often install extensions without documenting the rationale, resulting in a complex web of dependencies that complicates future upgrades and removals. The installation process involves PostgreSQL accessing a control file that details the extension's version and dependencies, which can lead to multiple extensions being installed unintentionally. Upgrading and removing extensions are often neglected, causing risks such as the loss of dependent objects. Extensions typically default to the public schema, which can become cluttered; relocating them to dedicated schemas can improve organization. Trusted Language Extensions (TLE) allow non-privileged users to utilize procedural languages in managed databases without needing superuser access. Key extensions recommended for use include pg_stat_statements, pg_trgm, hstore, citext, and PostGIS, each serving specific use cases. Proper governance is essential for managing extensions, including documenting their purpose, ownership, and dependencies, to prevent operational surprises.
The TechBeat: How Small Postgres Metadata Tables Quietly Throttle Your Largest Queries (7/6/2026)
Tech Optimizer
July 7, 2026

The TechBeat: How Small Postgres Metadata Tables Quietly Throttle Your Largest Queries (7/6/2026)

- Websites detect VPN usage through factors like IP reputation, DNS leaks, WebRTC, and browser fingerprints, with strategies to mitigate tracking. - Slow Postgres dashboards may be caused by stale metadata statistics rather than the fact table, with solutions provided to rectify these issues. - Spec-driven development enhances the reliability of AI coding agents through structured specifications, workflows, reusable skills, and proof. - A comparison of usage-based billing platforms for 2026 includes Credyt, Metronome, Orb, Lago, Stripe Billing, and Chargebee. - Loop Engineering is a rising trend in AI workflows expected to dominate in 2026, though challenges may accompany it. - ONLYOFFICE released version 3.7 of DocSpace Developer, introducing AI file generation, new webhook events, expanded plugin tools, and enhanced form workflows. - ANY.RUN's data inspection tool offers SOC teams comprehensive browser visibility for quicker phishing investigations. - AI systems are reducing obvious hallucinations, but deeper structural failures remain a risk for professionals. - A vendor audit revealed supply chain vulnerabilities in AI, including unpinned models and the use of trust_remote_code=True. - Developers using OpenAI Codex should avoid overloading a single conversation with tasks to maintain performance, recommending the use of subagents. - Barriers to AI adoption are not purely technological, as shared by insights from discussions with 500 individuals. - Guidance on creating production AI agent loops emphasizes state management, tool governance, evidence verification, and domain-specific controls. - Dawnguard launched a security architecture automation platform with million in pre-seed funding to address AI code vulnerabilities. - The AI boom is driven by complex memory architecture rather than GPUs, influencing consumer electronics pricing strategies. - The origins of functional programming trace back to John McCarthy's practical needs for differentiating expressions. - A guide for AI startup founders addresses common pitfalls in understanding AI agents and the autonomy trap for developing reliable systems.
Why most AI projects fail: It’s infrastructure and people
Tech Optimizer
July 6, 2026

Why most AI projects fail: It’s infrastructure and people

AI technology faces significant criticism for its low success rates in delivering business results, with studies indicating a 95% failure rate for enterprise AI solutions and only 9% of organizations in Europe, the Middle East, and Africa achieving measurable outcomes from AI initiatives. Four main shortcomings hinder the transition of AI prototypes to production: 1. Deployment Flexibility: Prototyping environments often lack the necessary flexibility for large-scale production deployment, particularly in regulated sectors. 2. Data Sovereignty: Production transitions can complicate data sovereignty at enterprise and regional levels. 3. Reliability: High availability is crucial for production environments, but vendor-managed platforms may not guarantee seamless upgrades or hardware swaps without downtime. 4. Disconnect in Tool Selection: Developers often choose tools for prototyping without considering production implications, leading to difficulties in scaling. The shortage of database administrators (DBAs) is exacerbated by the increasing use of AI tools, with 84% of developers utilizing them according to a 2025 survey. To address these challenges, Merrick suggests leveraging AI DBA agents to support human DBAs and improve database management efficiency. He emphasizes the need for both robust data infrastructure and enhanced operational support to improve the success rates of AI prototypes.
From monolith to Lakebase to LTAP: rethinking the database from storage up
Tech Optimizer
July 2, 2026

