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Tech Optimizer

October 11, 2026

A practical guide to cost optimization with Lakebase Postgres

Lakebase is a fully managed Postgres database designed for modern application development, featuring an architecture that separates storage and compute with a serverless compute layer. This design allows for cost efficiencies through various mechanisms: 1. **Branching**: Developers can create isolated environments for development, testing, or experimentation without duplicating storage costs, as branches share the same underlying storage. 2. **Autoscaling**: Lakebase adjusts compute resources based on activity levels, allowing users to pay only for the compute they use. It can scale down during low activity and can be suspended entirely after inactivity, reducing costs to zero. 3. **Read Replicas and High Availability**: The separation of storage and compute allows for adding read replicas and high availability without incurring additional storage costs, as these instances utilize the same storage layer. 4. **Synced Tables**: Integration with the Databricks Intelligence Platform enables efficient data syncing, allowing users to sync only the necessary working set of data, which helps avoid unnecessary storage and sync costs. 5. **Sync Modes**: There are three sync modes (Snapshot, Triggered, Continuous) for transferring data from the Lakehouse to Lakebase, each with different cost implications based on data freshness requirements. 6. **Right-sizing Compute**: Users can set the initial compute range during project provisioning to avoid over-provisioning and unnecessary costs. 7. **Monitoring and Metrics**: The Lakebase Metrics dashboard provides insights into working set size and cache utilization, helping users optimize their compute sizing and performance. 8. **Point-in-Time Restore (PITR) and Snapshots**: PITR maintains history for recovery within a configurable window, while snapshots offer discrete recovery points. Both are priced lower than standard storage, making them cost-effective options for data recovery. Lakebase costs are categorized into compute, storage, and serverless pipeline compute for syncing data. Compute is measured by CU usage, while storage includes branch storage, PITR history, and snapshot storage, all tracked separately. Users can access detailed usage and cost information through system billing tables.

Tech Optimizer

October 11, 2026

Amazon Aurora PostgreSQL now supports direct querying of Apache Iceberg and Parquet data in your data lake

Amazon has introduced a new capability for Amazon Aurora PostgreSQL that allows users to query operational data alongside data stored in data lakes formatted in Apache Iceberg and Apache Parquet. This feature enables direct access to Iceberg REST Catalog-compatible catalogs, eliminating the need for data duplication or relocation. The integration of DuckDB within Aurora PostgreSQL allows users to query live operational data, including uncommitted writes, alongside data from data lakes using standard PostgreSQL syntax. This capability is available on Aurora PostgreSQL versions 17 (starting with 17.11) and 18 (starting with 18.6). Users must create an Aurora PostgreSQL cluster, attach an IAM role with the AuroraAnalytics feature, and enable the aurora_analytics extension to utilize this feature. They can create foreign tables referencing Iceberg or Parquet data in the data lake, and query them using familiar PostgreSQL commands. Aurora optimizes query performance through techniques like predicate pushdown and column pruning, and users can monitor query behavior with metrics provided by aurora_analytics_stat_statements(). Direct querying of Apache Iceberg and Parquet data is available across all commercial AWS Regions and AWS GovCloud (US) Regions without additional charges, aside from incremental Aurora compute and Amazon S3 request costs.

Tech Optimizer

October 10, 2026

AWS embeds DuckDB in its PostgreSQL DBMS to speed queries of live and historical data

Amazon Web Services Inc. has enhanced its Aurora PostgreSQL database management system to allow users to query the Apache Iceberg data lake directly. This feature integrates the DuckDB analytical engine, enabling seamless access to live transactions and historical records without data duplication or complex ETL processes. Customers can use existing PostgreSQL applications to query data in Amazon S3, including S3 Tables. The integration aims to reduce the engineering workload associated with data pipelines and supports real-time applications. DuckDB processes analytical scans within Aurora, eliminating extra network hops and allowing single queries to access both live and archived data. The feature supports external catalogs compliant with the Iceberg REST Catalog specification and can create foreign tables referencing data across multiple catalogs. Aurora optimizes query performance by filtering records and caching frequently accessed data. Customers can combine recent transactions in Aurora with historical data from S3 without manual schema definitions. For low-latency workloads, selected data can be copied into native Aurora tables using standard SQL. This capability requires the aurora_analytics extension and an AWS IAM role for S3 and Glue access, and is compatible with Aurora PostgreSQL versions 17.11 and 18.6. The feature is available across all AWS regions at no additional charge, with costs incurred only for Aurora computing resources and S3 requests.

Tech Optimizer

October 10, 2026

AWS Embeds DuckDB in Aurora PostgreSQL, Enabling Direct Data Lake Queries Without ETL

Amazon Web Services (AWS) has integrated the open-source analytics engine DuckDB into its Amazon Aurora PostgreSQL database, allowing users to query Apache Iceberg tables and Parquet files directly from data lakes without traditional ETL processes. This feature, announced on September 30, 2026, enables seamless access to both operational and historical data in a single SQL statement. Users can enable the aurora_analytics extension, assign an IAM role for S3 and AWS Glue access, and create external tables using standard PostgreSQL syntax. The integration supports Iceberg tables managed by AWS Glue Data Catalog, Iceberg REST Catalog-compatible external catalogs, Parquet files on S3, and S3 Tables. Performance optimizations include predicate pushdown and column pruning on the data lake side, with caching for frequently accessed data. This feature is available for Aurora PostgreSQL versions 17.11 and later, and 18.6 and later, across all commercial regions and AWS GovCloud (US), with no additional charges beyond Aurora compute and S3 request fees. In a separate development, AWS has a significant contract with AI company Anthropic, anticipating billion in cloud-related contractual obligations over several years, with approximately billion projected to be paid to Amazon from a 10-year agreement announced in April 2026. Anthropic's revenue has shown impressive growth, with first-quarter 2026 revenue reaching 0 million and second-quarter revenue at .8 billion. However, risks are present, as a significant portion of Anthropic's revenue comes from a few customers, and many larger clients lack long-term contracts. Amazon has invested .8 billion in Anthropic, with its stake valued at approximately billion as of June 2026.

