nexla-data-flows-operator

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Build, deploy, monitor, and troubleshoot production Nexla data pipelines via Python SDK or REST API. Use for flow setup, data transformation pipelines, schema management, access control, credential rotation, batch operations, error recovery, monitoring, CI/CD integration, and operational troubleshooting.

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[Databases and Warehouses]

When & Why to Use This Skill

This Claude skill enables seamless management of Nexla data pipelines, allowing users to build, deploy, and monitor complex data flows programmatically via Python SDK or REST API. It provides a robust framework for automating schema management, error recovery, and CI/CD integration, significantly reducing the manual overhead of data engineering and integration tasks.

Use Cases

  • Automated Pipeline Lifecycle: Streamline the creation and deployment of end-to-end data flows (source to destination) with automated validation and rollback support for production environments.
  • Proactive Monitoring and SLA Tracking: Implement automated health checks and metric polling to detect pipeline failures or latency issues, ensuring high data reliability and uptime.
  • Operational Troubleshooting: Rapidly diagnose and resolve data flow errors by programmatically analyzing logs and metrics, utilizing built-in retry strategies and circuit breakers.
  • Security and Access Governance: Automate credential rotation and manage granular team permissions across data resources to maintain strict security standards and audit trails.
  • CI/CD Integration for Data Ops: Integrate data pipeline deployments into existing DevOps workflows to ensure idempotent updates and consistent environment configurations.
name"nexla-data-flows-operator"
description"Build, deploy, monitor, and troubleshoot production Nexla data pipelines via Python SDK or REST API. Use for flow setup, data transformation pipelines, schema management, access control, credential rotation, batch operations, error recovery, monitoring, CI/CD integration, and operational troubleshooting."

Requirements: Python >= 3.8, nexla_sdk >= 2.0.0 | License: Apache-2.0

What this skill is for

  • Build or modify Nexla pipelines end-to-end: credential → source → nexset → destination → flow.
  • Operate and troubleshoot active data flows with repeatable checks and safe retries.

When to use this skill

  • Build flows: Create credential → source → nexset → destination → flow pipelines
  • Transform pipelines: Create reusable transforms, apply to nexsets, validate output
  • Access control: Grant team/user access, manage permissions, audit changes
  • Production automation: CI/CD deployment, batch updates, scheduled operations
  • Error recovery: Retry strategies, circuit breakers, transient failure handling
  • Monitoring: Health checks, metrics tracking, alerting, SLA monitoring
  • Advanced workflows: Credential rotation, schema migration, data quality checks
  • Troubleshooting: Debug flow failures, analyze logs/metrics, recover from errors
  • Webhooks: Push data to Nexla via webhook sources
  • Async tasks: Manage background jobs, exports, imports
  • AI integration: Configure GenAI for documentation suggestions

Quick start

  1. Set env vars (see .env template in EXAMPLES.md).
  2. Run python scripts/nexla_quickstart.py to validate auth and list resources.
  3. Use the step-by-step recipes in EXAMPLES.md.

Available scripts

  • scripts/list_resources.py: List/filter resources by type or name.
    • python scripts/list_resources.py --type sources --name "orders" --limit 5
  • scripts/deploy_flow.py: Deploy flow config with validation and rollback.
    • python scripts/deploy_flow.py --print-schema
  • scripts/get_resource_logs.py: Fetch flow logs for a resource run.
    • python scripts/get_resource_logs.py --resource-type data_sets --resource-id 123
  • scripts/manage_access.py: Manage access control for resources.
    • python scripts/manage_access.py --operation grant --resource-type sources --resource-id 123 --accessor-type TEAM --accessor-id 42 --role operator

Decision framework: REST vs SDK vs Scripts

Scenario Best Choice Rationale
One-time setup REST (cURL) Quick ad-hoc commands, no dependencies
Repeatable workflows Python SDK Type safety, retries, pagination, error handling
Production deployment Scripts (in this skill) Tested patterns, error recovery, idempotency
CI/CD integration Scripts + SDK Automated deployment, validation, rollback
Monitoring/health checks Scripts + SDK Scheduled polling, alerting, SLA tracking
Debugging/troubleshooting REST + Scripts Quick diagnostics + systematic debugging

Production readiness checklist

Before deploying flows to production, ensure:

  • Credentials validated via probe() before use
  • Idempotency: search by name/tag before create operations
  • Error handling: wrap all operations in try/except with retry logic
  • Flow isolation: pause flows before structural changes, activate after validation
  • Monitoring: set up health checks, metric polling, alerting
  • Access control: configure accessors, verify permissions
  • Audit trail: enable logging, track resource changes
  • Rollback plan: test flow pause/copy/delete procedures
  • Rate limiting: implement backoff, respect retry-after headers
  • Secrets management: use env vars, never commit credentials

Error resilience patterns

  • Transient failures (429, 5xx): Use exponential backoff retry (see scripts/retry_helpers.py)
  • Credential errors: Probe before use, implement rotation workflow
  • Transform failures: Validate on samples, test incrementally
  • Flow activation failures: Check upstream dependencies, verify access
  • Rate limits: Respect retry_after, use circuit breakers for sustained errors
  • Partial failures: Implement checkpoint/resume patterns for batch operations

See REFERENCE.md → Error Handling Deep Dive for implementation patterns.

Monitoring strategy

  • Health checks: Poll flow status, check last run timestamp (see scripts/health_check.py)
  • Metrics tracking: Daily aggregates, run-level summaries, error rates
  • Alerting: Detect failures, SLA breaches, credential expiry
  • Debugging: Analyze run logs, compare successful vs failed runs
  • SLA tracking: Monitor latency, throughput, success rate

See REFERENCE.md → Monitoring & Observability for detailed patterns.

Where to go deeper

  • Technical deep dives: REFERENCE.md (error handling, retry strategies, monitoring, advanced workflows, webhooks, async tasks, GenAI)
  • Transform & schema patterns: TRANSFORMS.md (reusable transforms, attribute transforms, schema validation)
  • Access control patterns: ACCESS_CONTROL.md (team access, permission management, audit)
  • Copy-paste recipes: EXAMPLES.md (18 recipes covering build, deploy, transform, access, monitor, webhooks, async tasks, GenAI)
  • Production scripts: scripts/ directory (deployment, health checks, batch operations, access management)
  • Quick validation: Run python scripts/nexla_quickstart.py to verify auth and connectivity
nexla-data-flows-operator – AI Agent Skills | Claude Skills