From monolith to Lakebase to LTAP: rethinking the database from storage up

Sixteen years ago, the author began their PhD at UC Berkeley and was advised to focus on analytics rather than OLTP databases, which were considered solved. This led to the creation of Apache Spark and Databricks. However, during the development of Databricks, it was found that OLTP databases were not solved problems; they were difficult to scale and fragile. This realization led to the development of Lakebase, a serverless Postgres database designed with modern technology. Lakebase architecture separates the write-ahead log (WAL) and data files into independent, scalable services. The WAL is externalized to a service called SafeKeeper, which uses Paxos-based replication for durability, while data files are managed by PageServer, which stores them in cloud object storage. This design addresses challenges such as data loss, scaling, and performance interference between transactional and analytical workloads. Lakebase maintains compatibility with Postgres, offers unlimited storage, serverless compute, durable writes, and simpler high availability. It also introduces LTAP (Lake Transactional/Analytical Processing), which allows both transactional and analytical processing to operate on a single data copy in real time, eliminating the need for separate data copies and reducing costs. LTAP utilizes a unified storage layer that allows data to be materialized in both row and columnar formats, optimizing it for both transactional and analytical workloads. The system ensures that analytics can access the most current data without affecting transactional performance by using a log sequence number (LSN) to retrieve the latest changes. Unlike traditional CDC approaches, LTAP requires no explicit table replication, as all data is stored in a single governed copy. This architecture circumvents common issues faced by hybrid transactional/analytical processing (HTAP) systems, such as incomplete feature sets, lack of ecosystem support, and performance contention. The Lakebase architecture has unlocked capabilities like unlimited storage, elastic compute, durable writes, and instant branching, with ongoing developments anticipated in the future.
silver Android smartphone
Tech Optimizer
July 2, 2026

From monolith to Lakebase to LTAP: rethinking the database from storage up

Sixteen years ago, the author began their PhD at UC Berkeley, where they were advised to focus on analytics rather than OLTP databases, which were considered a solved problem. However, upon developing Databricks, they found that OLTP databases were cumbersome, challenging to scale, and fragile. This led to the creation of Lakebase, a serverless Postgres database designed to address these issues by externalizing the write-ahead log (WAL) and data files into independent services. Lakebase architecture separates the WAL into a distributed service called SafeKeeper and data files into another service called PageServer, allowing for improved durability, scalability, and performance. This architecture eliminates data loss risks associated with disk failures and misconfigurations, simplifies high availability, and allows for elastic compute resources. Lakebase also introduces LTAP (Lake Transactional/Analytical Processing), which enables transactions and analytics to operate on a single data copy in real-time, avoiding the need for separate data copies and reducing costs. The system retains Postgres's ACID semantics while storing data in open columnar formats, making it accessible to both transactional and analytical engines without the delays and complexities of traditional replication methods. The architecture allows for unlimited storage, serverless compute, and instant branching, while ensuring that analytics can read the most current transactional data without affecting transactional workloads. LTAP aims to unify transactional and analytical processing by leveraging distinct compute engines for each workload while integrating at the storage layer.
silver Android smartphone
Tech Optimizer
July 2, 2026

EDB Named a Leader in Multimodel Data Platforms Evaluation

EDB has been recognized as a Leader in The Forrester Wave: Multimodel Data Platforms, Q2 2026, with EDB Postgres AI (EDB PG AI) achieving the highest scores in Vision, Innovation, Roadmap, and Partner Ecosystem criteria. EDB PG AI integrates transactional, analytical, and AI workloads into a unified platform, supporting open-source frameworks and enabling various deployment options. The platform features governance at the data layer and is designed for operational efficiency, allowing organizations to implement sovereign AI quickly. EDB PG AI can be deployed on-premises, in hybrid environments, or across cloud infrastructures, backed by partnerships with companies like Dell, IBM, and NVIDIA.
EDB named Forrester Leader in multimodel data platforms
Tech Optimizer
July 2, 2026

