Skill UI

Expert in designing, building, and optimizing scalable data pipelines, modern data warehouses, and real-time streaming architectures. Mastery covers the complete modern data stack, including Spark, dbt, Airflow, and cloud services (AWS, Azure, GCP). Focuses on ELT/ETL, data lakehouse implementation (Delta Lake, Iceberg), data governance, and ensuring high data quality and operational reliability.

Guides data engineering patterns for ETL pipelines, Spark analytics, and quality checks, showcasing Python and SQL snippets to extract, transform, load, validate, and model sales data in modern warehouses.

An expert in designing, implementing, and optimizing scalable, reliable, and cost-effective data pipelines for both batch and streaming data. Covers advanced topics including ETL/ELT patterns, Kappa/Lambda architectures, workflow orchestration (Airflow/Prefect), transformation using dbt/Spark, and managing modern storage formats like Delta Lake and Iceberg. Focuses heavily on data quality, monitoring, and cost optimization strategies.

This guide provides comprehensive, code-based solutions for diagnosing and fixing common errors encountered in Databricks environments. It covers critical issues such as cluster state failures, Spark OutOfMemory errors, Delta Lake concurrency conflicts, permission problems, and schema mismatches, offering tailored fixes in Python, SQL, and Bash.

A comprehensive guide to setting up a fast, isolated local development loop for data engineering projects using Databricks Connect v2, Asset Bundles, and standard IDEs (like VS Code). This workflow allows developers to run PySpark code locally for debugging and unit testing, while managing resource deployment and ensuring consistency across development, staging, and production environments.

This comprehensive guide details strategies for migrating enterprise data platforms to Databricks. It provides PySpark examples for discovery, schema mapping (converting types from Hadoop, Snowflake, etc.), and executing robust data transfer methods like SYNC, DEEP CLONE, and CTAS. Use this when moving data from legacy sources like Hadoop, Snowflake, or Redshift to a modern Delta Lake environment.

A comprehensive guide detailing optimization techniques for Databricks environments. It covers optimal cluster sizing for various workloads (ETL, ML, Streaming), advanced Spark configurations (AQE, memory management), and deep data optimization strategies for Delta Lake, including Z-Ordering and Liquid Clustering. It also provides methods for query plan analysis and join optimization.

Provides 139 proven marketing ideas for SaaS/software products, guiding you through context-aware recommendations, stage/budget considerations, and implementation tips to spark growth or resolve marketing plateaus.

Automated assistance for pyspark transformer work within data pipelines, activating when you request transformer patterns or best practices and covering ETL, data transformation, orchestration, and streaming scenarios.

A comprehensive skill set for mastering enterprise-grade Scala development. Expertise covers functional programming, distributed systems, and big data processing using technologies like Pekko, Akka, Spark, ZIO, and Cats Effect. Ideal for designing and implementing resilient, scalable microservices based on Domain-Driven Design principles.

Automates Sendspark workflows through Composio's Rube MCP toolkit by searching tools first, ensuring an active Sendspark connection, and issuing RUBE_MULTI_EXECUTE_TOOL calls with schema-compliant arguments.

This skill provides expertise in building production-grade, scalable data systems and pipelines. It covers the full spectrum of data engineering, including designing robust batch ETL/ELT workflows, implementing real-time streaming architectures (Kafka/Flink), and utilizing modern tools like Airflow, Spark, and dbt. Essential for data architecture design, data lakehouse implementation, and data governance.