From automating data pipelines to accelerating machine learning model deployment, Kestra streamlines complex workflows across a wide range of industries and domains.
Say goodbye to delays in your pipeline as Kestra initiates workflows based on specific times or events, such as data arrivals or file uploads.
Efficiently manage and deploy machine learning models at scale using Kestra’s orchestration capabilities.
Move, prepare, and clean data, consolidating it into a single data mart for easy access.
Easily connect and integrate with various data sources, ensuring smooth data flow across your systems.
Automatically detect and process changes in data sources with change data capture, keeping your data up-to-date and accurate.
Configure Kestra to distribute reports via email or Slack, keeping your team informed.
Easily collect data from various sources and integrate it into your workflows.
Manage the entire data lifecycle, from ingestion to reporting, with Kestra’s powerful orchestration capabilities.
Join a prediction log with the outcomes that arrive later, judge only cohorts with enough outcomes, and flag a statistically real drop in AUC or Brier score against the deploy-time baseline.
Validate feature completeness and freshness, then fit a real regression model only if the dataset passes its contract.
Sync Kafka feature events into a Redis online store and DuckDB offline snapshots with drift and freshness gates.
Audit ML training datasets for leaked credentials, API tokens, and covert prompt injection poisoning before fine-tuning.
Block or promote an MLflow registered model based on feature drift (PSI + KS) and training metric regression before it reaches Production.
Drop between scoring and deployment. Audit a classifier per group and intersection, let a reviewer sign off exceptions, and hand an approved flag to the next step.
Monitor MongoDB replica sets for Change Data Capture (CDC) lag, drain oplog backlog, track resume tokens, and alert Slack.
Capture inserts, updates and deletes from a MongoDB collection with a change stream trigger. Upsert flat rows into PostgreSQL with ON CONFLICT, keep deletes as tombstones and verify them against MongoDB.
Join a MongoDB child collection to its parent with $lookup, report the children whose reference resolves to nothing, delete the aged orphans behind an approval, then re-join to prove none is left.
Ingest failed MongoDB mutation dead-letters, deduplicate out-of-order writes, generate atomic bulkWrite NDJSON, and reconcile collections safely.
Find the MongoDB documents that do not match their collection validator, backfill a missing required field behind an approval, then re-read the collection and fail the run if any repairable violator is left.
Detect MongoDB documents sharing a business key no unique index could prevent, keep one survivor per key, delete the surplus behind an approval, then re-group to prove each key is unique.
Detect MongoDB documents older than a retention window, alert Slack, then delete them behind an approval and re-read the collection to prove none remain.
Detect orphaned binary chunks in MongoDB GridFS with $lookup, pause for operator confirmation, purge confirmed orphans, and reverify zero residual storage leaks.
Audit MongoDB system.profile for slow unindexed queries and collection scans (COLLSCAN), alerting DBAs on Slack.
Detect MongoDB documents whose embedded array is growing toward the 16MB BSON limit, archive the overflow, trim each array to its newest entries behind an approval and re-measure to prove it.
Audit MongoDB collections using $indexStats to detect unused secondary indexes consuming RAM, alerting DBAs on Slack.
Dump a MongoDB collection to S3-compatible storage, restore the stored archive into a scratch database, then promote it only when documents and indexes match.
Reconcile a denormalized MongoDB read model against its PostgreSQL source of truth, find orphaned and stale documents by reading the keyset from PostgreSQL, alert Slack, then behind an approval gate delete and refresh them and re-verify.
Audit MongoDB WiredTiger cache pressure via serverStatus, detect dirty byte eviction saturation, file GitHub issues, and alert Slack.
Join a prediction log with the outcomes that arrive later, judge only cohorts with enough outcomes, and flag a statistically real drop in AUC or Brier score against the deploy-time baseline.
Validate feature completeness and freshness, then fit a real regression model only if the dataset passes its contract.
Sync Kafka feature events into a Redis online store and DuckDB offline snapshots with drift and freshness gates.
Audit ML training datasets for leaked credentials, API tokens, and covert prompt injection poisoning before fine-tuning.
Block or promote an MLflow registered model based on feature drift (PSI + KS) and training metric regression before it reaches Production.
Drop between scoring and deployment. Audit a classifier per group and intersection, let a reviewer sign off exceptions, and hand an approved flag to the next step.
Built for Enterprises and SMBs, with strong governance, enterprise-grade security, seamless scalability, and dedicated enterprise support backed by SLAs.
