Why this matters
As a Data Architect, you must ensure every new source is onboarded quickly, reliably, and consistently. A standardized ingestion framework lets teams add sources by configuration instead of custom code, reducing errors, cost, and time-to-data.
- Real tasks you will handle: onboard new SaaS and database sources, define bronze/raw data layout, enforce schema management and data quality, guarantee idempotent re-runs, and provide observability and governance.
- Outcomes: faster onboarding, predictable costs, fewer production incidents, and easier compliance.
Concept explained simply
A standardized ingestion framework is a repeatable, configuration-driven way to pull data from many sources into your platform (often into a raw/bronze zone) with consistent structure, quality checks, lineage, and monitoring.
Mental model
Imagine a conveyor belt in a well-run factory. Each item (dataset) enters through a gate (connector), is scanned (schema + validation), stamped with metadata (lineage + tags), and placed on the right shelf (storage layout) with logs and counters recorded (observability). The belt behaves the same for every item, regardless of where it came from.
Core components of a standardized ingestion framework
1) Configuration-driven source registry
- One place to declare sources, formats, paths, file patterns, load mode (full/incremental/CDC), partitions, watermarks, and schedules.
- Avoids per-source custom code; the same engine reads configs and runs the job.
2) Connectors and protocols
- Support files (CSV/JSON/Parquet), object storage, databases (JDBC), message buses (Kafka), and APIs.
- Standard retry/backoff, timeouts, and pagination/partitioning options.
3) Schema management
- Schema registry or stored contracts (e.g., Avro/JSON schema) with evolution rules.
- Actions on change: allow compatible, quarantine incompatible, or block.
4) Data quality and validation
- Required fields, type checks, null rates, uniqueness, and referential checks.
- Quarantine bad records to a dead-letter queue (DLQ) with reasons.
5) Idempotency, deduplication, and late-arrival handling
- Re-runs do not create duplicates (idempotent writes).
- Keys and watermarks to dedup and handle out-of-order events.
6) Observability
- Metrics: rows in/out, error counts, latency, throughput, cost estimates.
- Structured logs with correlation IDs; alerts on thresholds.
7) Security and governance
- Access controls by zone, PII tagging, encryption at rest/in transit.
- Lineage capture and audit trails.
8) Storage layout and naming
- Zone-based layout (bronze/raw, silver/clean, gold/curated).
- Consistent directories, partitions, and naming conventions to enable pruning and efficient reads.
9) Orchestration integration
- Framework tasks callable from your orchestrator with parameters and sensor-based triggers.
- Clear SLAs, retries, and backoff policies.
Worked examples
Example 1: Batch file ingestion to Bronze with schema enforcement
- Config declares a source folder, file pattern, schema version, partitioning, and DLQ path.
- Ingestion engine lists new files, validates schema, writes valid rows to bronze, invalid rows to DLQ, and records metrics.
- Idempotency via a checkpoint + manifest of processed files (hash + size + mod time).
{
"source_id": "ecom_orders",
"type": "file",
"path": "s3://raw/ecom/orders/",
"pattern": "orders_*.json",
"format": "json",
"schema_version": "v3",
"partition_by": ["ingest_date"],
"watermark_column": "order_ts",
"load_mode": "append",
"checkpoint": "s3://meta/checkpoints/ecom_orders/",
"dlq_path": "s3://dlq/ecom/orders/",
"pii_fields": ["email"]
}Example 2: Streaming ingestion from Kafka with DLQ and schema evolution
- Consume from topic with schema registry; allow additive fields, block breaking changes.
- Per-batch quality checks (required keys, type checks); invalid events to DLQ topic.
- Late events accepted for N hours using event_time watermark; dedup by (order_id, event_type, event_time).
Rules: - schema.compatibility: backward - watermark.lateness: 6h - dedup.keys: [order_id, event_type, event_time] - dlq.topic: orders_events_dlq
Example 3: Database CDC to object storage with idempotent merges
- Connector captures inserts/updates/deletes with commit LSN.
- Write change events to bronze partitioned by date and LSN range.
- Ensure idempotency by last-committed LSN checkpoint; replays overwrite same LSN range only.
