Better data.Less busywork.
Profile, transform, and prepare incoming data for analytics using dynamic SQL, without custom code.
Born from the work.
Built to improve it.
Data Pipelines are Expensive to Maintain and Rigid by Nature.
We found that all analytical systems deal with the same burdens caused by data ingestion. In addition, these systems are never able to consistently communicate the data transformation rules applied to the data in terms that the business user community can understand. This inability to communicate data transformation rules results in an even greater need to rely on technical staff to address these business- driven inquiries.
So we created our SparkLoader solution.
SparkLoader (Extract, Load, Enhance, and Virtually Transform) is a platform-agnostic solution that leverages dynamic SQL to profile, improve, and prepare incoming data for future analytics. SparkLoader addresses common problems upfront while allowing systems to ingest self-described data without custom code. Time-versioned views are applied to implement any remaining complex business rules.
In addition to nearly eliminating source dependencies, SparkLoader also allows users to re-run their analytics against the system as it was in the past.
The details
make the difference.
- A platform-agnostic solution built using ANSI-Standard SQL.
- Reacts to incoming file layouts guided by user input to rapidly deliver improvements.
- Transformed data resides alongside raw data.
- Modular dynamic improvement transformations ensure consistent transformations across your whole system.
- Complex business logic allows for historic what/if analytics.
- Provides daily configurable data profiling and trend analysis across all incoming data sets.
- Addresses issues including: data ingestion, referential integrity, duplicate data, bad data typing, non- contiguous overlapping dates, lists of values, field and table aliases, and more.
Better tools.
New possibilities.
Connect with our SparkLabs team to discuss your workflow, deployment needs, and next steps.