# Databases Databases are repositories of information or data used in computing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The User Write Lock: an Alternative to Transactions for Google App Engine DevFeed: [The User Write Lock: an Alternative to Transactions for Google App Engine]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-06-20T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Transactions](), [Databases](), [Google]() Tags: [caching](), [databases](), [engineering](), [google](), [infrastructure](), [news](), [processes](), [retry](), [transactions]() ### AI overview This developer article explains how transactions in Google App Engine's datastore provide atomicity for single-entity and multi-entity writes, but can introduce frozen datastore state, retries, idempotence requirements, and restrictions on datastore queries. It presents the User Write Lock as an alternative, although the supplied text ends before describing that alternative. ### Source excerpt By Craig Silverstein Transactions are the standard method in Google App Engine's datastore -- as in many databases ... Read more ## Ensuring transaction-safety in Google App Engine DevFeed: [Ensuring transaction-safety in Google App Engine]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-06-27T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Transactions](), [Databases](), [Code](), [Google]() Tags: [cache](), [code](), [database](), [engineering](), [google](), [hooks](), [infrastructure](), [news](), [tracing](), [transactions]() ### AI overview The article explains how Khan Academy uses transactions and user-write locks with Google App Engine to prevent database corruption caused by concurrent requests overwriting one another. It describes hooks that detect transaction-safety violations and highlights caching and code-path mistakes that can cause stale data to be used inside transactions. ### Source excerpt By Craig Silverstein In last week's exciting post, I described an alternative to transactions that we use at ... Read more ## Implementing GitOps from Infrastructure to DB Operators to Unify Ops for Kubernetes Databases DevFeed: [Implementing GitOps from Infrastructure to DB Operators to Unify Ops for Kubernetes Databases]() Original publisher: [Read original article]() Author: Sucahyo Ardy Prasetiyo Published: 2026-09-16T08:57:10Z Content type: tutorial Language: en Sources: [SeveralNines]() Topics: [GitOps](), [Kubernetes](), [Terraform](), [argo-cd](), [ClusterControl](), [Databases](), [opentofu]() Tags: [argo-cd](), [clustercontrol](), [databases](), [gitops](), [hybrid-operations](), [infrastructure](), [kubernetes](), [opentofu](), [sovereign-dbaas](), [terraform]() ### AI overview This article describes a layered GitOps approach for Kubernetes database platforms: manage infrastructure with Terraform or OpenTofu, manage Kubernetes deployments with Argo CD, and standardize operations for operator-managed databases with ClusterControl. It explains how declarative configuration and drift reconciliation differ between Terraform and Argo CD, and compares infrastructure automation options including Atlantis and Flux tf-controller. ### Source excerpt Most platform teams already use GitOps for their Kubernetes apps. The config lives in Git, Argo CD applies it, and deployments are predictable. But look one layer down and things get messy. Infrastructure setup is often still done by hand, running Terraform locally or through scattered scripts. Database operations, especially for databases running through Kubernetes [...] The post Implementing GitOps from Infrastructure to DB Operators to Unify Ops for Kubernetes Databases appeared first on Severalnines. ## Turso Accident Protection Enables Database Restoration for Up to 5 Days DevFeed: [Turso Accident Protection Enables Database Restoration for Up to 5 Days]() Original publisher: [Read original article]() Author: Pedro Muniz Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Turso Blog]() Topics: [Turso](), [Databases](), [MCP Server]() Tags: [ai-agents](), [database](), [databases](), [feature](), [mcp](), [mcp-server](), [turso]() ### AI overview Turso has released Accident Protection, allowing databases deleted on a paid plan to be restored for up to five days at no additional cost. The feature is available through the UI and Turso MCP server. ### Source excerpt