Tech Optimizer

October 10, 2026

How and why we built Managed PostgreSQL for AI workloads

The Nebius Managed PostgreSQL team developed a solution for AI workloads using CloudNativePG and Kubernetes, resulting in improved backup and restore times by transitioning from Barman to WAL-G, reducing a 1.5 TB backup duration from over a day to two hours. Since its launch in 2025, the service supports hundreds of production clusters and manages tens of tebibytes of data. Key features include high availability, point-in-time recovery, automated failover, compressed and encrypted backups, rapid restoration, connection pooling, and strict synchronous replication for zero data loss. Kubernetes and CloudNativePG were chosen for their advantages, including out-of-the-box features and open-source distribution. Each cloud project has a separate MK8s cluster for customer isolation and security. Persistent data is stored on fault-tolerant SSD drives, enabling swift instance migration during failures. Regular physical backups are archived, allowing restoration within a seven-day window with a single click. The transition from Barman to WAL-G addressed slow backup and restoration times and resource control limitations. With WAL-G, backups and restorations for a 1.5 TB cluster improved to two hours and one hour, respectively. PgBouncer was implemented to manage database connections effectively, reducing connection storms during peak AI workload traffic. Two vector search extensions, pgvector and pgvectorscale, are available to cater to different memory and dataset needs. Benchmarking showed that Nebius performs competitively with AWS at a lower cost. The infrastructure integrates cloud engineering principles with specialized expertise to meet AI workload demands.

AppWizard

October 7, 2026

Have you experienced disappearing icons in Android Auto? If so, there’s now a fix — here’s how to solve it

Users of Android Auto have been experiencing an issue where the cellular signal icon disappears from their displays. Google has acknowledged the problem and is rolling out a fix. The stable version of Android Auto 17.8 has been released, which resolves the bug for many users. To fix the issue, users should ensure they have the latest version of Android Auto by checking the Google Play Store. If no update is available, they can verify their current version in the app settings. If running version 17.8 and the issue persists, users can clear the app's cache. If the update is not yet available, users may wait for the rollout or consider sideloading the update from a reputable source.

AppWizard

October 6, 2026

Pixel 11 users are reporting Google Maps freezes and strange visual glitches in Android Auto

Users of Google's Pixel 11 series are experiencing significant issues with Google Maps while using Android Auto, including freezing, flickering, and graphical glitches. Reports describe "pixelated black static" appearing in place of the map, affecting both wired and wireless connections. Attempts to resolve the issue by uninstalling the app or clearing its cache have been ineffective. The root cause is unclear, and it is uncertain if Google is addressing the problem. Some users have found that keeping their device unlocked improves performance, while others report no issues. An earlier update to Google Play Services aimed to fix connection bugs, but feedback on its effectiveness has been mixed.

Winsage

October 5, 2026

Microsoft warns fake CAPTCHA tests are hijacking Windows PCs

Microsoft has warned Windows users about a cyber threat that disguises itself as a CAPTCHA test, where cybercriminals use fraudulent verification pages to trick users into executing commands that can install malware. This tactic involves users being directed to a compromised website that presents a fake verification window, prompting them to copy text and execute it in the Windows Run dialog. This method allows attackers to bypass antivirus software by pre-loading malicious scripts disguised as harmless images in the browser's cache. Once activated, the malware collects system information, steals data, and maintains long-term control over the system. Microsoft recommends using robust security software and being aware that no legitimate service will ask users to copy and paste commands into system dialogs.

Winsage

October 3, 2026

Windows 11 Spotted Keeping Thumbnails of Deleted Photos

Windows 11 uses a thumbnail caching mechanism to store previews of images in specific databases located in %LocalAppData%MicrosoftWindowsExplorer, such as thumbcache32.db and thumbcache1024.db. When a user deletes an image, the corresponding thumbnail may remain in the cache, as deletion does not automatically remove it from the thumbnail database. The cache retains only reduced versions of images, not the original files, which is significant in digital forensics where thumbnails can provide insights even when original files are missing. Users can manage their thumbnail cache using the cleanmgr tool to clear saved thumbnails or disable thumbnail caching through the Local Group Policy Editor or File Explorer settings.

AppWizard

September 29, 2026

Stop Uninstalling Your Android Apps When You Need Space

Android 15 has introduced an Archive option that allows users to remove an app from their device while preserving all associated data in the cloud. This feature is useful for seasonal apps that may only be needed at certain times. To use the Archive option, users can go to Settings, find the Apps section, select the app to archive, and choose the Archive option. The app will be removed, and its data will be uploaded to the cloud. Users can restore the app by selecting it again in the Settings app and choosing Restore, which reinstalls the app with all previous settings, though new updates will need to be downloaded. Regular maintenance tasks, such as clearing the app cache, should still be performed for optimal performance.