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134 posts about Migrations. Every summary links to the original.
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Amazon EVS runs VMware Cloud Foundation on AWS bare metal, so the design question becomes where each workload's data lives. This layered diagram shows how vSAN, FSx for ONTAP, and cross-Region DR divide the work.
A side-by-side comparison of legacy Tomcat defaults and the strict defaults in Tomcat 11, showing which behaviors change and what to verify before you upgrade a Java web tier.
Learn the step-by-step process for deploying .NET 10 applications to Heroku, from buildpack detection to SDK pinning, and understand the lifecycle of .NET LTS releases.
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Verifone architect Yuri Geronimus presented at KotlinConf 2026 on using Kotlin’s type system to enforce payment specifications at compile time. He described six incident patterns where a single misused word caused outages, compliance failures, or financial loss, including a terminal fleet outage from a nullable mandatory field. His method uses value classes for spec nouns, validate factories at boundaries, non-null types, masked sensitive data by default, and state-specific classes to make illegal states unrepresentable. Geronimus advocates incremental migration from legacy Java and C++ systems using four parallel tracks rather than a rewrite, noting that payment terminals must operate offline for up to 40 days by regulation. The approach shifts reliability from developer discipline to compiler enforcement, enabling engineers to speak the business language and propose regulatory changes.
Clera, an AI talent agent representing 200,000+ candidates, migrated its 500 GB production Postgres database to ClickHouse Managed Postgres after outgrowing its previous provider. The previous database suffered from point lookups taking multiple seconds, 100% CPU for six straight days, and timeouts. Clera evaluated Supabase, Neon, PlanetScale, and ClickHouse, choosing ClickHouse for its local NVMe storage that avoids network-attached disk latency. In sysbench TPC-C testing, ClickHouse showed 16–42% higher throughput than the runner-up. The team migrated 500+ tables from the EU to the US overnight using ClickPipes and manual replay, with production traffic still hitting the database. After migration, CPU usage dropped to 10–20%, and no settings have been changed since the cutover.
The article provides a step-by-step framework for support leaders considering a switch to a modern, AI-powered customer service platform. It begins by guiding readers to identify push factors, such as an incomplete platform or low ROI, and pull factors, like speed and advanced AI capabilities. The guide then covers how to address team resistance by generating excitement and demonstrating how the new tool solves pain points. It details planning steps including securing executive sponsorship, planning data migration for user information and conversation history, and communicating the change internally. The final step focuses on monitoring the switch by evaluating which traditional KPIs remain relevant and how AI impacts metrics. The framework aims to equip teams to transition successfully to a platform that enables a seamless human-AI partnership.
Red Hat’s migration toolkit for applications 8.3 is now generally available, adding agentic code modernization, direct storage mobility, and automated pipeline generation to OpenShift. The release serves as the official transition path from the migration toolkit for containers, which has reached end of support. For stateful workloads, it introduces direct TLS-encrypted persistent volume claim transfers with a stage-and-cutover flow, S3-mediated indirect transfers for disconnected environments, and intra-cluster StorageClass conversions that bypass Kubernetes immutability constraints. A developer preview agentic migration factory runs configurable multi-agent workflows that analyze dependencies, update code, and validate builds across independent stages, passing context through handoff files on shared Git branches. The toolkit also ships three new Helm-based generators that produce Tekton pipelines, Argo CD Application resources, and Kubernetes deployment manifests for onboarded applications.
Repointing a dbt project to Databricks involves swapping the adapter and profile, handling SQL dialect differences, and keeping transformation logic unchanged. The dbt-databricks adapter bridges the project to Databricks Lakehouse, which uses a three-level namespace (catalog.schema.table) and supports open formats like Delta Lake and Apache Iceberg. Common dialect gaps include REGEXP_SUBSTR, DIV0, and OBJECT_CONSTRUCT, which can be fixed with Databricks-native functions or dbt_utils macros; the post walks through each error and its fix. After compilation and validation using audit-helper to compare outputs model by model, the project can be deployed as a dbt task in Lakeflow Jobs with built-in governance via Unity Catalog. The process is incremental: one-time setup costs apply, then each additional model requires only dialect fixes, making it a low-risk starting point for warehouse migration.
This post provides step-by-step procedures to deploy Oracle Database on Amazon Elastic VMware Service (Amazon EVS) with Amazon FSx for NetApp ONTAP as NFS datastore storage. It covers provisioning storage volumes, mounting NFS datastores, installing Oracle 19c, and configuring SnapMirror replication for cross-region disaster recovery. The deployment uses FSx for ONTAP volumes pinned to SSD tier and presents them as VMDKs on NFS datastores, with Oracle accessing them as local XFS filesystems. Four migration paths are detailed for moving on-premises Oracle workloads, including SnapMirror-based replication for existing ONTAP environments and RMAN backup/restore for non-ONTAP storage. Day-2 operations such as snapshot backup, point-in-time recovery, and database cloning via SnapCenter FlexClone are also covered.