Agents delete things. Now you can undelete them. Restore databases for up to 5 days after deletion, on any paid plan, at no additional cost. ## Introducing TIN: full-text search for Postgres DevFeed: [Introducing TIN: full-text search for Postgres]() Original publisher: [Read original article]() Author: Patrick Reynolds Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Blog -- PlanetScale]() Topics: [Databases](), [benchmarking]() Tags: [backups](), [benchmark](), [benchmarks](), [bm25](), [engineering](), [full-text-search](), [index](), [performance](), [postgres](), [reddit](), [replication](), [search](), [text](), [wikipedia]() ### AI overview PlanetScale announces TIN, a full-text search extension for Postgres and Neki databases. The article describes supported query and matching features, transaction and update behavior, and benchmark workloads and corpora used to assess performance. ### Source excerpt TIN is a fast, full-featured, full-text search index for Postgres ## Kubernetes 1.36 restores a lost guarantee for database backups DevFeed: [Kubernetes 1.36 restores a lost guarantee for database backups]() Original publisher: [Read original article]() Author: Shubham Pampattiwar Published: 2026-09-15T13:00:00Z Content type: news Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack](), [The New Stack]() Topics: [Kubernetes](), [PostgreSQL](), [Databases]() Tags: [backups](), [cncf](), [consistency](), [database](), [databases](), [kubernetes](), [post-contributed](), [postgresql](), [snapshots](), [sponsor-cncf](), [sponsored-post-contributed](), [stateful](), [storage]() ### AI overview The article explains how Kubernetes 1.36 restores coordinated snapshots for applications whose state spans multiple volumes, addressing inconsistent database backups caused by taking individual PersistentVolumeClaim snapshots at different times. ### Source excerpt It's 2 a.m., and you're restoring a PostgreSQL cluster from last night's backup. Its data directory lives on one PersistentVolumeClaim The post Kubernetes 1.36 restores a lost guarantee for database backups appeared first on The New Stack. ## PostgreSQL 19 graph queries fail the 'would you ship this?' test DevFeed: [PostgreSQL 19 graph queries fail the 'would you ship this?' test]() Original publisher: [Read original article]() Author: Lindsay Clark Published: 2026-09-15T09:42:59Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [PostgreSQL](), [SQL](), [Graphs](), [Databases]() Tags: [bugs](), [concurrent](), [databases](), [graph](), [postgresql](), [sql]() ### AI overview The article reports that PostgreSQL 19's SQL/PGQ graph queries were rejected because of unresolved bugs. It also discusses concurrent REPACK as a way to reduce overnight maintenance calls for database administrators. ### Source excerpt SQL/PGQ gets bounced over unresolved bugs as concurrent REPACK promises fewer midnight calls for DBAs ## AI DevFeed: [AI]() Original publisher: [Read original article]() Published: 2026-09-15T02:59:12.316196Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Artificial Intelligence](), [AI Agent](), [Security](), [Google](), [Protocol (disambiguation)](), [Databases](), [CockroachDB]() Tags: [2026](), [agents](), [ai](), [cockroachdb](), [databases](), [google](), [security](), [vector-search]() ### AI overview An AI-focused Cockroach Labs blog category page listing articles about agent-to-agent communication, AI agent security, database isolation, compliance, resilience, identity, vector search, operations automation, and durable execution with CockroachDB. ### Source excerpt Blog ai A2A Is Now an Open Standard. The Data Layer Underneath It Isn't. In April 2025, Google released a protocol for agent-to-agent communication. Within three months, Google had donated it to the Linux Foundation... Quentin Packard August 19, 2026 Multi-Tenant AI Agents: Why Data Isolation Starts at the Database Most SaaS teams shipping agentic features focus on prompt safety and API-layer filtering. But effective AI agent security also depends on... ## Introducing Encore x E2B DevFeed: [Introducing Encore x E2B]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-09-15T00:00:00Z Content type: release Language: en Sources: [Encore Updates]() Topics: [AI-assisted coding](), [Cloud](), [backends](), [SDKs](), [Databases](), [Deployment](), [API](), [Code]() Tags: [agents](), [api](), [backend](), [cloud](), [code](), [coding](), [coding-agents](), [database](), [deployment](), [pull-request](), [sandbox](), [sdk]() ### AI overview Encore and E2B can provide coding agents with isolated sandboxes containing complete local backends. The integration lets agents build and verify end-to-end changes against infrastructure such as databases, queues, and APIs, while supporting deployment in the user's AWS or GCP account. ### Source excerpt Give coding agents an isolated sandbox with a complete backend inside it -- and keep both in your own cloud. ## OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards DevFeed: [OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards]() Original publisher: [Read original article]() Author: Kristi Piechnik Published: 2026-09-14T12:00:14Z Content type: news Language: en Sources: [OpenSearch]() Topics: [Open Source](), [AI Infrastructure](), [observability](), [AI search](), [Security](), [Retrieval Augmented Generation (RAG)](), [Databases](), [real-time]() Tags: [2026](), [agentic-ai](), [ai-agents](), [ai-infrastructure](), [announcements](), [awards](), [data](), [enterprise](), [observability](), [open-source](), [opensearch](), [real-time](), [recognition](), [retrieval](), [retrieval-augmented-generation-rag](), [search](), [security]() ### AI overview OpenSearch won the Analytics & Data Intelligence Solutions category in SiliconANGLE Media's 2026 TechForward Awards. The recognition highlights its open source, vendor-neutral platform for enterprise search, observability, security analytics, vector databases, and agentic AI workloads. ### Source excerpt Recognition validates open source momentum, architectural consolidation, and enterprise scale as the project marks five years of community growth The post OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards appeared first on OpenSearch. ## Article: Implementing Durable Workflows on Postgres Without an External Orchestrator DevFeed: [Article: Implementing Durable Workflows on Postgres Without an External Orchestrator]() Original publisher: [Read original article]() Author: Raman Varma Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Databases](), [SQL](), [Provisioning](), [incident](), [CI/CD](), [Kubernetes](), [AWS Step Functions](), [Artificial Intelligence]() Tags: [ai](), [ai-ml-data-engineering](), [architecture-design](), [article](), [automation](), [aws-step-functions](), [database](), [durable-workflows-postgres](), [incident-response](), [infrastructure](), [kubernetes](), [ml-data-engineering](), [orchestration](), [postgres](), [queue](), [relational-databases](), [sql](), [workflows]() ### AI overview This article explains how to implement durable workflows on Postgres without an external orchestrator. It describes using row-level locking as a concurrent work queue, primary-key checkpoints for idempotency, and leases with a sweeper for crash recovery. Workflow state, sleeps, and human approvals can persist in the database and survive process restarts. ### Source excerpt Postgres can serve as the durable state store and coordination layer for workflows, eliminating the need for an external orchestrator. SKIP LOCKED enables concurrent work processing, primary-key checkpoints enforce idempotency, and leases support crash recovery. Workflow sleeps and human approvals can also be persisted as database state and survive restarts. By Raman Varma ## PostgreSQL Monitoring and Schema Linting for Laravel with Vacuum DevFeed: [PostgreSQL Monitoring and Schema Linting for Laravel with Vacuum]() Original publisher: [Read original article]() Author: Paul Redmond Published: 2026-09-14T04:24:35Z Content type: article Language: en Sources: [Laravel]() Topics: [Laravel](), [PostgreSQL](), [Monitoring](), [ci](), [Databases](), [Structured-data]() Tags: [ci](), [github](), [laravel](), [laravel-packages](), [monitoring](), [postgresql](), [schema](), [sql]() ### AI overview Vacuum is a PostgreSQL monitoring and schema-linting package for Laravel. It analyzes PostgreSQL statistics, reports issues such as bloat, wraparound, dead tuples, unused indexes, slow statements, and unindexed foreign keys, and provides SQL remediation guidance, health scores, dashboards, CI commands, history, and explainers. ### Source excerpt Vacuum checks PostgreSQL in Laravel apps for bloat, wraparound, and