Zero unpatched CVEs after migration
Native JSON, vector search, and time series
Faster throughput with Redis 8.x
Granular ACL and RBAC
Native AI and vector search
No more RedisStack module maintenance
Zero unpatched CVEs after migration
Native JSON, vector search, and time series
Faster throughput with Redis 8.x
Granular ACL and RBAC
Meet Security & Compliance Standards
0
Downtime migrations
Downtime migrations
Parallel staging validation and metadata migration in a low-volume window keep production DAGs running throughout every Airflow 3 cutover.
24/7
Managed support
Managed support
Scheduler latency, task failures, and provider connectivity issues are resolved before they impact your pipeline SLAs.
99.99%
Availability goals
Availability goals
Executor configuration, metadata DB failover, and DAG serialisation are tuned for availability under real production conditions.
Airflow 3
Upgrade ready
Upgrade ready
Deprecated operators scanned, Task SDK migration completed, and provider compatibility validated before production is touched.
Upgrade Apache Airflow Version with Ksolves Experts
Airflow 2 support ended on April 22, 2026. No more patches. No more fixes. No more provider updates. Any vulnerability discovered from this point forward stays open permanently. There was no maintenance window, no grace period. Support stopped completely.
Every provider integration you rely on, Snowflake, Databricks, BigQuery, is already requiring Airflow 3. Each new release that drops Airflow 2 support breaks your pipelines with no fix available.
Ksolves delivers your Airflow 2 to Airflow 3 upgrade as a fixed-price Airflow version upgrade service with a written DAG accuracy guarantee and a named team. You know the cost, the timeline, and the engineers before signing anything.
Why Move From Airflow2 to Airflow3?
Six compounding risks that get worse every day after the Airflow 2 eol date of April 22, 2026.
Unpatched Security Vulnerabilities
Every CVE discovered after April 22, 2026 goes permanently unpatched, making your pipelines a direct entry point for breaches.
Compliance and Audit Exposure
SOC 2, PCI-DSS, and HIPAA auditors flag EOL software. Running Airflow 2 can void cyber insurance and block certification renewals.
Provider Package Breakage
Snowflake, Databricks, and BigQuery providers are dropping Airflow 2 support. Your integrations will break without a planned Airflow 3 migration.
Blocked AI and ML Readiness
Airflow 3 delivers event-driven scheduling, asset-driven DAGs, and Human-in-the-Loop workflows. None of these are available in Airflow 2.
Growing Architectural Debt
Every DAG written in Airflow 2 patterns adds to your future refactor scope. Starting earlier costs less.
Shrinking Migration Window
Complex migrations take 6 weeks to 5 months. The EOL date has passed and qualified partners are filling up. Starting late raises cost and risk.
See Exactly What Your Airflow 2 to
Airflow 3 Upgrade Needs
See Exactly What Your Airflow 2 to Airflow 3 Upgrade Needs
Our engineers audit your DAGs, flag every breaking change, and
deliver a firm
Airflow 3 migration plan at no cost.
Airflow 2 vs Airflow 3
A direct comparison of what you have today against what Airflow 3 migration delivers, powered by Ksolves Airflow upgrade services.
| Capability | Apache Airflow 2 | Apache Airflow 3 |
|---|---|---|
| Community support | EOL from April 22, 2026 | Fully supported, active development |
| Security patches | Permanently stopped at EOL | Continuous, community-maintained |
| DAG versioning | Not available natively | Built-in, compare runs across versions |
| Scheduling model | Cron-only | Cron, event-driven, and asset-based |
| Task metadata access | Direct DB session (security risk) | Isolated via Task SDK and REST API |
| Backfill support | CLI only, manual process | Native UI-driven backfills |
| User interface | Legacy Flask App Builder | Fully rebuilt React UI, faster and extensible |
| Provider compatibility | Breaking behind schedule | Snowflake, Databricks, BigQuery fully current |
| Overall posture | EOL, growing risk daily | Secure, modern, future-ready |
Apache Airflow 2
EOL from April 22, 2026
Apache Airflow 3
Fully supported, active development
Apache Airflow 2
Permanently stopped at EOL
Apache Airflow 3
Continuous, community-maintained
Apache Airflow 2
Not available natively
Apache Airflow 3
Built-in, compare runs across versions
Apache Airflow 2
Cron-only
Apache Airflow 3
Cron, event-driven, and asset-based
Apache Airflow 2
Direct DB session (security risk)
Apache Airflow 3
Isolated via Task SDK and REST API
Apache Airflow 2
CLI only, manual process
Apache Airflow 3
Native UI-driven backfills
Apache Airflow 2
Legacy Flask App Builder
Apache Airflow 3
Fully rebuilt React UI, faster and extensible
Apache Airflow 2
Breaking behind schedule
Apache Airflow 3
Snowflake, Databricks, BigQuery fully current
Apache Airflow 2
EOL, growing risk daily
Apache Airflow 3
Secure, modern, future-ready
Our Airflow Upgrade Services
From Airflow 2 to 3 upgrades to post-migration health checks, Ksolves Airflow migration ensures a secure transition.
