Bizmetric Data Ingestion & Quality Accelerator

Built on the Databricks Lakehouse Platform, Bizmetric’s framework simplifies, automates, and governs the entire data lifecycle – from ingestion to transformation and validation. It can streamline data processes from ingestion to transformation and validation.

This prebuilt accelerator framework not only speeds up the implementation of enterprise data platforms but also reduces cost, improves data quality, and ensures scalability across cloud environments.

Common Modern Lakehouse Problems

These challenges can derail data modernisation initiatives and result in unreliable analytics outcomes. Modern data platforms face many hurdles. Here are some of them:

• Ingesting large volumes of data from disparate systems while maintaining quality
• Maintaining data consistency, accuracy, and completeness during transformation
• Managing schema changes and data evolution with minimal manual effort
• Building a framework that supports logging, lineage, traceability, and validation
• Delays in data availability caused by manual, time-intensive development

The Solution: Bizmetric Ingestion & Quality Accelerator

Bizmetric’s modular, reusable solution significantly shortens development time, improves data reliability, and accelerates insights. We solve this with an industrialised accelerator that provides:

• Configurable ingestion from multiple enterprise-grade sources
• Prebuilt validation rules to enforce data quality
• Layered architecture (Bronze, Silver, Gold) aligned with the Databricks Lakehouse methodology
• End-to-end observability via integrated logging and metadata tracking
• Support for Unity Catalog for governance, access control, and lineage

Supported Source Systems

The accelerator supports seamless ingestion from a wide variety of source systems commonly used across industries. These are just some sources, we can take data from industry-specific sources as well and ingest it into the bronze layer.

• ERP Systems: Oracle EBS, SAP
• CRM & Cloud Apps: Dynamics 365, Salesforce
• Databases: SQL Server, MySQL, PostgreSQL, Snowflake
• Flat Files: Excel, CSV, Parquet
• APIs and Custom Sources: REST APIs, XML feeds, and more

Data from these sources is ingested into a secure, scalable Azure Data Lake Storage (ADLS Gen2) environment using Azure Data Factory and/or native connectors, based on the data type and volume.

Ingestion Flow: From Source to Silver Layer

Here’s how the ingestion-to-curation flow works using Bizmetric’s accelerator:

Raw Layer (Bronze)

• Replica of source tables
• Stored in native format (Parquet/Delta) in ADLS
• Ideal for traceability and full historical snapshots
• Schema is mapped 1:1 to the source systems
• Supports easy reprocessing and audit tracking

Curated Layer (Silver)

• Business rules and transformations are applied
• Data is enriched and conformed to domain standards
• Maintains data change history using slowly changing dimension (SCD) logic
• Ensures schema consistency, referential integrity, and deduplication

Data is stored in Delta Lake format with versioning and ACID transaction support. You can choose to save data till the Silver Layer if your analytics use cases are intermediate and don’t require dimensional modelling.

Storage Strategy:

• Save raw and curated data for historical auditing and ML model training
• Use partitioning and Z-Ordering for optimal performance
• Optionally skip the Gold Layer if reporting is ad hoc or dataset-specific

Use Cases and Industry Applications
Bizmetric’s accelerated approach is suitable for every industry. Whether a business is operating from:

• Retail:SKU analytics, promotions

• Manufacturing: supply chain, sensors

• BFSI: customer 360, risk analysis

• Energy: field data, asset performance

This accelerator allows rapid ingestion and transformation with confidence in data quality and lineage.

It also enables advanced use cases like training ML models with curated data, feeding Power BI dashboards with reliable datasets, historical audits and time-series analysis from the Bronze layer.