ETL / ELT Pipelines
Data pipelines are the backbone of modern data platforms. ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) patterns move data from source systems into analytics-ready destinations.
- Apache Airflow - Workflow orchestration platform for scheduling and monitoring complex DAGs
- Azure Data Factory - Cloud-native data integration service for building ETL/ELT pipelines
- AWS Glue - Serverless ETL service with auto-generated code and crawlers
- dbt (Data Build Tool) - SQL-first transformation framework for analytics engineering
- Batch vs Streaming - Choosing between micro-batch and real-time processing based on latency requirements
Apache Spark & Big Data Processing
Apache Spark is the industry-standard engine for large-scale data processing, supporting batch, streaming, machine learning and graph computation workloads.
- Spark SQL - Structured data processing with DataFrames and Datasets API
- PySpark - Python API for Spark enabling data engineers to work in their preferred language
- Spark Structured Streaming - Unified batch and stream processing on the same engine
- Delta Lake - ACID transactions and time travel on top of data lakes
- Databricks - Unified analytics platform built on top of Apache Spark
Stream Processing & Event-Driven Architecture
Real-time data processing enables organizations to act on data as it arrives rather than waiting for batch cycles.
- Apache Kafka - Distributed event streaming platform for high-throughput, fault-tolerant messaging
- Kafka Connect - Scalable connectors for streaming data between Kafka and external systems
- Apache Flink - Stream processing framework for stateful computations over data streams
- AWS Kinesis - Managed real-time data streaming service on AWS
- Event Sourcing & CQRS - Architectural patterns for event-driven microservices
Data Warehousing & Lakehouse
Modern data architectures combine the best of data warehouses and data lakes into unified lakehouse platforms.
- Snowflake - Cloud data warehouse with elastic compute and storage separation
- Azure Synapse - Unified analytics service combining warehousing and big data
- Google BigQuery - Serverless data warehouse for petabyte-scale analytics
- Lakehouse Architecture - Combining data lake flexibility with warehouse reliability (Delta Lake, Iceberg, Hudi)
- Data Mesh - Decentralized data ownership with domain-oriented architecture
Data Quality & Governance
Ensuring data is accurate, consistent and trustworthy across the entire data lifecycle.
- Great Expectations - Data validation and profiling framework
- Data Lineage - Tracking data from source to consumption for auditability
- Schema Evolution - Managing changes to data schemas without breaking downstream consumers
- Data Catalogs - Centralized metadata management (Azure Purview, AWS Glue Catalog, Datahub)
- CI/CD for Data - Automated testing and deployment of data pipelines
Cloud & Infrastructure
- AWS - S3, Redshift, Glue, Lambda, EMR, Step Functions
- Azure - Data Factory, Synapse, ADLS, Databricks, Event Hubs
- GCP - BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Composer
- Docker & Kubernetes - Containerization for reproducible data pipeline deployments
- Terraform - Infrastructure as Code for provisioning cloud data resources