unused indexes, and flags unindexed foreign keys in migrations during CI. The post PostgreSQL Monitoring and Schema Linting for Laravel with Vacuum appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox. ## Unify your marketing data with Lakeflow Connect DevFeed: [Unify your marketing data with Lakeflow Connect]() Original publisher: [Read original article]() Author: Sonia Bendre; Giselle Goicochea Published: 2026-09-11T18:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Software as a service](), [Databases](), [API](), [Automation](), [dashboards](), [hubspot](), [CI/CD]() Tags: [analytics](), [api](), [automation](), [churn](), [ci-cd](), [connectors](), [crm](), [customer](), [customers](), [dashboards](), [data](), [databases](), [databricks](), [engineering](), [marketing](), [product](), [retention](), [saas](), [salesforce](), [series]() ### AI overview This first post in a series presents Lakeflow Connect as a fully managed data-ingestion service for unifying fragmented marketing and Customer 360 data. It describes native connectors for SaaS applications, databases, and files, configured through a point-and-click UI or API, with governed tables in Unity Catalog and integrations such as HubSpot and Salesforce. The article highlights reduced maintenance compared with custom pipelines and support for downstream reporting, analytics, retention, and churn analysis. ### Source excerpt This is the first post in a new series exploring how Lakeflow Connect brings fully... ## Run DuckDB analytics on your Amazon DynamoDB data with zero-ETL DevFeed: [Run DuckDB analytics on your Amazon DynamoDB data with zero-ETL]() Original publisher: [Read original article]() Author: Lee Hannigan Published: 2026-09-11T14:53:57Z Content type: tutorial Language: en Sources: [AWS Database Blog]() Topics: [Databases](), [Amazon S3](), [Apache Iceberg](), [AWS Lambda](), [AWS IAM](), [Latency]() Tags: [advanced-300](), [amazon-dynamodb](), [amazon-s3-tables](), [analytics](), [apache-iceberg](), [aws-lambda](), [duckdb](), [how-to](), [iam](), [integration](), [sql](), [technical-how-to]() ### AI overview This tutorial explains how to run ad hoc SQL analytics on Amazon DynamoDB data with DuckDB through a zero-ETL replication flow. ### Source excerpt Run ad hoc SQL analytics on your Amazon DynamoDB data with DuckDB. A zero-ETL integration replicates your table into Apache Iceberg tables on Amazon S3 Tables, and an AWS Lambda function running DuckDB serves SQL queries through an IAM-authorized function URL. ## Bringing custom knowledge to agents with AutoRAG DevFeed: [Bringing custom knowledge to agents with AutoRAG]() Original publisher: [Read original article]() Author: Diego Alvarez Ponce, Diego Garcia Perez Published: 2026-09-11T13:01:50Z Content type: article Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Retrieval Augmented Generation (RAG)](), [Model Context Protocol (MCP)](), [Databases](), [Large Language Model](), [AI Development](), [LLM evaluation / benchmarking]() Tags: [ai](), [ai-inference](), [artificial-intelligence](), [database](), [developer](), [generative-ai](), [gpu](), [guide](), [how-to](), [large-language-models-llms](), [model-context-protocol-mcp](), [rag](), [red-hat](), [terminal]() ### AI overview This guide explains how to use AutoRAG in Red Hat OpenShift AI to optimize retrieval-augmented generation for enterprise, domain-specific data. It also shows how to integrate a Model Context Protocol server that retrieves customer information from an internal database, using a fictional banking scenario. ### Source excerpt The large language models (LLMs) that power generative AI work by drawing upon the patterns and information present in their training data. Without access to the right data, LLMs struggle to comprehend context--like our internal corporate vocabulary--and as a result, start to hallucinate. The post Bringing custom knowledge to agents with AutoRAG appeared first on Red Hat Developer. ## AI Functions in ClickHouse: Upgrade your SQL to the AI age DevFeed: [AI Functions in ClickHouse: Upgrade your SQL to the AI age]() Original publisher: [Read original article]() Author: Andriy Yakovlev; George Larionov Published: 2026-09-11T12:49:32Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Large Language Model](), [AI search](), [Retrieval Augmented Generation (RAG)](), [Databases]() Tags: [ai](), [classification](), [clickhouse](), [embeddings](), [llm](), [rag](), [sql]() ### AI overview ClickHouse introduces beta AI Functions that invoke LLM and embedding providers directly from SQL for tasks including classification, extraction, generation, translation, filtering, redaction, embeddings, and semantic similarity. ### Source excerpt Explore ClickHouse AI Functions for classification, generation, translation, embeddings, semantic search, and cost controls--all directly from SQL. ## Presentation: How To Run on Three Clouds at Once, and When Not To DevFeed: [Presentation: How To Run on Three Clouds at Once, and When Not To]() Original publisher: [Read original article]() Author: Ross McFarlane, Kevin Holditch Published: 2026-09-11T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [networking](), [Databases](), [Kubernetes]() Tags: [amazon](), [architecture](), [architecture-design](), [availability](), [aws](), [cloud](), [cloud-architecture](), [cloud-computing](), [cloud-networking](), [cockroach-labs](), [cockroachdb](), [containers](), [databases](), [devops](), [disaster-recovery](), [engineering](), [europe](), [financial-applications](), [fintech](), [form3-multicloud-architecture](), [infoq](), [kubernetes](), [microservices](), [networking](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [resilience](), [transcripts](), [us]() ### AI overview A presentation on Form3's move from one cloud to a triple-active multi-cloud architecture, covering cross-cloud networking, distributed databases, Kubernetes operators, and regional disaster-recovery expectations. ### Source excerpt Ross McFarlane and Kevin Holditch discuss Form3's evolution from a single-cloud setup to a triple active multi-cloud architecture. They share key engineering strategies for cross-cloud networking, distributed databases with CockroachDB and NATS, custom Kubernetes operators, and navigating distinct regional disaster recovery expectations across the UK, Europe, and US financial markets. By Ross McFarlane, Kevin Holditch ## CERN PGDay 2027: Announcement and CfP DevFeed: [CERN PGDay 2027: Announcement and CfP]() Original publisher: [Read original article]() Author: Swiss PostgreSQL Users Group Published: 2026-09-11T00:00:00Z Content type: news Language: en Sources: [PostgreSQL news]() Topics: [Databases](), [Machine learning](), [AI search](), [Retrieval Augmented Generation (RAG)]() Tags: [announcement](), [community](), [conference](), [database](), [postgresql]() ### AI overview CERN PGDay 2027 is an announcement and call for papers for a single-day PostgreSQL community conference at CERN in Geneva. Proposed talk themes include large-scale database performance, AI and vector-search workloads, security, and PostgreSQL extensibility. ### Source excerpt CERN PGDay 2027 Date: Friday, February 12, 2027 Location: CERN Campus, Geneva, Switzerland / Suisse Romande Language: English Organizers: Swiss PostgreSQL Users Group (SwissPUG) & CERN Format: Single-track (6-7 sessions) followed by networking social event Homepage: swisspug.org/cern-pgday-2027 About the Event Continuing in the line of work of the past editions, CERN PGDay 2027 returns as the annual gathering for PostgreSQL users, developers, and enthusiasts across Suisse Romande (western Switzerland) and the neighboring French border regions. Co-organized by CERN and SwissPUG, this single-day community conference offers a premier opportunity to network, share practical experiences, and explore the future of the world's most advanced open-source database. The event takes place in the unique, international environment of Geneva--a hub for major scientific institutions, non-governmental organizations, and financial and tech enterprises. Format & Venue Single-Track Schedule: The program consists of a single track featuring 6 to 7 technical sessions presented entirely in English. Social Event & Networking: Following the technical presentations, a social event will take place to facilitate community networking, discussions, and collaboration in the inspiring atmosphere of CERN. CERN Visits: Attendees are encouraged to arrange their schedules before or after the event to take advantage of the conference location and visit CERN, the European Organization for Nuclear Research. Call for Papers (CfP) Speaker proposals for CERN PGDay 2027 can be entered online via Indico. Call