Design and implement scalable data pipelines with Spark, dbt, and Airflow.
npx skills add https://github.com/raksok/netherica --skill data-engineer-raksok
Please help me install this Agent Skill. Skill: data-engineer Source: https://github.com/raksok/netherica/tree/main/.agents/skills/data-engineer Command: npx skills add https://github.com/raksok/netherica --skill data-engineer-raksok
Data engineering teams need to design, implement, and maintain robust data pipelines and modern analytics architectures that scale with data volume and variety.
Instantiate a scalable data pipeline using Spark, dbt, and Airflow to ingest, transform, and validate data in your data warehouse.
High-intent search queries and answers about installing and using this skill.
Yes, modern data architecture supports both batch and streaming ETL workloads. You can design and implement scalable pipelines that process real-time analytics while applying data quality checks and governance controls across your cloud environment.
Yes, you can deploy cost-aware data pipelines on cloud platforms. The architecture applies monitoring to your ETL workflows, ensuring scalable data processing while optimizing operational costs across batch and streaming workloads.
Yes, dbt works with Airflow to orchestrate data quality checks. You can integrate dbt transformations within Airflow pipelines to validate data and enforce governance controls during batch and streaming ETL processing.
You set up a modern data stack for real-time analytics by automating scalable data pipeline design. This integrates Spark, dbt, and Airflow to ingest, transform, and validate streaming data in your cloud data warehouse.
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