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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.
Why B3 chose Android for secure AI-enabled productivity
AppWizard
July 1, 2026

Why B3 chose Android for secure AI-enabled productivity

B3 has deployed a secure mobile solution to 1,000 employees in two weeks using Android Enterprise, enhancing security with AI threat detection and Managed Google Play. The transition is expected to yield a 30% cost savings over the next decade. B3 prioritized security and compliance while providing devices that ensure constant availability and productivity. The deployment process was streamlined through collaboration with Samsung and Android Enterprise's zero-touch enrollment, allowing centralized management of devices. Employees received lightweight devices with 7-inch screens and longer battery life, leading to positive feedback. B3 is also expanding its use of AI to improve productivity and plans to refresh hardware more frequently due to the anticipated cost savings.
silver Android smartphone
AppWizard
June 28, 2026

The Steam client was hurting PC gaming on Android — Valve’s VR headset accidentally solved it

Playing Windows games on Android has become feasible due to Winlator and open-source technologies like Wine, Box64, and DXVK. User-friendly applications such as GameHub and GameNative have emerged, enhancing mobile gaming experiences to rival dedicated handheld consoles. A major challenge was the reliance on the x86 Windows Steam client, which was resource-intensive on mobile devices. GameNative 1.0 introduced an experimental feature that eliminates the need for the desktop Steam client by using Valve's native Android libraries, improving the gaming experience significantly. Valve released Steamworks SDK version 1.63 in November 2025, which included native ARM64 libraries for Android, allowing for essential Steam functionalities without a translation layer. GameNative integrated these libraries, replacing the desktop client with a more efficient "bionic" Steam client that operates without a user interface, streamlining DRM and matchmaking processes. GameNative now supports Steam Guard TOTP sign-in, enabling smooth authentication and access to the user's Steam library. It downloads games natively, supports cloud saves, and is compatible with most single-player games with Steam DRM. The application boasts a high compatibility rate, with 221 out of 241 games in one user's library showing as compatible. GameNative 1.0 has improved performance with a Vulkan renderer and reworked controller stack. Despite its success, GameNative is not yet available on the Play Store, with nearly a million users sideloading the application. Developers aim to create a Play Store version that complies with Google's policies for easier installation.
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.
The database storage problem is solved. Here’s what comes next.
Tech Optimizer
June 25, 2026

The database storage problem is solved. Here’s what comes next.

Postgres has been a reliable transactional database for three decades, used for managing customer records and financial transactions. Innovations in the Postgres ecosystem are now focused on minimizing data movement rather than just data storage. The challenge of interoperability is becoming crucial, as organizations seek to share operational data seamlessly across various systems without creating additional copies or pipelines. Many organizations are spending as much effort on data movement as on data storage. Postgres is increasingly viewed as the authoritative system for critical information, and its role is evolving to facilitate better interaction with operational data. Technologies like logical replication and change data capture are enhancing Postgres's integration within data ecosystems. The rise of AI has highlighted the need for real-time access to operational data and has prompted organizations to reconsider the necessity of maintaining multiple copies of the same data. The database industry is shifting focus from optimizing storage to enabling effortless data sharing across systems. Postgres continues to adapt to new workloads and architectural patterns, maintaining its reputation as a stable foundation for operational data while expanding its capabilities through innovative extensions.
Millisecond Latency at Scale: Why Our ML Feature Store Runs on Snowflake Postgres
Tech Optimizer
June 23, 2026

Millisecond Latency at Scale: Why Our ML Feature Store Runs on Snowflake Postgres

Organizations are consolidating their fragmented database environments with Snowflake Postgres, phasing out outdated systems and simplifying multivendor setups without extensive code rewrites. Ericsson migrated four legacy databases to Snowflake Postgres, achieving a 99% reduction in data processing time. SimCorp's transition to Snowflake Postgres resulted in a tenfold increase in disk operation speeds. Sigma Computing provides real-time analytics using Snowflake Postgres, eliminating the need for external systems. BlueCloud supports low-latency transactional workloads and analytics on a single platform. Superblocks enables developers to create full-stack applications using Snowflake CoCo, leveraging SQL tools against live data. Snowflake Postgres is approximately four times faster than Databricks Lakebase and has a 99.95% published uptime SLA. It operates on Postgres 18 and accommodates up to 64 TB of storage, surpassing Lakebase's 16 TB limit. Snowflake Postgres simplifies management with in-place major version upgrades and supports standard logical replication, enhancing flexibility for data movement and integration.
I forced Windows 11's CPU boost on a 10-year-old PC, and it feels surprisingly new
Winsage
June 22, 2026

I forced Windows 11’s CPU boost on a 10-year-old PC, and it feels surprisingly new

Windows 11 has introduced the Low Latency Profile to enhance the responsiveness of the Start menu, Search, and Action Center by rapidly increasing CPU frequency during user interactions. Testing on a Lenovo ThinkCentre M700 Mini Desktop with a 6th Generation Intel Core i3-6100 processor showed that the Low Latency Profile could be activated via the June 2026 Patch Tuesday update (KB5094126) and the third-party tool ViVeTool. Initially, no significant CPU frequency spike was observed, but after enabling the feature, the CPU frequency increased from approximately 800MHz to 3.0GHz and then to 3.7GHz almost instantaneously during interactions. This resulted in a marked improvement in the responsiveness of the Start menu and Action Center, with the latter showing the most significant enhancement. The Low Latency Profile effectively improved performance on older systems without adverse effects on battery life or thermal output.
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