CDC config: - source: postgres - table: public.customers - key: customer_id - cdc_lsn_checkpoint: s3://meta/lsn/customers/ - partitions: [ingest_date] - write.mode: upsert_by_lsn
Example 4: Cost-aware layout
- Small files are compacted post-ingestion if below target size threshold.
- Cold partitions moved to cheaper storage after retention period.
Compaction: - target_file_size_mb: 256 - compact_after_files: 100 Retention: - bronze_retention_days: 90 (then archive)
Design checklists
Reliability checklist
- Idempotent writes with clear primary/dedup keys
- Checkpointing strategy defined (files/offsets/LSN)
- Retry policy with exponential backoff
- DLQ path and structure
- Alerting thresholds
Security & governance checklist
- PII fields tagged and masked in bronze where required
- Encryption in transit and at rest
- Access controls by zone and role
- Lineage captured for each run
- Audit logs enabled
Performance checklist
- Partitioning aligned with query patterns and freshness needs
- File size targets to avoid small files
- Predicate pushdown enabled by format choice
- Batch sizes tuned for connectors
Exercises
Work offline or in your notes; then compare with the solutions in the exercise cards below. Use this quick self-check:
- Is your design configuration-driven?
- Did you include idempotency and dedup logic?
- Are schema evolution and DLQ rules explicit?
- Do you have observability and governance elements?
Exercise 1 (mirrors ex1): Design a source registry schema
Define the columns and rules for a metadata table (or YAML) that can onboard 3 sources: a daily CSV, a JSON API, and a JDBC table. Include identifiers, paths/URIs, format, file pattern, partitioning, load mode, watermarks, primary keys, checkpoint, DLQ, PII tags, schedule, and an active flag.
Exercise 2 (mirrors ex2): Idempotency and dedup spec
For an append-only events feed that may resend past 48 hours of data, write a clear rule set for idempotent reprocessing and deduplication, including keys, watermarks, and write behavior.
Common mistakes and self-check
- No single source registry. Self-check: Can you onboard a new source without writing code?
- Missing idempotency. Self-check: Can you re-run yesterday safely?
- Weak schema handling. Self-check: What happens if a new column arrives today?
- No DLQ or poor error context. Self-check: Can analysts see why rows failed?
- Small-file explosion. Self-check: Do you compact or size files intentionally?
- Unclear partitioning. Self-check: Are partitions aligned to how data is queried and refreshed?
- Missing PII tagging. Self-check: Can you list fields with PII across sources?
Practical projects
- Build a minimal ingestion framework: Read config for 3 sources (file, API, JDBC), enforce schema, write bronze, log metrics, and maintain processed-manifest.
- Add DLQ and data quality: Route invalid rows with structured error messages; add a summary report.
- Implement CDC + compaction: Simulate CDC events with ordering keys, checkpoint LSN/offset, and compact small files nightly.
Learning path
- Foundations: File formats (CSV/JSON/Parquet), partitioning, immutability vs merge, orchestrators, and retries.
- Governed ingestion: Schema contracts, DLQ, idempotency, and lineage.
- Scalability: Batch vs streaming, compaction, cost-aware layouts, and observability.
- Security: PII tagging/masking, encryption, access policies.
- Productionization: SLAs, alerting, capacity planning, documentation templates.
Who this is for
- Data Architects defining ingestion standards
- Data Engineers implementing pipelines
- Platform Engineers building shared data services
Prerequisites
- Basic SQL and at least one data processing language
- Familiarity with file/object storage and a messaging system
- Understanding of schemas, primary keys, and partitioning
Mini challenge
In 20 minutes, write a one-page ingestion spec for a new source: a daily product catalog CSV. Include schema (with types), partitioning, idempotency, dedup keys, DLQ rules, PII tags, and metrics. Keep it configuration-driven.
Checklist to validate your spec
- Clear source_id and ownership
- Format, pattern, and location defined
- Schema version and evolution policy
- Watermark and dedup keys
- Checkpoint and DLQ paths
- Partitioning and file size targets
- Alert thresholds and retention
Next steps
- Turn one worked example into a runnable prototype with configs.
- Add observability: counts, error rates, and latency.
- Extend to streaming and CDC once batch is stable.
Quick test
The quick test is available to everyone. If you are logged in, your progress will be saved automatically.