Python Libraries

Here’s a comprehensive list of the most important Python libraries for data engineering:

1. Pandas – Data manipulation and analysis library
2. NumPy – Numerical computing library
3. PySpark – Python API for Apache Spark
4. Dask – Parallel computing library
5. Polars – Fast DataFrame library, alternative to Pandas for large datasets

1. dbt (data build tool) – Data transformation tool for analytics
2. Apache Airflow – Workflow management platform
3. Prefect – Workflow management system
4. Dagster – Data orchestrator
5. Apache Beam Python SDK – Unified programming model for batch and streaming

1. SQLAlchemy – SQL toolkit and ORM
2. psycopg2 – PostgreSQL adapter
3. pymysql / mysql-connector-python – MySQL adapters
4. pyodbc – For connecting to ODBC databases
5. snowflake-connector-python – For Snowflake connectivity

1. PyArrow – For working with Arrow, Parquet, and other columnar formats
2. fastparquet – Alternative Parquet library
3. boto3 – AWS SDK for S3 and other AWS services
4. azure-storage-blob – For Azure Blob Storage
5. google-cloud-storage – For Google Cloud Storage

1. Requests – HTTP library for API calls
2. FastAPI / Flask – For building APIs
3. pydantic – Data validation and settings management

1. kafka-python / confluent-kafka – For Kafka integration
2. pyspark.streaming – For streaming applications

1. Great Expectations – Data validation and documentation
2. Pandera – Statistical data validation for pandas

1. logging (built-in) – Standard logging library
2. Prometheus Client – For metrics collection