database Archives

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.

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.

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.

Winsage

July 18, 2026

Windows 11 Secure Boot certificates have expired, and Microsoft says what to do next

Microsoft held its OEM Secure Boot Office Hours event on July 15, where engineers collaborated with OEM representatives from companies like Acer, Asus, Cisco, Dell, and HP. IT administrators were able to ask live questions about the Secure Boot 2023 rollout. The discussion thread became a detailed technical record, especially following the expiration of the first certificates three weeks prior. Concerns raised by IT admins included BitLocker recovery loops, stuck confidence ratings, and unhelpful Intune error codes. Key facts include: - Devices offline for long periods will still receive the 2023 certificates upon reconnecting to Windows Update. - Devices with existing 2023 certificates in firmware will switch to the new boot manager after the latest Windows patches are installed. - A new script, Detect-SecureBootCertUpdateStatus.ps1, is available in Windows for checking certificate status. - BIOS updates may reset a device’s confidence rating to unrated, which is normal and does not indicate certificate failure. - Admins should edit the AvailableUpdates registry key, not the AvailableUpdatesPolicy, which is managed by Intune and Group Policy. - A licensing bug affecting AvailableUpdatesPolicy on devices upgraded from Pro to Enterprise was resolved by Microsoft in 2026. - BitLocker recovery is not typically linked to the certificate update process but may relate to firmware or PCR issues. - Dell and HP provided guidance on which BIOS versions include the 2023 certificates for their newer models. - Older HP EliteBook 840 G5 units require a manual update package for the new certificates. - Eligible devices can still receive the 2023 certificates in the future, and Surface devices released from 2024 onward come pre-equipped with them. - Microsoft confirmed that devices running 2011 certificates will not lose the ability to receive the 2023 chain. - The Microsoft Corporation KEK CA 2011 and Microsoft UEFI CA 2011 certificates have expired, with the Microsoft Windows Production PCA 2011 set to expire on October 19, 2026.

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.

Winsage

July 15, 2026

LabubaRAT malware infiltrates Windows systems while posing as NVIDIA software

LabubaRAT is a newly identified Rust-based remote access tool (RAT) that disguises itself as NVIDIA software to facilitate operations on Windows systems. It establishes a reusable foothold for hands-on activity and can profile the host system, identify security tools, execute commands, transfer files, capture screenshots, and proxy network traffic. LabubaRAT retrieves its configuration at launch via command-line arguments or environment variables, allowing operators to specify parameters like the command-and-control server and API key at runtime. It maintains local state in a SQLite database and supports communication through HTTPS polling, Microsoft Edge WebView2, and DNS tunneling. The malware includes capabilities for command execution, file handling, and establishing persistence through a Windows Run registry key. The entry point for the attack is an executable named nvidia-sysruntime.exe, which masquerades as NVIDIA’s container runtime toolkit. LabubaRAT conducts thorough profiling of the host, checking for installed browsers and security products, and gathers information on the system's specifications. Blackpoint Cyber has shared indicators of compromise to help detect LabubaRAT activity.

AppWizard

July 14, 2026

Chatto: Open-source team messenger with privacy at its core

Chatto is a self-hosted messaging software released under an open-source license, allowing users to run it on their own hardware. It supports builds for Linux, macOS, and Windows and requires only a single executable for installation. Chatto encrypts message texts and selected durable account fields using per-user keys, ensuring that only the account owner can access their plaintext data. When a user deletes their account, the associated keys are destroyed, rendering the data unreadable. Each Chatto server is dedicated to a single community, isolating users and messages from others. The software includes built-in voice and video calls with end-to-end encryption. A paid hosting option, Chatto Cloud, is set to enter public beta soon, operating on European infrastructure while maintaining compatibility with self-hosted versions. Chatto is available for free on GitHub.

Winsage

July 14, 2026

Microsoft’s July 2026 Patch Tuesday is here

Microsoft has introduced a point-in-time restore feature on its Microsoft Learn platform, allowing users to revert Windows systems to a previous state to protect against data loss and system errors. The company is also addressing critical security vulnerabilities, including CVE-2026-20833 and CVE-2026-50656, by working on patches and updates to enhance security.

Winsage

July 12, 2026

Microsoft is killing 15 products in 2026, including Windows 11 24H2 and Office 2021

Microsoft will discontinue support for several products in 2026, including: 1. Windows 11 24H2 (Home and Pro) - End of servicing on October 13, 2026. 2. Office 2021 and Office LTSC 2021 - End of support on October 13, 2026. 3. Windows Server 2012 and 2012 R2 - Final ESU year ending on October 13, 2026. 4. SQL Server 2016 - Extended end of support on July 14, 2026. 5. SharePoint Server 2016 and 2019, Project Server 2016 and 2019 - Support ends on July 14, 2026. 6. Microsoft Publisher - Retired after October 2026. 7. Exchange Server 2016 and 2019, Skype for Business Server - Final ESU cutoff on October 31, 2026. 8. Windows 11 Enterprise and Education 23H2, .NET 8, .NET 9, PowerShell 7.4 - End of servicing on November 10, 2026. 9. Windows 10 2016 LTSB and IoT Enterprise LTSB 2016 - Extended support ends on October 13, 2026. 10. Windows 11 SE - Support ends on October 1, 2026. 11. Dynamics CRM 2016 and older versions - Lost support earlier in January 2026. 12. Microsoft Configuration Manager, version 2409 - End of support on June 6, 2026. 13. Visual Studio 2022 LTSC channels, versions 17.10 and 17.12 - End of support in January and July 2026, respectively. 14. InfoPath 2013, SharePoint Designer 2013 - End of support on July 14, 2026. 15. Azure service retirements - Occurring in late 2026. Organizations are advised to review their software landscape and plan for transitions to avoid disruptions.

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.