Datalex, a leader in airline ecommerce, needed to modernize its mature EJB2/Java 8 system for Modern Airline Retailing while maintaining continuous airline operations. Over three days, 16 Datalex engineers and 6 AWS specialists proved migration feasibility by extracting a Reservation service into a Spring Boot microservice on Java 21. The team used agentic AI tools—Kiro and AWS Transform Custom—to automate code changes, and established repeatable patterns for DevSecOps, observability, and a multi-agent AI prototype via Amazon Bedrock AgentCore. A business service proxy using the Strangler Fig pattern enabled incremental migration with backward compatibility. The board approved significant investment, the prototype team expanded into a full Agile team, and the EBA shortened discovery from an estimated 8–12 weeks to three days.
Cloudflare is racing toward a 2029 deadline for full post-quantum readiness, having already upgraded many products to post-quantum encryption but still needing to address authentication and remaining classical cryptography. To manage this massive migration, they built CryptoLabe, an internal AI-powered tool that scans their centralized codebase to discover and classify cryptography usage. The tool operates in two stages: discovery finds raw observations of cryptography across source, configuration, manifests, lockfiles, scripts, tests, and documentation, then analysis re-checks each observation, investigates runtime use and dependencies, and assigns classifications such as classical encryption, signature, or token. CryptoLabe also surfaces prerequisites where PQ migration is blocked by missing standards or library support, enabling Cloudflare to work with standards bodies and ecosystem stakeholders to drive migration plans. The post shares learnings for other organizations, recommending starting with a single repository, validating results, and prioritizing high-impact systems.
Red Hat JBoss Web Server 7, based on Apache Tomcat 11 and Jakarta EE 11, requires Java 17 as the new minimum and drops support for Java 11. Jakarta EE 11 does not require another import rename but adds a RecordELResolver for records and an OptionalELResolver for optionals. Tomcat 11 changes parameter parsing to throw exceptions on malformed requests, reduces maxParameterCount to 1,000 from 10,000, removes FailedRequestFilter and HTTP/2 push, and retains quotes in cookie values per RFC 6265. The Java SecurityManager is removed, UBI 8 images are discontinued for UBI 9, Prometheus is no longer bundled, and Red Hat recommends container isolation as replacement. Teams must confirm Java 17 readiness, check for SecurityManager usage, update their base image pipeline, and note that Spring Boot 4 on JWS 7 is unsupported.
The post presents a five-stage playbook for moving production workloads from closed-source models to open-source models: discover, evaluate, adapt, decide, and take the system to production. It argues that managed services, model-agnostic harnesses, gateways, and tooling can reduce migration complexity, potentially making the effort weeks to months rather than months to years. Discovery combines workload definition, relevant benchmark filtering, cost-per-task and runtime analysis, playground testing, and narrowing candidates to two or three models. Evaluation separates accuracy from performance and recommends replaying real traffic against explicit goals for cost, speed, and quality, while adaptation may involve prompts, inference settings, context, and the surrounding harness. The excerpt frames migration as iterative rather than assuming a model passes unchanged.
YouTrack announces live webinars for teams considering migration from Atlassian after Atlassian’s stated discontinuation of Data Center sales and support. The worldwide English-language session is scheduled for September 30, 2026, at 2:00 pm CET on YouTube, alongside a Japanese Zoom session and a Russian-language regional session hosted by Noventiq on October 28. The agenda covers migrating projects, users, and data from Jira, Confluence, and Jira Service Management, with a live demo, customer stories, pricing, licensing, deployment options, user packs, and partner support. It is aimed at administrators, project managers, and team leads, and presents YouTrack as a place to combine project management, documentation, and customer support. The Japan session additionally covers workflow automation and connecting AI agents through MCP, while registration for the Asia and Caucasus session is not yet open.
This user story describes three teams that moved production Postgres workloads from Neon to ClickHouse Managed Postgres after encountering reliability, performance, cost, and network-transfer problems as usage grew. ClickPipes handled schema migration, a parallel initial load, and change data capture, allowing teams to test synchronized databases before cutover. Reported outcomes included zero downtime or connection restarts, faster and more predictable queries, lower CPU or transfer costs, and p99 latency improving from seconds to single-digit milliseconds in one case. The post attributes these gains to local NVMe storage and eliminating transfer fees when analytics already runs on ClickHouse, while noting differences from Neon such as slower branch creation and manual application of several DDL changes.
Sentry is replacing the JavaScript SDK boolean option sendDefaultPii with dataCollection, a category-level control for automatically collected data. In version 11, dataCollection becomes the default, sendDefaultPii is removed, and several categories—including user information, cookies, HTTP bodies, GenAI inputs and outputs, database query data, and stack-frame variables—are collected by default. The post distinguishes PII, which is collected by default, from sensitive credentials, whose values in collected headers are filtered automatically; manually attached data remains unaffected. Developers can preserve v10’s restrictive baseline or select true, false, allow lists, and deny lists for supported fields, while beforeSend and beforeSendSpan handle additional filtering. Because v11 also enables span streaming by default, span drops move to ignoreSpans, and existing withStreamedSpan wrappers should be removed.
