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.
Flag Cassandra base tables stuck at default_time_to_live = 0 in keyspaces meant to expire data, alert Slack, then optionally set a bounded default TTL with ALTER TABLE and verify the schema.
Query Cassandra's system_schema.keyspaces on a schedule, flag any keyspace still using SimpleStrategy replication, and alert Slack when the cluster is unsafely replicated or unreachable.
Flag Cassandra tables whose caching option sets rows_per_partition to ALL or above a ceiling, alert Slack, then rewrite it with ALTER TABLE behind an approval and re-read the schema.
Audit Apache Cassandra table speculative retry policies, pause for operator approval, mitigate read retry storms with safe CQL alterations, and verify alignment.
Audit Cassandra's system_auth.roles for superuser roles with login enabled, alert Slack on exposure, and optionally revoke login with ALTER ROLE.
Flag Cassandra base tables stored uncompressed, alert Slack, then turn on LZ4 compression with ALTER TABLE behind an approval and re-read the schema.
Monitor Cassandra 4.0+ virtual tables on schedule, detect saturated thread pools with blocked or pending tasks, and alert Slack.
Extract Cassandra data with CQL, serialize it to CSV, and load it into Google BigQuery for analytics.
Flag Cassandra tables whose compaction tombstone_threshold is too high to purge tombstones while unchecked_tombstone_compaction is off, alert Slack, then tune both subproperties with ALTER TABLE behind an approval while keeping the compaction class and re-read the schema.
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.
Flag Cassandra base tables stuck at default_time_to_live = 0 in keyspaces meant to expire data, alert Slack, then optionally set a bounded default TTL with ALTER TABLE and verify the schema.
Query Cassandra's system_schema.keyspaces on a schedule, flag any keyspace still using SimpleStrategy replication, and alert Slack when the cluster is unsafely replicated or unreachable.
Flag Cassandra tables whose caching option sets rows_per_partition to ALL or above a ceiling, alert Slack, then rewrite it with ALTER TABLE behind an approval and re-read the schema.
Audit Apache Cassandra table speculative retry policies, pause for operator approval, mitigate read retry storms with safe CQL alterations, and verify alignment.
Audit Cassandra's system_auth.roles for superuser roles with login enabled, alert Slack on exposure, and optionally revoke login with ALTER ROLE.
Flag Cassandra base tables stored uncompressed, alert Slack, then turn on LZ4 compression with ALTER TABLE behind an approval and re-read the schema.
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. |
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