Airflow 2 to Airflow 3 upgrade
With Airflow 2 EOL approaching, delaying migration risks unpatched vulnerabilities. Airflow 3 introduces a redesigned task execution model, decoupled API server, and asset-based scheduling, all requiring an Airflow DAG refactor. Ksolves handles compatibility assessment, code rewrites, and parallel environment testing before cutover.
On-premise Airflow to Amazon MWAA
MWAA migration involves IAM-based connections, S3-backed DAG storage, VPC networking, and plugin compatibility constraints. Ksolves performs an Airflow DAG refactor to meet managed environment requirements, migrates Connections and Variables to AWS Secrets Manager, and validates all DAG runs before decommissioning the source cluster.
On-premise Airflow to Google Cloud Composer
Cloud Composer enforces GKE-specific package constraints, GCS-based DAG syncing, and DAG parsing timeouts. Ksolves resolves dependency conflicts, migrates Connections to Google Secret Manager, rewrites filesystem-dependent operators, and tunes worker concurrency to match your production throughput requirements.
Airflow to Astronomer (Astro)
Astronomer's Deployments, Workspaces, and Astro CLI represent a distinct operational model from standard Airflow. Ksolves restructures your DAG repository for Astro project layout, configures Deployment-level connections, sets up CI/CD via GitHub Actions or GitLab CI, and enables DAG-level resource isolation through worker queue configuration.
Airflow to Databricks Workflows
Native Databricks Workflows eliminate cross-tool orchestration overhead for Spark jobs, notebooks, and Delta Live Tables pipelines. Ksolves maps DAG dependencies into Workflow task graphs, configures job clusters with autoscaling and spot instance policies, enables Git-backed job definitions, and integrates Slack or PagerDuty alerting.
Airflow metadata database migration and cleanup
Stale DagRuns, TaskInstances, and Logs degrade scheduler performance and inflate storage over time. Ksolves audits your metadata DB, runs retention-based cleanup via Airflow's db clean command, migrates from MySQL to PostgreSQL where needed, and implements log rotation to prevent recurrence.
Our Airflow Upgrade Services
From Airflow 2 to 3 upgrades to post-migration health checks, Ksolves Airflow migration ensures a secure transition.
Airflow 2 to Airflow 3 upgrade
With Airflow 2 EOL approaching, delaying migration risks unpatched vulnerabilities. Airflow 3 introduces a redesigned task execution model, decoupled API server, and asset-based scheduling, all requiring an Airflow DAG refactor. Ksolves handles compatibility assessment, code rewrites, and parallel environment testing before cutover.
On-premise Airflow to Amazon MWAA
MWAA migration involves IAM-based connections, S3-backed DAG storage, VPC networking, and plugin compatibility constraints. Ksolves performs an Airflow DAG refactor to meet managed environment requirements, migrates Connections and Variables to AWS Secrets Manager, and validates all DAG runs before decommissioning the source cluster.
On-premise Airflow to Google Cloud Composer
Cloud Composer enforces GKE-specific package constraints, GCS-based DAG syncing, and DAG parsing timeouts. Ksolves resolves dependency conflicts, migrates Connections to Google Secret Manager, rewrites filesystem-dependent operators, and tunes worker concurrency to match your production throughput requirements.
Airflow to Astronomer (Astro)
Astronomer's Deployments, Workspaces, and Astro CLI represent a distinct operational model from standard Airflow. Ksolves restructures your DAG repository for Astro project layout, configures Deployment-level connections, sets up CI/CD via GitHub Actions or GitLab CI, and enables DAG-level resource isolation through worker queue configuration.
Airflow to Databricks Workflows
Native Databricks Workflows eliminate cross-tool orchestration overhead for Spark jobs, notebooks, and Delta Live Tables pipelines. Ksolves maps DAG dependencies into Workflow task graphs, configures job clusters with autoscaling and spot instance policies, enables Git-backed job definitions, and integrates Slack or PagerDuty alerting.
