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133 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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To support its expansion from Homes into Experiences and Services, Airbnb evolved its offline data warehouse architecture to balance domain-specific needs with organizational consistency. Rather than enforcing a single global pattern, data teams adopted a framework of foundational principles that paired separate data models for unique product features with monolithic models for cross-cutting domains like payments and messaging.
Palantir’s document store needs to repair or rebuild Elasticsearch indices when mappings, settings, cluster versions, or index state change, while continuing to serve live search and writes. Its reindex machinery reconstructs schema-aware indices from the strongly consistent database, rather than relying on Elasticsearch’s _reindex API, and is designed to limit manual work and resource consumption across many deployments and schemas. For an online reindex, it creates a physical shadow index with the intended mappings and settings, backfills it from the database, dual-writes current API changes to both indices, and promotes the shadow index after completion; searches remain on the primary during the rebuild. Cross-cluster migrations use a separate machine with cluster-specific metadata, configuration, index and alias evolution, and preservation of old-cluster indices to support rollback.
Part 2 describes how to build a repeatable Apache Spark replay harness for testing high-impact microservices against historical, production-shaped inputs without reimplementing the service. The design turns dependency state—such as database snapshots, versioned rules, merchant configuration, eligibility state, and logged service responses—into datasets, while representing side effects as output rows and matching fidelity to the decision under test. A thin Spark wrapper reconstructs ReplayInput objects, invokes the same deterministic decision engine used online, compares current and candidate results, and writes traceable diffs with intermediate values and rule explanations. The source cautions that incomplete context, distributed state, asynchronous workflows, and external actors limit replay’s predictive power, so it should model explicit assumptions rather than claim full production simulation. It presents migrations as a practical point to establish these boundaries and use replay to reduce risk.
Spotify experienced rapid codebase growth alongside widespread adoption of AI coding tools, resulting in a 76% increase in pull request frequency. To manage fleet-wide codebase maintenance and complex refactoring, the company combined its Fleetshift automation platform with Honk, a background coding agent powered by Claude. Honk runs inside Kubernetes pods with access to CI build environments and integrates with Backstage via Model Context Protocol tools to inspect component metadata. Standardized service architectures and active lint guardrails enable the agent to maintain consistency and auto-correct invalid design patterns during execution. By automating routine migrations, Spotify reduced fleet-wide Java backend updates from multi-week cross-team efforts down to three days handled by a single engineer.
Traditional ERP practice prioritizes a standardized, stable core, but the post argues that forcing every process into it can erode an organization’s identity and differentiated ways of creating value. It proposes keeping the ERP as the system of record while placing an interoperability layer above it. This layer uses a shared ontology to reconcile concepts across systems and supports workflows, automation, user experiences, agentic AI, and human engineering under shared security and governance. Because these capabilities remain outside the core, organizations can preserve distinctive processes during ERP upgrades and treat migration as a continuous software- and ontology-driven activity rather than a one-time manual exercise. The intended model is structured adaptability: standardized ERP functions retain discipline, auditability, and financial integrity while operational software adapts to the business.
Sentry rebuilt its marketing site and blog of roughly 2,500 pages, replacing a legacy Gatsby setup and traditional headless CMS with Astro, Markdown, Frontmatter, and AI-driven automation. The previous architecture suffered from 14-minute build times, restrictive CMS schema limits, and frequent build failures caused by external API dependencies. A team of 2.5 developers executed the migration in two months using Claude Code agents for data extraction, template translation, and testing. To manage content without a CMS dashboard, Sentry built Claude Skills to guide updates, run local previews, and draft GitHub pull requests. The migration reduced average build times to under 4 minutes, cut broken staging builds by 95 percent, and improved Web Vitals scores from 89 to 97.
ClickHouse Cloud has launched a fully managed Postgres service in beta to combine transactional and analytical workloads within a single platform. Backed by local NVMe storage, the service provides up to ten times faster transactional performance alongside native change data capture into ClickHouse. Developers can query across both transactional and analytical layers using the pg_clickhouse extension without managing custom data pipelines. The platform also offers managed migration workflows via ClickPipes, database branching, point-in-time recovery, high availability with up to two standbys, and support for over ninety PostgreSQL extensions. More than fifty virtual machine configurations are available, ranging from single vCPU instances to clusters with ninety-six vCPUs and sixty terabytes of NVMe storage.
