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.
Download a JSON export of embeddings, reshape it into Pinecone's vector format with Python, upsert the file in one call, and report the count to Slack.
Fetch the requested vector ids, delete them, fetch again to prove absence, and post a before-and-after audit message to Slack.
Scheduled DescribeIndexStats check that compares the total vector count against a threshold, alerts Slack, and fails the execution on a breach.
Clear every vector in a stale Pinecone namespace with one deleteAll call, verify with a stats readback, and confirm the purge in Discord.
Create a Pinecone serverless index with a fixed dimension, cloud, and region, verify it with a stats readback, and confirm the bootstrap in Slack.
Query a Pinecone index by an existing vector's id, pull the top five neighbors with metadata, and post the match count to Discord for retrieval QA.
Audit Apache Pinot real-time table consuming segments for stalled commits, recycle wedged partition workers, and verify ingestion.
Classify an Apache Pinot table HEALTHY, STALE, or EMPTY_TABLE from its own ingestion lag, and alert Slack when a streaming consumer stalls.
Move Apache Pinot query results into BigQuery as a clean, schema-typed CSV load.
Join features to labels as they were known at each label's cutoff with a DuckDB ASOF join, audit the set for future rows and correlation lift, then publish it or block it.
High-performance data transformation with Polars, SQL quality gating with DuckDB, and publication to AWS S3 Lakehouse.
Ingest raw files, run streaming LazyFrame transformations with Polars, validate schema rules, and sink partitioned Parquet with Slack alerts.
Join GeoJSON points to service zones, including boundaries, and export matched, unmatched and ambiguous assignments as CSV.
Repair polygon batches in PostGIS, quarantine unsuitable features and approve the exact report before saving a snapshot.
Find PostgreSQL tables with autovacuum switched off or a lax vacuum scale factor, and a server with autovacuum off, alert Slack with the dead rows, and optionally reset, vacuum and verify.
Monitors transaction freeze horizon in pg_database to prevent emergency read-only lockups.
Schedule pg_dump database archives to Amazon S3 and test-restore them into a scratch database to verify backup integrity.
Daily and on-demand PostgreSQL backup pipeline using pg_dump and gzip, streaming to Amazon S3 with size verification and Slack alerting.
Detect PostgreSQL lock conflicts and cascading uncommitted blockers via pg_locks, alerting DBAs on Slack before connections exhaust.
Audit disabled integer foreign-key triggers, count existing orphan rows then restore enforcement under bounded locks and approval.
Download a JSON export of embeddings, reshape it into Pinecone's vector format with Python, upsert the file in one call, and report the count to Slack.
Fetch the requested vector ids, delete them, fetch again to prove absence, and post a before-and-after audit message to Slack.
Scheduled DescribeIndexStats check that compares the total vector count against a threshold, alerts Slack, and fails the execution on a breach.
Clear every vector in a stale Pinecone namespace with one deleteAll call, verify with a stats readback, and confirm the purge in Discord.
Create a Pinecone serverless index with a fixed dimension, cloud, and region, verify it with a stats readback, and confirm the bootstrap in Slack.
Query a Pinecone index by an existing vector's id, pull the top five neighbors with metadata, and post the match count to Discord for retrieval QA.
Built for Enterprises and SMBs, with strong governance, enterprise-grade security, seamless scalability, and dedicated enterprise support backed by SLAs.