Airflow metadata database migration and cleanup
Stale DagRuns, TaskInstances, and Logs degrade scheduler performance and inflate storage over time. Ksolves audits your metadata DB, runs retention-based cleanup via Airflow's db clean command, migrates from MySQL to PostgreSQL where needed, and implements log rotation to prevent recurrence.
Our Airflow 2 to Airflow 3 Process
Every deliverable backed by certified data engineers and a written DAG accuracy guarantee as part of every Apache Airflow upgrade service engagement.
Phase 1: Environment Audit (Days 1 to 3)
Document Airflow version, executor, topology, providers, and plugins. Flag every DAG using direct DB sessions, SubDAGs, and deprecated context variables.
Phase 2: Compatibility Analysis (Days 3 to 5)
Run Ruff AIR301/AIR302 across your DAG library. Output: written compatibility gap report ranked by blocking, recommended, and informational severity.
Phase 3: Upgrade Runbook (Days 5 to 7)
Step-by-step airflow dag refactor runbook covering import updates, Task SDK migration, FAB plugin conversion, and executor changes. Reviewed before staging begins.
Phase 4: Staging Validation (Days 7 to 14)
Full airflow 3 migration on staging. Validate DAG parse, task outputs, XCom values, and scheduler latency against Airflow 2 baselines. No production move without a clean pass.
Phase 5: Production Cutover
Metadata migration in a low-volume window, typically under 2 hours. Tested rollback path ready. Live call with your team throughout cutover.
Phase 6: Post-Upgrade Monitoring (48 to 72 Hours)
Monitor scheduler latency, DAG parse times, and provider connectivity. Airflow 2 to airflow 3 upgrade closes with a written handover document.
Our Airflow 2 to Airflow 3 Process
Every deliverable backed by certified data engineers and a written DAG accuracy guarantee as part of every Apache Airflow upgrade service engagement.
Phase 1: Environment Audit (Days 1 to 3)
Document Airflow version, executor, topology, providers, and plugins. Flag every DAG using direct DB sessions, SubDAGs, and deprecated context variables.
Phase 2: Compatibility Analysis (Days 3 to 5)
Run Ruff AIR301/AIR302 across your DAG library. Output: written compatibility gap report ranked by blocking, recommended, and informational severity.
Phase 3: Upgrade Runbook (Days 5 to 7)
Step-by-step airflow dag refactor runbook covering import updates, Task SDK migration, FAB plugin conversion, and executor changes. Reviewed before staging begins.
Phase 4: Staging Validation (Days 7 to 14)
Full airflow 3 migration on staging. Validate DAG parse, task outputs, XCom values, and scheduler latency against Airflow 2 baselines. No production move without a clean pass.
Phase 5: Production Cutover
Metadata migration in a low-volume window, typically under 2 hours. Tested rollback path ready. Live call with your team throughout cutover.
Phase 6: Post-Upgrade Monitoring (48 to 72 Hours)
Monitor scheduler latency, DAG parse times, and provider connectivity. Airflow 2 to airflow 3 upgrade closes with a written handover document.
Your DAGs Will Not Migrate Themselves, Book a Airflow Dag Refactor Scoping Call.
Your DAGs Will Not Migrate Themselves, Book a Airflow Dag Refactor Scoping Call.
Frequently Asked Questions
Apache Airflow 2 EOL is April 22, 2026. After that, no more security patches, bug fixes, or provider updates for the 2.x line. Your pipelines keep running, but every CVE goes permanently unpatched. Ksolves gets you to Airflow 3 before that date.
Yes. Ksolves runs Airflow 2 and Airflow 3 in parallel on staging before any production change. The metadata schema migration runs in a low-volume window, typically under 2 hours. The production cutover includes a tested rollback path throughout, so if anything unexpected surfaces you are not stuck
AWS MWAA, GCP Cloud Composer, Azure Kubernetes, Astronomer, and self-managed Helm. Helm chart updates, provider migration, and CI/CD adjustments are included alongside the Airflow dag refactor in every Ksolves engagement.
To upgrade Apache Airflow version 2 to Airflow 3, you need to run Ruff with the AIR301 and AIR302 rules across your DAG library, migrate all direct database session usage to the Task SDK, convert any FAB plugins to the new plugin model, update provider packages to Airflow 3 compatible versions, and run a full metadata schema migration. Ksolves includes every one of these steps in a structured 6-phase engagement before production is touched.
An Apache Airflow upgrade service engagement covers environment audit, DAG compatibility analysis, provider migration, upgrade runbook, staging validation, production cutover, and 48 to 72 hours of post-upgrade monitoring. Ksolves scopes every engagement upfront with a named team, a fixed price, and a written DAG accuracy guarantee before any work begins.