for Papers Closes: November 8, 2026 (23:59 CET) Call for Sponsors (CfS) Sponsors proposals for CERN PGDay 2027 are welcome. Please find the contract with all details online. Key Themes We welcome talk proposals covering broader PostgreSQL and community trends and localized enterprise & scientific topics: High-Performance Science & Big Data: Managing massive datasets, extreme write throughput, partitioning ## Choosing among write-through, write-behind, and write-around caching strategies DevFeed: [Choosing among write-through, write-behind, and write-around caching strategies]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-11T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Caching](), [Databases](), [Redis](), [consistency](), [Latency]() Tags: [cache](), [caching](), [consistency](), [database](), [latency](), [orders](), [postgres](), [redis](), [strategies](), [system-design](), [write](), [write-behind](), [write-through]() ### AI overview This article compares write-through, write-behind, and write-around caching strategies. It explains their trade-offs among database latency, cache hit rates, consistency, and the risk of losing acknowledged writes when a process fails. ### Source excerpt The three write strategies differ in what happens between the cache write and the database write, and that gap is where correctness lives. Write-through updates both before acknowledging, so it is safe and pays the full database latency. Write-behind acknowledges after the cache write and flushes later, which is fast and can lose acknowledged writes if the process dies. Write-around skips the cache entirely on write, which avoids caching data nobody reads and costs a miss on the next read. ## Improving Lakebase Postgres compute cache DevFeed: [Improving Lakebase Postgres compute cache]() Original publisher: [Read original article]() Author: David Wein; Sunil Kamath; Haoyu Huang Published: 2026-09-10T13:47:03Z Content type: article Language: en Sources: [Databricks]() Topics: [Caching](), [databricks](), [Databases](), [autoscaling](), [Amazon S3](), [Filesystems](), [cloud-infrastructure]() Tags: [autoscaling](), [cache](), [caching](), [data](), [data-engineering](), [databricks](), [engineering](), [filesystem](), [latency](), [low-latency](), [memory](), [s3](), [storage]() ### AI overview This Databricks article describes improvements to compute-side caching for Lakebase Postgres, a disaggregated storage system backed by object storage such as Amazon S3. It explains PostgreSQL shared buffers, the operating system page cache, and the planned use of dynamically autoscaling shared buffers consuming up to 75% of compute memory. It also introduces a local file cache as an incremental solution for fixed compute instances. ### Source excerpt The disaggregated storage model of Lakebase Postgres provides a feature rich, flexible... ## PostgreSQL Migrator 1.0 : first stable release DevFeed: [PostgreSQL Migrator 1.0 : first stable release]() Original publisher: [Read original article]() Published: 2026-09-10T00:00:00Z Content type: release Language: en Sources: [PostgreSQL news]() Topics: [migration](), [Databases](), [releases](), [MySQL](), [Oracle Database](), [ci]() Tags: [ci](), [migration](), [mysql](), [open-source](), [oracle](), [postgresql](), [release]() ### AI overview PostgreSQL Migrator 1.0 is the first stable release of an open-source tool for migrating Oracle, MySQL, and MariaDB databases to PostgreSQL. It supports full inspection and limited conversion and migration for selected database objects, with compatibility promised across 1.x releases. ### Source excerpt Paris, 7th september 2026. The Dalibo team is pleased to announce the release of PostgreSQL Migrator 1.0 stable, a free and open-source tool designed to help migrate databases from Oracle and MySQL/MariaDB to PostgreSQL. Key features Easy installation: pure Go binary with zero proprietary dependencies. Fast, offline inspection: Extract the full source catalog once, then iterate on conversion offline. Complexity scoring: Quickly assess and compare the difficulty of migration projects. Interactive web UI: Browse both the original and converted catalogs, search objects, and identify migration blockers. Schema and data conversion: Converts datatype and SQL expression, convert column to boolean. Code analysis and transpilation: Transpiles