danielbeach/data-engineering-practice
Data Engineering Practice
Data engineering practice repository providing tutorials, distributed processing engines, and Python data pipeline automation scripts. The system encompasses automated data validation, distributed compute aggregation, embedded columnar querying, lazy evaluation planning, partitioned storage export, and cloud storage retrieval.
The capability surface covers cloud integration and storage, data engineering and pipelines, data processing and analytics, data quality and testing, database and storage, file management, and monitoring and observability.
Features
- Distributed Computing Engines - Loads large-scale files into distributed compute engines to perform parallel data transformations and numerical aggregations.
- Practice Problem Repositories - Provides hands-on tutorials and exercises for learning data pipeline design and SQL analytics.
- Automated Data Validation - Inspects incoming datasets with automated checks to prevent downstream pipeline failures.
- Data Quality and Validation - Validates incoming datasets against automated schema rules and structural constraints prior to pipeline execution.
- Analytical Query Engines - Executes analytical queries and data transformations using lightweight embedded columnar database engines.
- In-Database Columnar Engines - Queries columnar data locally with lightweight embedded database engines optimized for analytical transformations.
- Columnar Storage - Exports processed query results into partitioned columnar storage formats organized by attributes.
- Parquet Exporters - Saves query results into partitioned columnar storage formats organized by specific data attributes.
- Distributed Data Processing Engines - Runs lazy evaluations, transformations, and numerical aggregations across large-scale tabular datasets.
- Distributed Data Processing - Loads large-scale files into distributed compute clusters to execute parallel transformations.
- File Data Ingestion - Reads comma-separated values from local files to load raw records into structured database tables.
- Python Data Pipeline Frameworks - Executes automation scripts for downloading, extracting, validating, and transforming remote datasets.
- Relational Schemas - Builds database table definitions, primary keys, and indexes for relational storage.
- Tabular Data Analysis - Aggregates file-based records to calculate daily counts, weekly statistical summaries, and historical trends.
- Cloud Storage - Downloads and retrieves compressed datasets from remote cloud storage buckets for local processing.
- Validation Pipelines - Checks incoming datasets for structural anomalies and format errors before downstream processing.
- Data Pipeline Alerting - Triggers notifications when incoming records violate expected business rules or exhibit extreme outliers.
- Cloud Storage Clients - Interacts with remote cloud storage buckets via API clients to coordinate multi-step download workflows.
- Column Transformation - Applies conditional string manipulation, regular expression parsing, type casting, and hashing functions to enrich columns.
- DataFrame Ingestion - Retrieves remote files via HTTP, loads them into dataframes, and queries records to extract insights.
- Large Dataset Processors - Processes datasets exceeding available system memory using streaming file lines.
- Lazy Evaluation Frameworks - Processes memory-constrained datasets efficiently using deferred evaluation and optimized query planning.
- Lazy Query Execution - Defers computation and builds optimized execution graphs to process larger-than-memory datasets efficiently.
- Compressed HTTP Extractions - Fetches compressed files over HTTP and extracts their contents locally using automated scripts.
- Format Conversions - Traverses directory structures to translate nested files into flat tabular formats.
- Archive Retrievals - Downloads compressed archives from cloud storage buckets and extracts contents for downstream processing.
