Version – SSIS 2013
Client based in – USA
Enterprise
Load prestaging data from .CSV, .TXT or any other flat file type
Our customer basically receives the zip file(s) for the health care data from various medications units and organisations (who are their clients) to be loaded to their Prestaging databases. Each zip file has one or more CSV, TXT, or any other flat file type of source with very bulky data to be loaded to Pre staging database.
The Challenge
What Was Holding the Business Back?
Processing large healthcare datasets arriving in different flat file formats required a scalable, automated solution capable of handling changing schemas and high data volumes.
Multiple File Formats
Incoming ZIP files contained CSV, TXT, and other flat file formats with varying structures and metadata.
Dynamic Schema Handling
Source files contained inconsistent metadata, requiring automatic table creation and flexible column mapping.
High Data Volumes
Large incremental datasets needed to be processed efficiently without impacting daily ETL operations.
Operational Monitoring
The solution had to integrate with the client's ETL framework and capture detailed execution statistics for every load.
Objective
What We Set Out to Achieve
Develop a dynamic SSIS solution capable of processing multiple flat file formats, automatically creating pre-staging tables, performing incremental upserts, and delivering high-performance data loading with complete ETL logging.
Our Approach & Solution
How We Delivered Results
Built a metadata-driven SSIS framework that automates schema creation, incremental loading, archiving, and monitoring for large healthcare datasets.
Schema Generation
Developed a one-time pre-staging schema builder that reads file metadata and dynamically creates SQL Server tables.
Dynamic File Processing
Extracted ZIP files and processed configurable CSV, TXT, and other flat file formats using SSIS Foreach Loop containers.
Incremental Loading
Performed high-performance upsert operations using primary keys to load only new or modified records.
ETL Automation
Archived processed files, captured execution metrics, and enabled parallel processing for faster daily data loads.
Metadata-Driven Design
Automatically interpreted source metadata and applied default data types when complete metadata was unavailable.
Flexible File Support
Supported configurable flat file extensions, enabling easy onboarding of new source formats.
Integrated Logging
Captured batch details, row counts, inserts, updates, deletions, and execution status within the client's ETL framework.
Parallel Processing
Implemented parallel execution to significantly reduce loading time for high-volume healthcare datasets.
Results & Impact
The Outcome
The automated SSIS framework streamlined healthcare data ingestion, improved processing speed, and reduced manual intervention while supporting large-scale incremental loads.
Auto
Schema Creation
Fast
Flat File Processing
Parallel
Parallel ETL
100%
ETL Monitoring
Conclusion
The Bigger Picture
The metadata-driven SSIS solution transformed healthcare data ingestion by automating schema creation, supporting multiple flat file formats, and enabling high-performance incremental loading. With integrated ETL logging, configurable processing, and parallel execution, the client achieved a scalable and reliable pre-staging framework capable of handling large daily healthcare data volumes with minimal manual effort.
Additional Details
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