data access

Tech Optimizer
September 24, 2026
The collaboration between ICE Data Services and FactSet provides users with access to extensive market data and reference data, enhancing analytical capabilities and decision-making processes for financial professionals. ICE Data Services offers real-time market insights, while FactSet supplies comprehensive coverage of securities and financial instruments. This partnership is supported by organizations like the American Bankers Association, ensuring the credibility and legal compliance of the data provided. The alliance aims to meet the increasing demand for high-quality data in the evolving financial sector.
AppWizard
September 5, 2026
Android Auto, enhanced by Gemini, allows users to navigate to restaurants, make calls, and play music. It integrates with applications like Google Maps for real-time navigation and supports voice commands for hands-free operation. The platform is compatible with third-party apps such as Spotify and Waze, and offers specialized apps for electric vehicles. However, users have reported issues with the Gemini voice assistant, including misunderstandings and system shutdowns. Google Maps can automatically display the last-used navigation app, which may not be preferred, and navigation apps can consume data unnecessarily. Message notifications can distract drivers, but users can disable them for better focus.
Tech Optimizer
September 1, 2026
Choosing a database instance size without prior knowledge of the workload can lead to inefficiencies and excessive compute usage. Lakebase Postgres addresses this with an autoscaling feature that eliminates manual sizing, utilizing in-place VM resizing and a monitoring algorithm for CPU, memory, and working set size. Lakebase Postgres separates compute and storage layers, allowing independent resizing of compute nodes without affecting the database. The autoscaling algorithm relies on three signals: CPU load (cpuGoalCU), memory use (memGoalCU), and compute-cache working set size (lfcGoalCU). The CPU load is monitored every five seconds, aiming to maintain it at or below 90% capacity. Memory usage is tracked at two frequencies: overall memory every five seconds and Postgres-specific memory every 100 milliseconds, with a goal to keep usage below 75% of allocated RAM. The compute cache evaluates active data access efficiency, adjusting size based on workload. The working set is estimated using a modified HyperLogLog algorithm that records timestamps for page accesses, allowing for distinct page estimates over various time frames. The algorithm projects future working-set growth to allocate sufficient cache while capping it at 75% of RAM. Resizing the compute involves four components: the autoscaler-agent, vm-monitor, Kubernetes scheduler, and NeonVM. Scaling up occurs when any of the three goals indicate a need for more resources, while scaling down includes verification to ensure sufficient memory remains for operations. Timely adjustments in both directions are prioritized to minimize costs.
Tech Optimizer
August 14, 2026
Databricks has acquired ElectricSQL, a startup specializing in PostgreSQL database capabilities, to enhance its offerings by extending PostgreSQL functionalities to edge devices. The acquisition, announced on August 11, aims to streamline operations for AI agents by allowing local data access. ElectricSQL has developed PGlite, a lightweight WebAssembly version of PostgreSQL for faster data access and real-time synchronization. This acquisition follows Databricks' earlier integration of PostgreSQL capabilities through the acquisition of Neon in May 2025. The founders of Electric, James Arthur and Kyle Matthews, will join Databricks. PostgreSQL has become the most popular open-source database by 2024, recognized for its versatility in handling diverse data types. Databricks' acquisition strategy also includes previous acquisitions like Neon and MosaicML, aimed at enhancing its AI development tools.
Tech Optimizer
August 5, 2026
Google Cloud has introduced its Database Migration Service, which focuses on converting SQL Server stored procedures with multiple result sets into PostgreSQL code. The service uses an automated decision-making process to determine if a SQL Server procedure should be translated into a PostgreSQL stored procedure or function, based on the number of result sets and the presence of a scalar return value. Procedures with a single result set or scalar return value are converted into PostgreSQL stored procedures, while those with multiple result sets are transformed into functions returning a SETOF refcursor. In a healthcare reporting example, a master procedure retrieves patient details and may return multiple result sets along with a status integer. The PostgreSQL translation involves simpler child procedures as standard procedures and more complex routines as functions that open cursors sequentially. Scalar return values are handled by placing them in a dedicated cursor at the end of execution, changing how applications interact with the outputs. Testing of these translated objects must occur within an explicit transaction block due to PostgreSQL cursor lifecycle constraints. The user executes the function to generate cursor references and fetches data sequentially. Google Cloud's internal analysis classifies stored procedures by scanning for direct result sets and considers conditional logic and loops that may complicate result set counts. A directed graph of procedure calls is constructed to determine the result set classification. Database migrations often face challenges due to legacy application logic, especially with stored procedures designed to minimize database round trips. Successful code conversion is only part of the task, as teams must also adjust test harnesses and application data access layers for PostgreSQL's cursor management. Google Cloud's service categorizes SQL Server procedures into three types: no result sets, a single result set, or multiple/dynamic result sets.
AppWizard
August 5, 2026
Recent research from the Electronic Frontier Foundation (EFF) has revealed that millions of Android users' location data are being inadvertently exposed to advertisers through third-party code libraries. This occurs when seemingly harmless applications, like weather services and fitness trackers, integrate third-party SDKs that collect user location information automatically upon permission approval, often without developers' awareness. Data brokers aggregate this location information to create detailed movement profiles sold to advertisers and government agencies. Despite privacy regulations like GDPR and CCPA, enforcement is inconsistent, and developers may claim ignorance regarding data practices they did not implement. Google has improved Android's privacy controls, but visibility into third-party libraries accessing data remains limited. Developers face challenges in auditing third-party code due to resource constraints, leading to a complex landscape where user information traverses multiple entities without clear accountability. Privacy advocates are calling for new technical standards to require SDKs to disclose their data practices.
Tech Optimizer
July 29, 2026
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.
BetaBeacon
July 24, 2026
The Red Magic Astra 2 tablet is powered by a Snapdragon 8 Elite Gen 5 processor, up to 16 GB of RAM, and 512 GB of storage. It features a 9-inch 2.4K OLED display with a 185 Hz refresh rate and 1,600 nits of peak brightness. The tablet includes a liquid cooling system to manage heat buildup during intensive use. It can emulate PC games locally, supports external peripherals, and offers customization options for a complete gaming experience. The tablet excels in delivering high-performance gaming but may experience thermal throttling during extended sessions.
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