As Yelp's JavaScript monorepo expanded over time, Webpack build speeds became a significant engineering bottleneck. To resolve this without rewriting complex configurations, the team selected Rspack for its direct compatibility with existing Webpack setups and plugins. Yelp implemented an adapter pattern to generate Rspack configurations dynamically from original Webpack files, enabling a staged opt-in rollout across development tools and CI environments. Following the initial migration, engineers addressed performance issues in barrel files using automated codemods to replace star re-exports and import-then-export patterns with named re-exports. Combined with Rspack's portable persistent caching, the migration reduced integration build times by approximately 52 percent and warm cache builds by up to 80 percent.
Airbnb migrated its massive identity graph from a third-party graph database vendor to a unified, in-house knowledge graph platform to solve long-tail latency and scaling bottlenecks. Built on JanusGraph with AWS DynamoDB for persistence and OpenSearch for indexing, the new architecture decouples storage operations from graph traversal logic. The migration improved P99 query latency, eliminated routine instance reboots, and supported a tenfold increase in write throughput.
Deutsche Börse Group is migrating a high volume of Zeppelin notebooks used by more than 2,000 users from Cloudera to Databricks, after Cloudera’s 2027 Zeppelin decommissioning and the group’s move to cloud analytics. Because the notebooks combine SQL, Python, custom interpreters, data-source references, visualizations, widgets, and scheduling logic, the team rejected fully rule-based rewriting. Its Zeppelin to Databricks Notebook Converter, delivered as a Databricks App, deterministically maps paragraphs to cells, translates interpreter syntax, reformats metadata as .ipynb JSON, and preserves original content. A generated, environment-specific prompt then gives Genie context about interpreters, data sources, and configuration patterns so it can reconstruct logic, ask clarifying questions, and handle the variable work. The hybrid workflow reduced redevelopment to 15–20 minutes per notebook, while keeping SQL, Python, visualizations, widgets, Oracle and HDFS references, scheduling, and custom business logic untouched; large-scale testing is next.
ChatFeatured, a Toronto-based startup, helps brands track and improve how AI models discover and recommend them, including by analyzing cited sources and guiding content production. As prompt ingestion grew to about 1,000 prompts daily across six AI models, managed Postgres instances became slow, CPU reached 90% during ingestion windows, and PlanetScale hit an IOPS limit; analytics queries remained the deeper constraint. The team migrated from PlanetScale to Postgres managed by ClickHouse using the Postgres-to-Postgres ClickPipes connector, which handled initial load, schema transfer, continuous sync, and a five-minute maintenance window; the migration took 30 minutes overall. This setup retains Postgres for transactional workloads while providing a built-in path to ClickHouse for analytics, and a 30-day LifestyleRx query fell from about two and a half minutes to less than a second, with 20x compression reported for CDN log data.
PipelineRL migrated its rollout inference engine from vLLM V0 (0.8.5) to V1 (0.18.1), but trainer-side logprobs, policy ratios, KL, entropy, clip rate, and reward diverged from the V0 reference. The investigation separated semantic mismatch, inference-path mismatch, and objective mismatch, then prioritized backend parity before changing the RL objective. Fixes included processed rollout logprobs, explicit V1 runtime settings disabling prefix caching and async scheduling, a V0-like inflight weight-update path, and an fp32 lm_head for the final projection. With those changes, the final V1 run tracked the V0 trajectory across the reported metrics, while processed_logprobs alone did not resolve the training gap.
Slack's data platform accumulated over 700 SSH-based Airflow operators executing jobs directly on AWS Elastic MapReduce clusters, creating broad security surfaces and operational instability. Stateful SSH connections frequently failed when Kubernetes pods restarted, leaving orphaned zombie processes and preventing infrastructure modernization such as migrating EMR clusters to child accounts. To eliminate SSH access, Slack adopted a REST-based architecture using Quarry, a gateway providing unified REST APIs and token authentication across compute engines. For non-Hadoop CLI workloads, Slack leveraged YARN Distributed Shell to execute arbitrary shell scripts stored in Amazon S3 within managed, isolated YARN containers. The phased migration achieved zero downtime across eight data regions, unlocking reliable monitoring, simplified onboarding, and future initiatives like Spark on Kubernetes.
Supabase has made dashboard-based database branching the default for all projects, adding a GitHub-free alternative to its existing git-based workflow. Developers can create a branch from the dashboard, work in the SQL Editor or Table Editor against a separate Postgres instance, review a schema diff, and merge changes into production without Git configuration. The workflow uses pg-delta, a new schema-diffing engine in pg-toolbelt that covers tables, columns, RLS policies, functions, triggers, indexes, and extensions, replacing migra as dashboard branching’s default engine; pg-delta is alpha software and is also available in the CLI behind a flag. Git-based branching remains fully supported, and the two approaches can be switched between or used together, while branches created through the Supabase MCP server use dashboard branching automatically.
