AI/ML Concepts: DE, DS, AI/ML Explained
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Basics to advanced concepts in data engineering, data science, and AI/ML roles
Table 1: Core Data Storage Architectures (Essential Fundamentals)
| Concept | Definition | Purpose | Architecture | Use Cases | Examples |
|---|---|---|---|---|---|
| Data Warehouse | Centralized repository for structured, processed data optimized for analytics | Store historical business data for reporting and analysis | Subject-oriented, integrated, non-volatile, time-variant | Business intelligence, reporting, historical analysis | Snowflake, Amazon Redshift, Google BigQuery |
| Data Lake | Centralized storage for raw data in various formats | Store diverse data types at scale with schema-on-read | Flat architecture storing raw files | Data science, machine learning, exploratory analysis | AWS S3, Azure Data Lake, Hadoop HDFS |
| OLTP (Online Transaction Processing) | System designed for managing transaction-oriented applications | Handle high-volume, low-latency operational transactions | Normalized, row-based, ACID compliant | Order processing, banking transactions, inventory management | PostgreSQL, MySQL, Oracle Database |
| OLAP (Online Analytical Processing) | System designed for complex analytical queries | Support business intelligence and data analysis | Denormalized, column-based, optimized for aggregations | Sales analysis, financial reporting, trend analysis | Amazon Redshift, Google BigQuery, Microsoft Analysis Services |
| Data Lakehouse | Hybrid combining data lake flexibility with warehouse performance | Best of both worlds - structured and unstructured data analytics | Layer of metadata and governance over data lake | Unified analytics, ML on diverse data types | Databricks Delta Lake, Apache Iceberg |
Table 2: Data Architecture Evolution and Comparison
| Architecture | Data Types | Schema | Processing | Scalability | Cost | Best For |
|---|---|---|---|---|---|---|
| Traditional Data Warehouse | Structured | Schema-on-write | SQL queries, batch processing | Vertical scaling | High storage, compute costs | Well-defined business reporting |
| Data Lake | All types (structured, semi-structured, unstructured) | Schema-on-read | Diverse processing frameworks | Horizontal scaling | Low storage, variable compute | Exploratory analytics, ML experiments |
| Modern Cloud Warehouse | Primarily structured, some semi-structured | Schema-on-write with flexibility | SQL-first, some ML capabilities | Auto-scaling | Pay-per-use | Real-time analytics, BI at scale |
| Data Lakehouse | All types | Schema evolution support | Unified batch and streaming | Horizontal scaling | Optimized storage + compute | Advanced analytics, ML production |
Table 3: Expanded Data Engineering Fundamentals
| Concept | Basic Definition | Technical Details | Implementation Considerations | Tools & Technologies | Real-World Example |
|---|---|---|---|---|---|
| Batch Processing | Processing large volumes of data at scheduled intervals | Processes data in chunks, typically scheduled (hourly, daily) | Data latency acceptable, resource optimization important | Apache Spark, Hadoop MapReduce, AWS Batch | Nightly processing of retail sales data for next-day reporting |
| Real-Time/ Stream Processing | Processing data as it arrives, near-instantaneous | Continuous data processing with low latency | Requires robust error handling, state management | Apache Kafka, Apache Flink, AWS Kinesis | Fraud detection on credit card transactions |
| ETL (Extract, Transform, Load) | Transform data before loading into target system | Data validation and cleaning happen before storage | Better data quality, higher processing costs | Informatica, Talend, AWS Glue | Cleaning customer data before loading into warehouse |
| ELT (Extract, Load, Transform) | Load raw data first, transform in target system | Leverages target system's processing power | Faster initial loading, requires powerful target system | dbt, Snowflake, BigQuery | Loading raw logs into data lake, transforming for specific use cases |
| CDC (Change Data Capture) | Identifying and capturing changes in source systems | Tracks inserts, updates, deletes in real-time | Minimizes data transfer, enables incremental processing | Debezium, AWS DMS, Oracle GoldenGate | Syncing customer updates from CRM to data warehouse |
