AI Data Architecture
Data Pipeline Orchestration
Apache AirflowWorkflow orchestration for batch ML data pipelines
Key Features
- DAG-based workflows with Python API
- Extensive operators library for integrations
- Backfilling and historical data reprocessing
- Web UI with monitoring and alerting
- Dynamic pipeline generation
- Distributed task execution
Use Cases
- ETL for ML training data
- Scheduled data processing pipelines
- Batch feature generation workflows
- Multi-step ML pipeline orchestration
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Apache Kafka + Kafka StreamsReal-time streaming data platform
Key Features
- Event streaming with high throughput
- Exactly-once semantics for processing
- Stream processing with Kafka Streams
- Topic partitioning for parallelism
- Durable message storage
- Connect API for integrations
Use Cases
- Real-time feature pipelines
- Event-driven ML systems
- Streaming ETL for online learning
- Model inference result streams
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PrefectModern workflow orchestration with dynamic DAGs
Key Features
- Python-native dynamic workflows
- Hybrid execution (local/cloud)
- Parameterized flows and deployments
- Advanced observability and alerting
- Retries and error handling
- Cloud-native design
Use Cases
- Complex ML pipelines with dependencies
- Dynamic data workflows
- Research experiment pipelines
- Cloud-native orchestration
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DagsterData orchestrator with asset-based pipelines
Key Features
- Software-defined assets paradigm
- Data lineage tracking built-in
- Type system for data validation
- Testing framework for pipelines
- Declarative scheduling
- Integrated with dbt, Spark, Pandas
Use Cases
- Data platform engineering
- Asset-oriented ML workflows
- Data quality enforcement
- Multi-team pipeline collaboration
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Data Storage Layers
Delta LakeLakehouse storage layer with ACID transactions
Key Features
- ACID transactions on data lakes
- Time travel for data versioning
- Schema evolution and enforcement
- Upserts and deletes on Parquet
- Audit history and rollback
- Unified batch and streaming
Use Cases
- ML data lakes with versioning
- Feature data storage
- Training dataset management
- Batch + streaming pipelines
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Apache IcebergHigh-performance table format for analytics
Key Features
- Hidden partitioning (no user management)
- Schema evolution without rewrites
- Time travel and snapshots
- ACID guarantees for data lakes
- Multi-engine support (Spark, Flink, Trino)
- Partition evolution over time
Use Cases
- Petabyte-scale ML datasets
- Multi-engine data access
- Feature stores on data lakes
- Data science workloads
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LakeFSGit-like version control for data lakes
Key Features
- Branch, commit, merge for data
- CI/CD pipelines for data
- Zero-copy branching (metadata only)
- Data lineage and governance
- Hooks for validation
- S3-compatible interface
Use Cases
- Data versioning for reproducibility
- ML experiment isolation
- Data testing and validation
- Safe experimentation on production data
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MinIOHigh-performance S3-compatible object storage
Key Features
- S3 API compatible
- On-premises deployment
- Object versioning
- Lifecycle management
- Multi-tenancy support
- Erasure coding for durability
Use Cases
- Private cloud ML storage
- On-premises data lake
- Model artifact storage
- Edge and air-gapped deployments
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ML Data Processing Patterns
| Pattern | Description | Latency | Complexity | Best For |
|---|---|---|---|---|
| Batch Processing | Periodic large-scale data processing | Hours-Days | Low | Historical features, model training data, offline analytics |
| Stream Processing | Real-time continuous data processing | Seconds-Minutes | High | Real-time features, online learning, fraud detection |
| Micro-batch | Small batch processing at intervals | Minutes | Medium | Near-real-time features, cost optimization |
| Lambda Architecture | Batch + stream layers combined | Varies | Very High | Comprehensive analytics, serving historical + real-time |
| Kappa Architecture | Stream-only processing (no batch layer) | Seconds | High | Event-driven ML, unified pipeline, simplification |
| Change Data Capture (CDC) | Capture database changes in real-time | Real-time | Medium | Database sync, event sourcing, incremental updates |
Data Quality & Validation
verified
Data Validation
Frameworks:
- Great Expectations: Declarative validation with extensive checks
- Pandera: Schema validation for pandas
- Deequ: Amazon's data quality library on Spark
- TFDV: TensorFlow Data Validation
- Soda: SQL-based quality testing
schema
Schema Management
Tools:
- Schema Registry: Confluent's centralized schema management
- Protobuf: Binary serialization with schema evolution
- Apache Avro: Row-oriented data serialization
- Parquet Schema: Columnar format with embedded schema
- Schema inference: Automated schema detection and drift
account_tree
Data Lineage
Platforms:
- OpenLineage: Open standard for lineage
- Apache Atlas: Data governance and metadata
- Marquez: Metadata service for lineage
- DataHub: Metadata platform with lineage tracking
- Monte Carlo: Data observability platform
troubleshoot
Data Drift Detection
Solutions:
- Evidently AI: ML monitoring and drift detection
- WhyLabs: AI observability platform
- Alibi Detect: Outlier and drift detection
- NannyML: Post-deployment monitoring
- Statistical tests: KS, PSI, Jensen-Shannon divergence
ML Data Architecture Patterns
| Architecture | Components | Strengths | Challenges | Use Case |
|---|---|---|---|---|
| Data Warehouse for ML | Snowflake/BigQuery/Redshift + dbt + Feature Store | Structured data, SQL-friendly, proven patterns, strong governance | Not for real-time, costly at scale, limited flexibility | Enterprise ML with structured data, BI-driven analytics |
| Data Lakehouse | Delta Lake/Iceberg + Spark + Unity Catalog | Unified storage, ACID guarantees, flexibility, cost-effective | Complexity, governance overhead, learning curve | Modern ML platforms, data science teams, mixed workloads |
| Streaming-First | Kafka + Flink/Spark Streaming + Feature Store | Real-time capabilities, event-driven, low latency | High complexity, operational burden, cost | Real-time ML, fraud detection, recommendations, trading |
| Cloud-Native | S3 + Glue/Dataflow + BigQuery/Athena | Serverless, scalable, managed services, fast to market | Vendor lock-in, cost at scale, limited customization | Cloud-first organizations, startups, rapid prototyping |
| Hybrid (On-Prem + Cloud) | MinIO + Spark + Cloud connectors | Data sovereignty, flexibility, gradual migration | Complex networking, dual management, synchronization | Regulated industries, gradual cloud migration, compliance |
Data Pipeline Best Practices
Pipeline Design Principles
- Idempotency: Same input produces same output (safe retries)
- Incremental processing: Process only new/changed data
- Backfill capability: Reprocess historical data easily
- Checkpointing: Resume from failures without restarting
- Partition pruning: Only read necessary data partitions
- Late data handling: Manage out-of-order events gracefully
Performance Optimization
- Columnar storage: Use Parquet, ORC for analytics
- Partition strategies: Date, region, model_id partitioning
- Predicate pushdown: Filter data at source
- Broadcast joins: For small dimension tables
- Cache frequently used: Hot datasets in memory
- Compression: Snappy for speed, Gzip for storage
Reliability & Monitoring
- Data validation: At pipeline boundaries (Great Expectations)
- Alerting: On pipeline failures and SLA breaches
- SLA monitoring: Track data freshness and completeness
- Dead letter queues: Failed records for debugging
- Pipeline testing: Unit, integration, end-to-end tests
- Observability: Logs, metrics, traces for pipelines