Blockchain gaming platform Gala struggled to ingest and query expanding volumes of telemetry and blockchain data on its Databricks-based data infrastructure, resulting in multi-minute query times and heavy engineering maintenance overhead. To solve these bottlenecks, the company migrated its data platform to ClickHouse on AWS, ingesting data through Kafka, Airbyte, Amazon S3, and Fivetran. Gala leveraged ClickHouse's indexing capabilities alongside saved queries and API endpoints to power company-wide Metabase business intelligence dashboards for non-technical teams. Following the migration completed in December 2024, data capacity expanded from 3 TB to 9 TB while query times dropped from minutes to sub-second on previously unoptimized tables. The switch also lowered initial infrastructure costs by 30 percent, with plans underway to implement ClickHouse ClickPipes for improved data processing pipelines.
NAB standardized 6,000 developers on Cursor after evaluating Amazon Q and GitHub Copilot, and is expanding access to more than 10,000 technology employees. The bank uses Cursor across legacy modernization and new development: BizCalc's Silverlight/.NET monolith is being refactored into Java microservices and React, while Assembly-based core-banking programs are being migrated using generated flowcharts and business summaries. Engineers use Ask Mode, Plan Mode, custom NAB CEL skills, Composer, and Opus coding models to document systems, produce requirements and API specifications, parallelize implementation, and test changes. NAB reports that both modernization efforts are progressing three times faster than expected, while a hardware-agnostic payment app was built in under three weeks rather than four months; one team reported a 5–8x velocity improvement.
ClickHouse Cloud transitioned its Google Cloud infrastructure from x86 N2D instances to Arm-based Google Axion C4A instances to resolve CPU throttling and performance ceilings under peak query loads. The migration leverages Axion's dedicated physical cores per vCPU, higher memory bandwidth, local SSDs, and improved thermal and clock frequency stability. In automated ClickBench testing across 100 million rows, every query in the 43-query benchmark improved by 30% to 55%, while data load times decreased by approximately half. A production analysis of 38 migrated customer services demonstrated an average 15% reduction in compute credits consumed per query across diverse production workloads. ClickHouse Cloud executed this rollout using automated validation checks, scheduled maintenance windows, and capacity coordination with Google Cloud.
Brazilian payment processor Trio faced severe data challenges after scaling and acquiring PayBrokers, processing over 243 million payments in early 2025 across disparate legacy databases. Because financial reconciliation and compliance demand absolute precision, the engineering team unified their analytics layer by migrating to ClickHouse Cloud. To handle out-of-order and duplicate events from their Redpanda streaming pipelines, Trio implemented sliding-window refreshable materialized views alongside ReplacingMergeTree engines rather than recomputing entire historical datasets. A custom ETL service manages schema drift before inserting clean records into ClickHouse, while batch ingestion is coordinated through Airflow. Consequently, Trio reduced storage consumption by approximately 88% through columnar compression, ingested a 5-billion-row migration without issue, and maintained real-time query performance across more than one billion daily ledger data points.
An IDC survey of 1,400 organizations across eight Asia/Pacific markets reveals that legacy architectures and accumulated data debt severely hinder enterprise artificial intelligence initiatives. While 43% of surveyed organizations cite legacy systems as a major obstacle to application development, 95% report project delays and 90% have experienced failed modernization attempts. In contrast, high-performing organizations treat modernization as a continuous discipline across people, processes, and cloud-centric data platforms capable of processing both structured and unstructured workloads. These leading enterprises achieve nearly three times more digital revenue growth than peers still constrained by technical debt. IDC notes that addressing technical and data debt proactively avoids escalating AI failure rates and delivers superior return on investment compared to superficial lift-and-shift migrations.
Documentation platform Mintlify experienced major dashboard performance degradation and rate limiting under PostHog as multi-tenant analytics traffic grew rapidly. Because PostHog lacked incremental materialized views, queries scanned full raw datasets and took tens of seconds to load. Mintlify migrated its customer-facing analytics infrastructure to ClickHouse Cloud, ingesting events through Amazon MSK and ClickPipes into a single events table backing incremental materialized views. The team executed the transition over two weeks by dual-writing events and replicating the existing PostHog schema to preserve API surface compatibility. The migration reduced dashboard query latency to sub-one-second, decreased related support bug reports to zero, lifted NPS by an estimated 30%, and lowered analytics infrastructure costs by approximately 60%.
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