Evenpay develops compensation transparency software to meet the EU Pay Transparency Directive, requiring strict safeguards for sensitive employee salary data. The startup initially selected a lightweight B2B authentication platform for quick onboarding, but soon encountered unmet data residency and compliance demands from Finnish enterprise clients. To resolve these limitations, Evenpay migrated all customers to Auth0 over a few weeks using documentation and AI assistance. Adopting Auth0 allowed the team to fulfill regional EU data controls while reducing managed Single Sign-On setup times from weeks to as little as one day through self-service options. Furthermore, relying on an established authentication brand provided instant credibility with enterprise security officers without requiring custom authentication development.
Suprema Gaming migrated its analytics platform from Snowflake to ClickHouse Cloud on AWS as part of a company-wide move toward agentic operations. The previous stack delivered data about four hours late, required heavy dbt rebuilds, and often took minutes to answer queries, limiting real-time investigations and interactive analysis. Suprema rebuilt ingestion with ClickPipes for supported sources and Debezium plus Kafka for others, while dbt and GitHub Actions deploy the warehouse-layer structure without external cron jobs, Airflow, or a separate orchestrator; governed access uses role-based controls and query-time PII masking. Before cutover, every metric was validated against the reference source, and the resulting platform reduced warehouse spend by roughly 62%, improved direct-read performance 27x, and maintains about one-minute p95 freshness, supporting an early production deployment of ClickHouse Agents.
Open table formats add metadata layers over Parquet or ORC files in object storage, enabling ACID transactions, schema evolution, time travel, and database-like consistency for data lakes. Apache Iceberg uses manifests and manifest lists in a metadata tree, supports partition evolution without rewriting existing files, and creates immutable snapshots for auditing, rollback, and reproducible queries. Delta records atomic writes in an ordered transaction log with checkpoints and is closely associated with Spark, while Hudi uses record-level indexes for frequent updates, change-data-capture, and near-real-time ingestion. All three share core capabilities but differ in partition evolution, update and delete strategies, and multi-engine support; Parquet and ORC remain file formats rather than table formats. The overview recommends scheduled compaction, metadata maintenance, health monitoring, and a production-volume proof of concept when selecting a format.
Cloudflare migrated its official blog to EmDash, an Astro-focused content management system, acting as internal Customer Zero to evaluate scaling and usability. To prepare for traffic spikes reaching thousands of requests per second, engineers used k6 to execute ramp, breakpoint, and burst load tests. The resulting production architecture runs EmDash on Cloudflare Workers, pairing it with Workers Cache, an object cache backed by Workers KV, and Hyperdrive connected to PlanetScale. This layered caching design serves 99.5 percent of static files and 70 percent of requests from cache, flattening p95 latency under real-world loads reaching 850 requests per second. The migration also introduced a frontend redesign using the Kumo design system and added Model Context Protocol servers for automated agent workflows.
A global payment processor migrating 382 AWS accounts between AWS Organizations encountered control-plane failures when AWS Resource Access Manager automatically severed organization-bound resource shares. Although data planes continued operating for shared resources such as AWS Transit Gateways, infrastructure automation lost the ability to modify dependencies like AWS Glue Data Catalog databases. The engineering team resolved this by implementing temporary retained bridge shares, establishing external principal associations that survived cross-organization account transfers. Following each account move, automated tooling restored durable original shares, validated Lake Formation permissions, and deleted the bridge copies to prevent configuration drift. This bridge-share workflow enabled the migration of 378 accounts before a Transitional Service Agreement deadline without customer downtime.
The post addresses the procedural core of warehouse-to-lakehouse migrations: legacy stored procedures that use temporary tables, cursors, control flow, multi-table updates, and transaction handling while remaining maintained by SQL teams. Using a composite daily-orders procedure based on an Oracle migration, it translates rather than rewrites the logic on Databricks, covering procedure registration in Unity Catalog, session-scoped temporary tables, cursor syntax, handlers, conditional scripting, and atomic transactions. Databricks SQL scripting supports cursors from Runtime 18.1, and BEGIN ATOMIC provides automatic commit or rollback plus row-level conflict detection; catalogManaged tables are required inside atomic blocks. The post says this mechanical translation can cut migration timelines by 50–75% for complex procedures, while preserving business logic and allowing the SQL team to maintain it, though the procedure still needs validation in a migration project.
A major Canadian freight railroad with roughly 20,000 route miles needed to modernize a decades-old analytical estate while handling hundreds of pipelines and rising demand for real-time analytics and AI. Using Databricks Genie Code, Unity Catalog, custom Agent Skills and a Streamlit Databricks App, the team created a metadata-grounded pipeline-generation workflow driven by compact YAML prompts. The workflow discovers schemas, maps source fields, and emits six artifacts—DDL, historical and streaming ingestion, incremental merges, and automated tests—using deterministic patterns for audit columns, deduplication, change-sequence guards and soft-delete reconciliation. Human-reviewed Source-to-Target Mapping preserves business interpretation, while Genie Code handles discovery, orchestration and artifact generation within governed Databricks execution. The reported result was more than 90% automation for new table ingestion, reducing delivery from days per table to minutes and supporting single, multi-table and bulk generation.
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