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NewApp Digest
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data synchronization

Scaling AI Inference at the Edge With Distributed PostgreSQL
Tech Optimizer
July 21, 2025

Scaling AI Inference at the Edge With Distributed PostgreSQL

Data is crucial for artificial intelligence, especially for inference workloads used in real-time decision-making across various platforms. Traditional centralized cloud-based AI inference struggles with demands for low latency and high availability, particularly in applications like autonomous vehicles and healthcare. Shifting AI inference to the edge reduces latency, enhances data privacy, and lowers bandwidth costs. Antony Pegg emphasizes the need for a multi-master active-active architecture that allows read and write operations at any node, ensuring data synchronization and high availability. Misconceptions about edge AI include beliefs that edge hardware can't handle AI workloads, that edge inference is limited to low-stakes use cases, and that centralized systems are necessary for data integrity. The shift to distributed inference can lead to reduced latency, faster insights, and lower costs, while supporting data compliance with regulations. Companies are adopting distributed PostgreSQL solutions with multi-master architecture for low latency and high availability. Enquire AI is an example of a company that improved performance and compliance by transitioning to pgEdge Cloud. This architecture allows for consistent data availability and supports scalable AI solutions at the edge.
ParadeDB Raises Funds, Challenges Elasticsearch with Postgres Integration
Tech Optimizer
July 16, 2025

ParadeDB Raises Funds, Challenges Elasticsearch with Postgres Integration

Postgres, an open-source database management system, is experiencing a revival due to its compatibility with AI applications and its ability to manage structured data effectively. Despite its strengths, Postgres lacks native high-performance full-text search and analytics capabilities, leading organizations to use separate systems like Elasticsearch, which complicates data management. ParadeDB, an open-source extension developed in 2023, addresses these limitations by enabling sophisticated data search and analytics directly within Postgres, eliminating the need for data transfers. ParadeDB has gained traction, securing its first enterprise customer by early 2024 and recently completing a funding round for platform enhancements. Its integration with Postgres simplifies workflows and reduces operational overhead, making it a compelling alternative to traditional search solutions.
New Windows 11 trick will improve your Galaxy Book's battery life
Winsage
July 15, 2025

New Windows 11 trick will improve your Galaxy Book’s battery life

Microsoft is testing a new feature called Adaptive Battery Saver Mode for Windows 11 devices, which adjusts battery usage based on workload rather than just battery level. Unlike traditional Energy Saver mode, it maintains screen brightness and allows background tasks to continue without interruption. This feature is currently being tested in the Canary Channel under build number 27898, and if successful, it will be rolled out to all Windows 11 devices. The development coincides with advancements in power-efficient processors from AMD, Intel, and Qualcomm, aimed at improving battery performance in Windows laptops.
Query Amazon Aurora PostgreSQL using Amazon Bedrock Knowledge Bases structured data
Tech Optimizer
July 9, 2025

Query Amazon Aurora PostgreSQL using Amazon Bedrock Knowledge Bases structured data

Amazon Bedrock Knowledge Bases has introduced a fully managed Retrieval Augmented Generation (RAG) feature that connects large language models (LLMs) with internal data sources, enhancing the relevance and accuracy of responses by integrating contextual information from private datasets. During AWS re:Invent 2024, it was announced that this feature now supports natural language querying for structured data retrieval from Amazon Redshift and Amazon SageMaker Lakehouse, allowing generative AI applications to access both structured and unstructured data sources. The system converts user queries into SQL queries using natural language processing, enabling data retrieval without requiring users to know SQL syntax. Amazon Bedrock Knowledge Bases currently supports structured data retrieval from Amazon Redshift and SageMaker Lakehouse. Although direct support for Aurora PostgreSQL-Compatible is not available, users can utilize zero-ETL integration between Aurora PostgreSQL-Compatible and Amazon Redshift to make their data accessible. This integration replicates Aurora PostgreSQL tables to Amazon Redshift in near real-time, simplifying data management. To enable natural language querying of structured application data stored in Aurora, organizations can set up an Aurora PostgreSQL database, create a schema with interconnected tables (products, customers, and orders), and populate these tables with sample data while maintaining referential integrity. Subsequently, they can establish zero-ETL integration with Amazon Redshift, which involves creating a Redshift Serverless workgroup and mapping the database for synchronization. Once the zero-ETL integration is verified, organizations can create an Amazon Bedrock knowledge base for natural language querying. This requires granting appropriate permissions to the Amazon Bedrock Knowledge Bases AWS Identity and Access Management (IAM) role and ensuring the knowledge base is synchronized with Amazon Redshift. After setting up the knowledge base, users can execute natural language queries, which are translated into SQL and processed to generate human-readable responses. Examples of queries include counting unique customers and identifying customers who have purchased the most products. Finally, it is recommended to clean up resources after use to avoid ongoing charges.
New OLTP: Postgres With Separate Compute and Storage
Tech Optimizer
June 21, 2025

