Big Data & Analytics
Architectural patterns for processing, transforming, and deriving value from large-scale data — from batch pipelines to real-time streaming analytics.
Lambda Architecture
Batch and speed layers for processing large-scale data with low-latency queries.
Kappa Architecture
Simplifying Lambda by using a single streaming layer for all data processing.
Apache Spark Architecture
RDDs, DataFrames, Spark SQL, and the unified analytics engine for big data.
Real-Time Analytics
Stream processing with Kafka Streams, Flink, and real-time OLAP databases.
Data Pipeline Patterns
Batch, streaming, micro-batch, and event-driven pipeline design patterns.
Data Pipeline Design
Orchestrating data workflows with Apache Airflow, Prefect, and Dagster.
ML Feature Engineering
Transforming raw data into ML-ready features — pipelines, encoding, and scaling.
Feature Stores
Centralized feature repositories for training/serving consistency in ML systems.
Data Governance for Analytics
Lineage, quality, cataloging, and access control for analytics data.
Batch Processing
MapReduce, Spark batch jobs, and scheduling for large-scale offline computation.
Stream Processing
Apache Kafka, Flink, and Kinesis for real-time event stream processing.
Data Quality
Profiling, validation, monitoring, and remediation for analytics data quality.
BI & Reporting
Data warehouses, OLAP cubes, and reporting layer design for business intelligence.
Lakehouse Governance
Delta Lake, Apache Iceberg, and Hudi for ACID transactions on data lakes.