EDB named Forrester Leader in multimodel data platforms

EDB has been recognized as a Leader in Forrester's Multimodel Data Platforms evaluation for Q2 2026 for its EDB Postgres AI platform, receiving the highest scores in Vision, Innovation, Roadmap, and Partner Ecosystem. The platform is designed to manage mixed translytical and AI workload demands, offering flexibility in deployment across on-premises, hybrid, and multi-cloud environments. EDB's recent product update introduced agentic database and converged analytics functionalities, reportedly accelerating database tuning by up to tenfold and reducing analytics ownership costs by as much as 58%. The platform is supported by a partner ecosystem that includes Dell, IBM, NVIDIA, Red Hat, and Supermicro, which plays a crucial role in influencing database purchasing decisions. EDB's roadmap focuses on advancements in GPU-accelerated workloads, semantic intelligence, governance, and knowledge graph functionalities. The emphasis on sovereign deployment aligns with organizations' needs for control over sensitive data amidst stricter regulations.
EDB Postgres AI Launch: Shifting To Autonomous, Sovereign Data For The AI Era
Tech Optimizer
June 26, 2026

EDB Postgres AI Launch: Shifting To Autonomous, Sovereign Data For The AI Era

EnterpriseDB (EDB) introduced the EDB Postgres AI (EDB PG AI) platform on June 23, 2026, designed for AI applications to operate directly on live data rather than outdated copies from cloud data lakes. The platform allows organizations to host AI models, live data, and enterprise regulations within their infrastructure, reducing vendor lock-in and protecting regulated data. The EDB PG AI platform features a self-optimizing system that transforms PostgreSQL into an autonomous database, monitoring over 200 metrics for automated tuning and scaling. EDB claims performance troubleshooting can be up to 10 times faster, with issues resolved in minutes instead of the traditional 60 to 90 minutes. It also includes a converged query interface that integrates various data types into a unified engine, enabling AI agents to access authorized live data. An agent governance framework will be introduced in late 2026 to address risks associated with AI operations. EDB collaborates with IBM Power for a robust AI-ready infrastructure and integrates Red Hat Ansible Automation Platform for enhanced management capabilities.
EDB launches agentic Postgres AI with governance tools
Tech Optimizer
June 26, 2026

EDB launches agentic Postgres AI with governance tools

EDB has introduced new features for its Postgres AI platform, including an agentic database and converged analytics capabilities, allowing enterprises to run AI agents alongside transactional workloads on a unified PostgreSQL foundation. The platform includes governance tools that position control mechanisms at the data layer and integrates AI processing with operational data, enabling businesses to connect live records with AI systems without transferring sensitive information. The agentic database can monitor over 200 metrics, identify issues, suggest changes, and apply fixes automatically based on user-defined policies. It consolidates various data types through a single SQL interface, significantly accelerating database tuning processes and enhancing application performance. EDB has also expanded its analytics capabilities with a zero-ETL architecture for real-time analysis and large-scale warehousing. EDB PG AI for ClickHouse targets real-time analysis, while EDB PG AI for WarehousePG focuses on historical analysis at petabyte scale. The platform claims up to 30 times faster query performance compared to legacy systems and improved scaling efficiency. EDB's platform integrates vector search and retrieval for AI agents, demonstrating lower query latency and higher retrieval accuracy than competitors. NTT East is using EDB PG AI for AI-driven network operations, while the governance feature manages agent access at the data querying point using native Postgres roles and row-level security. The platform can be deployed on-premises, in hybrid environments, or across cloud infrastructures, with partnerships including Dell, IBM, Nvidia, Red Hat, and Supermicro.
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