New OLTP: Postgres With Separate Compute and Storage

Reynold Xin, co-founder of Databricks, highlighted the outdated nature of online transaction processing (OLTP) databases, which have not evolved significantly since the 1990s and face issues like over-provisioning and performance challenges. Databricks is introducing Lakebase, a product that separates compute from storage to enhance the efficiency of transactional databases, particularly for AI applications. Lakebase allows for instantaneous branching of databases, significantly improving workflow efficiency. Built on open-source Postgres, it supports various open storage formats and offers a copy-on-write capability to reduce costs. The separation of compute and storage is essential as streaming data becomes more integral to enterprises, enabling scalability and timely insights. Databricks aims to manage the entire data lifecycle, ensuring data remains within its ecosystem for rapid reporting and analytics. The integration of Lakebase with existing infrastructure enhances developer experience and operational maturity. The architecture supports extensive experimentation at minimal cost, fostering innovation. As AI agents become more prevalent, the focus on data evaluation and reliability will grow, necessitating a deeper examination of model accuracy.
Implement a rollback strategy for Amazon Aurora PostgreSQL upgrades using Amazon RDS Blue/Green deployments
Tech Optimizer
June 21, 2025

Implement a rollback strategy for Amazon Aurora PostgreSQL upgrades using Amazon RDS Blue/Green deployments

The Amazon Aurora PostgreSQL-Compatible Edition supports managed blue/green deployments to minimize downtime and risks during updates. This deployment strategy involves creating a staging environment (green) that mirrors the production database (blue) through logical replication. The blue environment represents the current production database, while the green environment incorporates updates without changing the application endpoint. After validating changes, the green environment can be promoted to production. In case of issues post-upgrade, a rollback plan is essential, as the managed blue/green deployment feature does not provide built-in rollback functionality. A manual rollback cluster can be established using self-managed logical replication to maintain synchronization with the new version after a switchover. Before the switchover, two clusters exist: the blue cluster (production) and the green cluster (staging). After the switchover, three clusters are present: the old blue cluster (original production), the new blue cluster (updated production), and the blue prime (rollback) cluster (a clone of the old blue cluster). To implement the solution, prerequisites include a cluster parameter group for the new version database with logical replication enabled and familiarity with the Aurora cloning feature. The process involves creating a blue/green deployment, stopping traffic on the blue cluster, performing the switchover, deleting the blue/green deployment, cloning the old blue cluster to create the blue prime cluster, and establishing logical replication from the new blue cluster to the blue prime cluster. Limitations of the managed blue/green deployment include the inability to replicate certain DDL operations and the need to handle endpoint changes manually if a rollback is required. Setting up the rollback cluster incurs additional downtime. To roll back to the blue prime cluster, application traffic must be ceased, the application or DNS records updated, the subscription on the blue prime cluster dropped, and sequence values manually updated if necessary. This process is not automatic and requires careful planning and testing. In production, it is advisable to retain the new blue prime cluster until all applications have transitioned successfully, and the old blue cluster can be backed up for compliance before deletion. For testing purposes, all clusters should be deleted to avoid additional charges.
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