Description
- BI Data Engineering Features Basics to Advanced
- Data sources: connect to transactional databases, APIs, files, cloud storage, and third‑party services.
- Ingestion: batch and incremental extract processes to reliably bring raw data into the platform.
- ETL / ELT: transform data for analytics using scheduled pipelines or push-down transformations in the warehouse.
- Data staging: landing zones and raw schemas to preserve source fidelity and enable reproducible pipelines.
- Data modelling: canonical, dimensional, and star/snowflake schemas to support reporting and fast queries.
- Data warehousing: centralized columnar stores or cloud warehouses for high-performance analytical queries.
- Data lakes and lakehouses: scalable object storage with schema-on-read and transactional metadata layers.
- Schema design and partitioning: partitioning, clustering, and columnar formats to optimize scan and I/O.
- Data quality: validation rules, anomaly detection, and automated tests to ensure trusted metrics.
- Metadata and lineage: cataloguing, data dictionaries, and end-to-end lineage for discoverability and audits.
- Orchestration and scheduling: workflow engines to manage dependencies, retries, and SLA enforcement.
- Streaming and real-time: event ingestion, stream processing, and change data capture for low-latency analytics.
- Performance tuning: query optimization, materialized views, caching, and cost-aware resource allocation.
- Semantic layer and BI models: reusable metrics, business logic, and access-controlled datasets for analysts.
- Security and governance: role-based access, encryption, masking, and compliance controls.
- Observability and monitoring: pipeline health, data drift detection, and alerting for operational reliability.
- Automation and MLOps integration: automated feature pipelines, model feature stores, and deployment hooks.
- Advanced capabilities: data virtualization, federated queries, self-service analytics, and automated lineage-driven impact analysis.
