pluginagentmarketplace/custom-plugin-data-engineer
GitHubpython-programming
Master Python fundamentals, OOP, data structures, async programming, and production-grade scripting for data engineering
big-data
Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads
etl-tools
Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines
api-development
FastAPI, REST APIs, GraphQL, data service design, and API best practices
containerization
Docker, Kubernetes, container orchestration, and cloud-native deployment for data applications
sql-databases
SQL query optimization, schema design, indexing strategies, and relational database mastery for production data systems
nosql-databases
MongoDB, Redis, Cassandra, DynamoDB, and distributed database patterns for scalable applications
data-engineering
Data pipeline architecture, ETL/ELT patterns, data modeling, and production data platform design
mlops
MLflow, model versioning, experiment tracking, model registry, and production ML systems
iac-automation
Terraform, Pulumi, CloudFormation, and infrastructure as code for data platforms
testing-quality
pytest, data validation, Great Expectations, and quality assurance for data systems
cloud-platforms
AWS, GCP, Azure data platforms, infrastructure as code, and cloud-native data solutions
statistics-math
Statistics, probability, linear algebra, and mathematical foundations for data science
deep-learning
PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production
data-warehousing
Snowflake, BigQuery, Redshift, dimensional modeling, and modern data warehouse architecture
cicd-pipelines
GitHub Actions, GitLab CI, Jenkins, and automated deployment pipelines
career-growth
Portfolio building, technical interviews, job search strategies, and continuous learning
llms-generative-ai
LLMs, prompt engineering, RAG systems, LangChain, and AI application development
git-version-control
Git workflows, branching strategies, collaboration, and code management
machine-learning
Python machine learning with scikit-learn, PyTorch, and TensorFlow
monitoring-observability
Monitoring and observability strategy, implementation, and troubleshooting. Use for designing metrics/logs/traces systems, setting up Prometheus/Grafana/Loki, creating alerts and dashboards, calculating SLOs and error budgets, analyzing performance issues, and comparing monitoring tools (Datadog, ELK, CloudWatch). Covers the Four Golden Signals, RED/USE methods, OpenTelemetry instrumentation, log aggregation patterns, and distributed tracing.