Description
- Big Data Architect Features Basics to Advanced
- Data ingestion: connect and ingest from databases, logs, IoT, APIs, and third‑party streams.
- Storage layers: design raw landing zones, data lakes, and cloud data warehouses for scale and cost control.
- Batch processing: implement scalable ETL/ELT pipelines for large‑volume transformations and aggregations.
- Streaming and real‑time: support event ingestion, stream processing, and change data capture for low‑latency use cases.
- Compute frameworks: choose distributed engines like Spark, Flink, or cloud serverless compute for processing workloads.
- Data modelling: apply canonical, dimensional, and hybrid schemas to optimize analytics and BI performance.
- Metadata and lineage: maintain catalogs, data dictionaries, and end‑to‑end lineage for governance and impact analysis.
- Partitioning and file formats: use columnar formats, partitioning, and compaction to reduce I/O and speed queries.
- Orchestration: schedule, monitor, and retry complex workflows with DAG‑based orchestrators and SLA enforcement.
- Data quality: embed validation, anomaly detection, and automated tests to ensure trusted datasets.
- Security and governance: enforce RBAC, encryption, masking, and compliance controls across the stack.
- Performance tuning: optimize queries with materialized views, caching, and cost‑aware resource allocation.
- Observability: instrument pipelines for latency, throughput, error rates, and data drift monitoring.
- Feature stores and ML integration: provide consistent feature pipelines and online stores for model serving.
- Scalability and cost management: design for elastic scaling, autoscaling policies, and cost visibility.
- Advanced patterns: adopt lakehouse architectures, data virtualization, and federated query for hybrid ecosystems.
