NewApp Digest
search bot

cloud-native

silver Android smartphone
Tech Optimizer
August 10, 2026

Microsoft’s PostgreSQL alternative, HorizonDB: Worth the wait?

AWS's Aurora PostgreSQL service became generally available in 2017 and has evolved into an AI-ready offering with features like vector search and integrations with Amazon Bedrock. Google Cloud launched AlloyDB in 2022, which includes AlloyDB AI with vector search and model interaction. Databricks introduced Lakebase, available on AWS and Azure in 2023, while Snowflake released Snowflake Postgres in February 2026. Microsoft announced HorizonDB at Ignite in November 2025, featuring disaggregated compute and storage, a database-as-log design, native vector search, and integration with Foundry and Fabric.
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.
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.
What Is Serverless PostgreSQL?
Tech Optimizer
July 12, 2026

What Is Serverless PostgreSQL?

Serverless PostgreSQL is a fully managed cloud database model that separates compute and storage, allowing them to scale independently and automatically based on demand. It eliminates the need for manual infrastructure provisioning and capacity planning, charging only for active usage. Unlike traditional PostgreSQL setups, which require continuous resource allocation and manual scaling, serverless PostgreSQL provisions resources on demand and can scale down to zero during idle periods. Serverless PostgreSQL integrates with serverless compute platforms, enabling analytical queries to access the same data within a unified architecture. Key differences between traditional and serverless PostgreSQL include manual versus automatic provisioning and scaling, fixed versus usage-based billing, and high versus reduced operational overhead. Lakebase architecture is an emerging model that combines transactional databases with lakehouse foundations, allowing operational and analytical workloads to coexist on a single platform. This architecture minimizes data duplication and simplifies access, enhancing data management and analysis. Serverless PostgreSQL operates on a cloud-native architecture that enhances efficiency by allowing compute and storage to scale autonomously. It features scale-to-zero behavior, where compute resources are suspended when inactive and reactivated upon new queries. Major providers include Databricks Lakebase, Amazon Aurora Serverless v2, and Neon, each offering varying capabilities and integrations. Pricing for serverless PostgreSQL typically includes charges for compute resources, storage, and data transfer, with costs fluctuating based on workload activity. Cold start latency is a performance consideration, as reactivating compute resources can introduce delays. Strategies to mitigate this include keeping resources partially active or selecting providers with minimal cold start impacts. Serverless PostgreSQL is well-suited for OLTP workloads, while lakebase architecture is better for AI development, variable workloads, and environments requiring rapid iteration. Setting up serverless PostgreSQL involves choosing a provider, creating a database instance, and configuring access settings. It can also be used alongside serverless compute platforms for analytics, further extending its capabilities.
9 Best Antivirus Software On G2: My Top Picks
Tech Optimizer
June 11, 2026

9 Best Antivirus Software On G2: My Top Picks

Antivirus software can become overwhelming for organizations due to alert fatigue shortly after deployment. Analysts often struggle to prioritize notifications, leading to the mismanagement of legitimate tools and unclear incident timelines. A review of nine antivirus solutions based on G2's Winter 2026 Grid® Report identified the following top performers: 1. ESET PROTECT: Best for machine learning-driven endpoint protection; offers enterprise-grade security with a free trial available. 2. Sophos Endpoint: Best for ransomware prevention; provides centralized policy control with a free trial available. 3. ThreatDown: Cost-effective EDR with MDR flexibility; combines antivirus and endpoint detection with a free trial available. 4. CrowdStrike Falcon: Best for large-scale enterprise threat prevention; cloud-native platform with subscription-based pricing and a free trial available. 5. Check Point Harmony Endpoint: Best for unified endpoint and zero-trust protection; integrates malware prevention and phishing defense with a free trial available. 6. Microsoft Defender for Endpoint: Best for Microsoft-native environments; deeply integrated with Microsoft 365, licensed through enterprise agreements. 7. Kaspersky AntiVirus: Best for traditional malware protection; provides real-time protection against various threats. 8. SentinelOne: Best for autonomous AI-driven endpoint response; features automated remediation and ransomware rollback with a free trial available. 9. FortiClient: Best for Fortinet-centric environments; offers VPN access and security policy enforcement with a free basic client available. The analysis highlighted that effective antivirus solutions prioritize behavioral analysis over traditional signature-based detection, minimize false positives, and maintain low system impact during operation. Key factors for evaluating antivirus software include threat detection accuracy, centralized visibility, response capabilities, and deployment stability.
Azure HorizonDB Enters Public Preview: Web IQ Already Powers Copilot and ChatGPT
Tech Optimizer
June 6, 2026

Azure HorizonDB Enters Public Preview: Web IQ Already Powers Copilot and ChatGPT

Microsoft announced the public preview of Azure HorizonDB, a fully managed PostgreSQL-compatible database designed for agentic AI workloads, during Microsoft Build 2026 in San Francisco. HorizonDB features a "database-as-logs" architecture, allowing for sub-millisecond multi-zone commit latency and independent scaling of compute and storage. It incorporates a Rust-based storage engine, native DiskANN vector search, and in-database AI model invocation. Additionally, Microsoft launched Web IQ, a web-grounding API layer integrated into Microsoft Copilot and OpenAI's ChatGPT, which provides passage-level structured evidence objects rather than full documents. Web IQ is model-agnostic and aims to enhance information density and reduce costs. Both services are currently in limited availability, with HorizonDB open for preview signups across five Azure regions.
10 GitHub Repositories for Modern Database Systems and Tools
Tech Optimizer
June 2, 2026

10 GitHub Repositories for Modern Database Systems and Tools

Databases have evolved from merely storing application records to supporting real-time analytics, embedded SQL, caching, monitoring, replication, AI agent memory, and comprehensive application backends. Numerous open-source GitHub repositories have emerged, allowing developers to experiment with and deploy self-managed servers. 1. ClickHouse: A real-time analytics database management system for fast analytical queries on large datasets, ideal for dashboards and business intelligence workloads. 2. DuckDB: An in-process analytical SQL database designed for local environments, suitable for data scientists and analysts needing rapid SQL-based analytics. 3. Supabase: A Postgres development platform offering a dedicated Postgres database and tools for authentication, APIs, and storage, popular for web and mobile applications. 4. Redis: A high-speed in-memory data store for caching, real-time applications, and session storage, known for its exceptional performance and support for various data structures. 5. Prometheus: A monitoring system and time series database for collecting and querying metrics, essential for observability in production systems. 6. Vitess: A database clustering system for horizontally scaling MySQL, addressing sharding, routing, and replication challenges for large deployments. 7. LiteFS: A FUSE-based file system for replicating SQLite databases across clusters, extending SQLite's capabilities into distributed environments. 8. OpenViking: An open-source context database for AI agents, managing memory and resources through a file system-like structure. 9. pgAdmin: An open-source administration platform for PostgreSQL, providing a graphical interface for managing databases and writing queries. 10. Adminer: A lightweight database management tool in a single PHP file, offering simple deployment and administration for various database systems. The modern database ecosystem is crucial for building reliable, real-time web applications, with tools like ClickHouse and DuckDB excelling in analytics, while Supabase and Redis enhance application development. Prometheus, Vitess, and LiteFS address monitoring, scaling, and replication needs, and OpenViking supports AI applications. Starting with DuckDB, Supabase, and Redis is recommended for beginners, while exploring ClickHouse, Prometheus, Vitess, and pgAdmin is beneficial for production systems.
Search