and analyzes SQL, PL/SQL and routines with the companion transqlate tool. High-performance data copy: Streamed bulk COPY to PostgreSQL (or to files / pipes), with low memory usage and significantly higher throughput than traditional tools in our tests. Hackable & configurable: Reliable JSON catalog data, TOML configuration, and templating for fine-tuning. Supported features PostgreSQL Migrator supports migrating from Oracle Database 11g to 26ai, MySQL 8.4+ and MariaDB 10+ to PostgreSQL 16 up to 19. Version 1.0 has full inspection including jobs, system triggers, etc. However, conversion and migration is limited to role, schema, sequences, table, virtual columns, constraints and indexes. The documentation describes implemented and planned features including views, procedural objects, SQL Server and more. Stability We have built a demanding CI pipeline including end-to-end tests based on Oracle Free container and cypress. This effectively prevents regression and ensure quality. After one year of beta and rc, PostgreSQL Migrator is stable. We'll ensure configuration, CLI and project compatibility for all 1.x releases. License and availability PostgreSQL Migrator is released under the PostgreSQL License. Documentation: https://postgresql-mi ## Loading Parquet data into MySQL with ClickHouse DevFeed: [Loading Parquet data into MySQL with ClickHouse]() Original publisher: [Read original article]() Author: Mark Needham Published: 2026-09-10T00:00:00Z Content type: tutorial Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [MySQL](), [parquet](), [Databases](), [Amazon S3]() Tags: [clickhouse](), [mysql](), [parquet](), [s3](), [sql]() ### AI overview A tutorial on using ClickHouse to load Parquet data into MySQL and query MySQL through ClickHouse table functions. ### Source excerpt Use ClickHouse to load Parquet files into MySQL, explore remote data, and run MySQL queries with table functions and named collections. ## Validating multi-Region DR for Terraform Enterprise with AWS FIS DevFeed: [Validating multi-Region DR for Terraform Enterprise with AWS FIS]() Original publisher: [Read original article]() Author: Frenil Randeria Published: 2026-09-09T21:05:02Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [Terraform](), [AWS Fault Injection Service (FIS)](), [Chaos Engineering](), [Amazon S3](), [Amazon EC2](), [Databases]() Tags: [advanced-300](), [amazon-aurora](), [amazon-ec2](), [amazon-s3](), [aws](), [aws-fault-injection-service-fis](), [disaster-recovery](), [postgresql](), [technical-how-to](), [terraform](), [validation]() ### AI overview This article explains how to validate customer-operated multi-Region disaster recovery for Terraform Enterprise on AWS using three-phase AWS FIS experiments. It covers failover and failback testing, hidden dependency discovery, and reported recovery times of 12-14 minutes. ### Source excerpt Learn how AWS, HashiCorp, and Athenahealth designed and chaos-tested a multi-Region disaster recovery strategy for Terraform Enterprise on AWS. This post walks through three-phase AWS Fault Injection Service experiments across Amazon EC2, Aurora, and Amazon S3, the 12-14 minute recovery times achieved, and the state file dependency pitfall to avoid. ## Improving Lakebase Postgres Compute Cache on Neon, Part 1 DevFeed: [Improving Lakebase Postgres Compute Cache on Neon, Part 1]() Original publisher: [Read original article]() Author: Sunil Kamath Published: 2026-09-09T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Caching](), [Databases](), [Operating system](), [Amazon S3](), [cpu](), [Latency](), [autoscaling]() Tags: [autoscaling](), [cache](), [compute](), [cpu](), [engineering](), [latency](), [memory](), [os](), [postgres](), [s3](), [storage]() ### AI overview Neon describes a Lakebase Postgres compute-cache change that allocates most memory to shared buffers backed by huge pages. The goal is to keep hot pages in DRAM, reducing storage reads, CPU use, and latency; the article reports up to roughly 2x throughput on specified fixed-size computes. ### Source excerpt On large fixed-size Lakebase Postgres computes on Neon, we now put most of the machine's memory into Postgres shared buffers and back that cache with huge pages. Hot pages stay in DRAM instead of falling through to a local disk cache, so the same working set is served faster and with less CPU. [Next page]()