Loading...
Loading...
Found 30 Skills
Build ETL pipelines and analytics dashboards for Harvard Art Museums API data with MySQL storage and Streamlit visualization
Build end-to-end ETL pipelines with Harvard Art Museums API, SQL analytics, and Streamlit visualization
End-to-end retail ETL pipeline using PySpark, SQL Server, and Medallion Architecture (Bronze/Silver/Gold layers) for data warehousing
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
Implement robust batch processing systems with job queues, schedulers, background tasks, and distributed workers. Use when processing large datasets, scheduled tasks, async operations, or resource-intensive computations.
Use when "Polars", "fast dataframe", "lazy evaluation", "Arrow backend", or asking about "pandas alternative", "parallel dataframe", "large CSV processing", "ETL pipeline", "expression API"
Use this for SQL queries, database schema design, ETL pipelines, data transformations (pandas/Spark), and data validation.
Reference portfolio demonstrating Azure data engineering patterns, Medallion architecture, and end-to-end analytics solutions
Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.
Use when "data pipelines", "ETL", "data warehousing", "data lakes", or asking about "Airflow", "Spark", "dbt", "Snowflake", "BigQuery", "data modeling"
Generate synthetic test data with edge cases for ETL pipeline testing.
End-to-end ETL pipeline and analytics application for Harvard Art Museums API with Streamlit dashboards