Batch Data Processing
A production-ready accelerator for batch ETL/ELT — from low/no-code change data capture through medallion-layered transformation to quality-gated, idempotent delivery.
Architecture
Low/No-Code CDC
Capturing changes from source systems shouldn't require hand-rolled connectors for every database. The accelerator provides pre-built, configuration-driven CDC connectors that stream inserts, updates, and deletes into your lake or warehouse with minimal custom code.
Pre-built Connectors
Configuration-driven CDC connectors for common relational and NoSQL sources — no custom integration code required.
Log-based Capture
Reads directly from database transaction logs (WAL/binlog) to capture changes with minimal impact on source systems.
Schema Drift Handling
Automatically detects and propagates upstream schema changes without breaking downstream pipelines.
Initial Snapshot + Ongoing Sync
Combines a full initial load with continuous incremental capture, so targets stay in sync from day one.
Medallion Architecture
Every pipeline lands data through a consistent Bronze → Silver → Gold structure, so raw, cleaned, and business-ready data are always clearly separated and independently reprocessable.
Bronze
Raw, immutable ingestion layer that preserves data exactly as received from source, for full traceability and replay.
Silver
Cleaned, deduplicated, and conformed data — validated against schema and enriched with standard business keys.
Gold
Aggregated, business-ready datasets modeled for direct consumption by analytics, reporting, and downstream applications.
Data Quality Checks & Documentation
Trust in data starts with visibility. The accelerator wires automated quality checks directly into the pipeline, and generates living documentation so every dataset's lineage, freshness, and quality are always discoverable.
Automated Validation Rules
Schema, null, uniqueness, and referential integrity checks enforced at every layer, with pipeline runs failing fast on violations.
Anomaly Detection
Statistical checks flag unexpected volume, distribution, or freshness shifts before they reach downstream consumers.
Auto-generated Documentation
Table and column-level documentation, generated and kept in sync with the pipeline — no manually maintained wikis.
Data Lineage
End-to-end lineage from source to Gold layer, so impact analysis and root-cause investigation take minutes, not days.
ELT Idempotent Pipelines
Pipelines fail, get re-run, and get backfilled — idempotency is what keeps that safe. Every pipeline built with the accelerator can be re-run against the same input any number of times without producing duplicate or inconsistent results.
Merge/Upsert Semantics
Loads use merge-based writes keyed on natural or surrogate keys, so re-runs overwrite rather than duplicate.
Idempotent Backfills
Historical reprocessing is safe by design — backfilling a date range produces the same result no matter how many times it's run.
Exactly-once Bookkeeping
Watermarking and run-tracking ensure each source batch is processed once, even after failures or retries.
Transformation in the Warehouse
ELT pattern pushes transformation into the warehouse/lake engine, keeping pipelines lightweight, testable, and version-controlled.