| Data Lineage | Tracking data flow from source to destination | Maintains metadata about data transformations | Critical for compliance, debugging, impact analysis | Apache Atlas, DataHub, Monte Carlo | Tracing customer data from source system to ML model |
Table 4: Advanced Data Concepts and Patterns
| Concept | Definition | Why Important | Implementation Challenges | Success Metrics | Industry Applications |
|---|---|---|---|---|---|
| Data Mesh | Decentralized data architecture with domain ownership | Scales data teams, reduces bottlenecks | Cultural change, technology standardization | Domain autonomy, data discovery efficiency | Large enterprises with multiple business units |
| Data Fabric | Unified data management across hybrid environments | Consistent data access across platforms | Integration complexity, vendor dependencies | Query performance across sources | Multi-cloud enterprises |
| Zero-ETL | Direct analytics on operational data without separate ETL | Reduced complexity, real-time insights | Performance impact on operational systems | Query response time, system availability | Real-time dashboards on transactional data |
| Data Contracts | Agreements between data producers and consumers | Ensures data quality, prevents breaking changes | Governance overhead, change management | Contract compliance, downstream stability | API-like guarantees for data products |
| Semantic Layer | Abstraction providing business context to data | Consistent metrics, self-service analytics | Modeling complexity, performance optimization | User adoption, query consistency | Business intelligence democratization |
Table 5: Data Quality and Governance (Comprehensive)
| Dimension | Basic Checks | Advanced Techniques | Automation Tools | Measurement | Business Impact |
|---|---|---|---|---|---|
| Accuracy | Null checks, format validation | Statistical outlier detection, ML-based anomaly detection | Great Expectations, Monte Carlo | Error rate, confidence scores | Prevents wrong business decisions |
| Completeness | Missing value detection | Completeness profiling across time periods | Apache Griffin, Talend Data Quality | Percentage complete, trend analysis | Ensures comprehensive analysis |
| Consistency | Cross-field validation | Referential integrity, cross-system reconciliation | Custom validations, dbt tests | Consistency score, violation counts | Maintains data reliability |
| Timeliness | SLA monitoring | Real-time freshness tracking | Airflow, Prefect monitoring | Data age, SLA compliance | Enables timely decision making |
| Uniqueness | Duplicate detection | Fuzzy matching, entity resolution | Dedupe libraries, entity resolution tools | Duplicate rate, unique identifiers | Prevents double counting |
| Validity | Business rule validation | Domain-specific constraints | Rule engines, custom validators | Rule compliance rate | Ensures business logic adherence |
Table 6: Cloud Data Architecture Comparison
| Cloud Provider | Data Warehouse | Data Lake | Analytics | Streaming | Integration | Strengths |
|---|---|---|---|---|---|---|
| AWS | Redshift | S3 + Athena | QuickSight, SageMaker | Kinesis | Glue, Step Functions | Mature ecosystem, breadth of services |
| Google Cloud | BigQuery | Cloud Storage + Dataflow | Looker, Vertex AI | Pub/ Sub, Dataflow | Cloud Composer | BigQuery performance, AI/ ML integration |
| Microsoft Azure | Synapse Analytics | Data Lake Storage | Power BI, Azure ML | Event Hubs | Data Factory | Enterprise integration, Office 365 synergy |
| Snowflake | Snowflake | External tables | Partner ecosystem | Streams/ Tasks | Partner connectors | Multi-cloud, performance, ease of use |
Table 7: Complete Career Skills Progression
| Level | Data Engineering | Data Science | AI/ ML | Business Skills | Key Projects |
|---|---|---|---|---|---|
| Junior (0-2 years) | SQL, Python, basic pipelines | Statistics, pandas, visualization | Supervised learning basics | Requirements gathering | ETL pipelines, basic analytics |
| Mid-Level (2-5 years) | Spark, Kafka, cloud platforms | ML algorithms, feature engineering | Deep learning, model deployment | Stakeholder communication | End-to-end ML systems, streaming pipelines |
| Senior (5-8 years) | Architecture design, optimization | Advanced ML, causal inference | LLMs, computer vision | Project leadership | Platform architecture, research projects |
| Principal (8+ years) | Strategy, cross-functional leadership | Research, methodology development | Novel architectures, industry innovation | Organizational influence | Technical strategy, team building |