One platform for data, AI, and business-critical workflows.
Kestra has streamlined our data processes, reduced costs, and significantly enhanced our scalability and efficiency. It has truly been a critical asset in our digital transformation journey.
Reported by teams switching from Airflow to Kestra in production.*
Self-hosted Kestra runs on minimal infrastructure. No overprovisioned workers or complex component scaling. Deploy on your existing Kubernetes cluster or a single VM.
Eliminate weeks of operational overhead. No upgrades breaking plugins, no dependency conflicts, no executor tuning. Ship pipelines, not infrastructure.
Many orchestrators lock enterprise features behind expensive managed plans. Kestra delivers them in open source or flexible commercial licensing.
Kestra Copilot turns natural language into working pipelines using dbt, Airbyte, Spark, Snowflake, BigQuery, Databricks, and2,600+ plugins.
Pick from our production-ready Blueprints for common data workflows. Copy, customize, deploy in minutes.
Geocode a CSV of US addresses to longitude, latitude and state, county, tract and block FIPS codes with the free Census Geocoder batch endpoint, one multipart POST per run, and keep every answer in a Parquet cache so re-runs only send the addresses not seen before.
Fetch the Ceph Dashboard's own full health report and alert Slack on HEALTH_WARN or HEALTH_ERR, read-only.
List Ceph pools, alert Slack on any replicated pool below a minimum replica size, and optionally raise it back up.
List an RBD image's snapshots via the Ceph Dashboard API and alert Slack on any that are not protected, read-only.
Reverse, de-duplicate newest-wins, filter tombstones, split into partitions processed by a worker subflow, then reassemble a clean file and a rejects file. No script, only core storage tasks.
Score each change request on service criticality, blast radius, size, backout plan, testing and the team's failure rate, catch freeze windows and colliding changes, and route it to pre-approval, peer review or the CAB.
Build a RAG chat-with-your-data flow that retrieves answers from Elasticsearch and generates grounded responses with OpenAI.
Audits Chroma collections to validate HNSW distance metrics and prevent RAG retrieval corruption.
Keep a Chroma collection in sync with a Markdown docs repository, embedding only changed sections locally with FastEmbed, deleting stale chunks, and failing the run if retrieval regresses.
Automated ChromaDB collection preflight validation, paginated vector extraction to Parquet, and AWS S3 disaster recovery backup.
Audit Chroma vector collections for tombstoned and dangling embeddings, archive quarantined documents to KV, safely evict stale vectors with human approval, and verify post-deletion state.
Check a list of DOIs against Crossref each week, flatten the retraction, expression-of-concern and correction notices Crossref and Retraction Watch attach to each paper, report every notice once using the KV store, flag DOIs Crossref does not know, and alert in Slack with the notice title and link.
Find Citus distributed tables that should share a colocation group but do not, which turns their joins into repartition joins and blocks foreign keys between them, then re-colocate them behind an approval and prove it from pg_dist_partition.
Find Citus workers left with isactive false in pg_dist_node after an outage, prove each one answers a query right now, then activate the healthy ones behind an approval and read isactive back to confirm the cluster is wide again.
Find Citus workers that are missing a reference table after citus_add_node, list the copies that would run, then call replicate_reference_tables behind an approval and fail the run if a worker is still short.
Measure per-worker shard count and bytes on a Citus cluster, report an empty worker or a byte skew over a threshold, print the moves Citus would make, then rebalance behind an approval and verify the skew improved.
Sync only the datasets that changed on a CKAN open-data portal since the last run, download their CSV files in parallel and convert each to Parquet with DuckDB, with a KV-stored cursor.
Diff a CKAN open dataset's DataStore schema, licence and row count against the last approved snapshot every day, let Gemini explain breaking changes, pause for a human only when a change would break downstream loads, and keep a DuckDB ledger of every check and decision.
Audit ClickHouse asynchronous insert buffer memory and flush delays via system.asynchronous_inserts, gate operator queue flushes, and alert on backpressure.
Periodically audit ClickHouse system.backups to detect failed snapshot creations, enforce backup recency SLAs, and alert Slack on anomalies.
Geocode a CSV of US addresses to longitude, latitude and state, county, tract and block FIPS codes with the free Census Geocoder batch endpoint, one multipart POST per run, and keep every answer in a Parquet cache so re-runs only send the addresses not seen before.
Fetch the Ceph Dashboard's own full health report and alert Slack on HEALTH_WARN or HEALTH_ERR, read-only.
List Ceph pools, alert Slack on any replicated pool below a minimum replica size, and optionally raise it back up.
List an RBD image's snapshots via the Ceph Dashboard API and alert Slack on any that are not protected, read-only.
Reverse, de-duplicate newest-wins, filter tombstones, split into partitions processed by a worker subflow, then reassemble a clean file and a rejects file. No script, only core storage tasks.
Score each change request on service criticality, blast radius, size, backout plan, testing and the team's failure rate, catch freeze windows and colliding changes, and route it to pre-approval, peer review or the CAB.
Fivetran
Snowflake
Databricks
Azure
DBT
Airbyte
MongoDB
Cassandra
PostgreSQL
MySQL
Redshift
BigQuery
ClickHouse
Redis
Elasticsearch
Kafka
Pulsar
RabbitMQ
Fivetran
Snowflake
Databricks
Azure
DBT
Airbyte
MongoDB
Cassandra
PostgreSQL
MySQL
Redshift
BigQuery
ClickHouse
Redis
Elasticsearch
Kafka
Pulsar
RabbitMQ
| Legacy orchestrators (e.g. Airflow) | Modern alternatives | Why this matters | ||
|---|---|---|---|---|
| Workflow definition | Declarative YAML | Python DAGs | Python SDKs with custom abstractions | Your existing scripts work as-is. Zero migration tax. |
| Languages supported | Any (Python, SQL, R, Bash, etc.) | Python only | Python only | Analysts use SQL, engineers use Python, ops use Bash. |
| Time to first pipeline | < 5 minutes | 1-2 hours | 30-60 minutes | Ship 10x faster. Deliver value day one. |
| Learning curve | YAML (immediate) | Framework + infrastructure setup | Framework concepts and abstractions | Junior engineers ship pipelines their first day. |
| Live DAG preview | Yes, real-time | No (deploy to see) | No (deploy to see) | See changes instantly. No deploy-to-preview cycles. |
| Event-driven triggers | Native, unlimited | Limited sensors (polling-based) | Yes, native support | React in seconds, not minutes, with real-time workflows. |
| Execution isolation | Containers per task | Shared workers | Configurable | No dependency conflicts. Ship fearlessly. |
| Built-in observability | Metrics, logs, tracing included | Requires manual setup | Setup required or cloud-dependent | Debug in minutes with full visibility out of the box. |
| Multi-language team support | SQL, Python, Bash all native | Python wrappers required | Python wrappers required | Everyone contributes using their native tools. |
| Scope beyond data | AI, infra, business workflows | Data pipelines only | Data pipelines primary focus | Future-proof your platform with one orchestrator for everything. |
| Enterprise deployment | Self-hosted or cloud | Self-managed infrastructure | Self-hosted or cloud-first | 50%+ lower TCO. Less infrastructure, less ops time. |
| Migration path | Incremental (run Airflow + Kestra in parallel) | N/A | Requires full rewrite | Move pipelines one by one, no big-bang cutover. |
Join 500+ data teams who’ve modernized their orchestration with Kestra.
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