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slug: reference-what-is-nollejbasevisit nollejBase.com to learn more about this tool.
A product of HBG Consulting, LLC, nollejBase is a powerful metadata layer that enriches datasets. Our proprietary structure enhances data types, defines cube structures, machine learning model parameters, and data views.
nollejBase maintains this metadata for datasets it's aware of, even if the datasets are not stored locally in a trqdition sql table.
JSON fields in a data table row could be a dataset. API endpoints are datasets.
Without having to invest the processing time, nor burn up access tokens, nollejBase remains aware of what to expect from a dataset when it is eventually accessed.
The moment a dataset is brought in, regardless of where it comes from, nollejBase treats that dataset as a first-class citizen in the system.
Important note: nollejBase enhances business analytics and presentations of data, not intended to alter systems of record. This distinction allows us to fine-tune performance for complex calculations and ro use presentations. In essence,, nollejBase makes ETL processes efficient, without the burden of warehousing sensitive client data.
16Nottom line, nollejBase is a virtual data warehouse.
The "data A to Z processing language" DAZL is a forth generation language (4GL), and specific to the business analytics and presentstion domain. You'll often see this described as a DSL, domain specific language.
Forth generation languages require minimal code to perform complex tasks.
Each step operates on complete datasets, so typical programming that requires the coder to write loops that iterates over datasets is abstracted away. This leaves the developer free to think about higher level process flows, without worrying a out the mechanics required to perform each step.
Bottom line: if nollejBase is the virtual data warehouse, DAZL is the powerful ETL processor and dashboard builder combined.
DAZL steps are really pipeline workflow tasks. A dataset, or a collection of datasets, are moved through a series of transformations (steps) to manage the dataset, perform complex statistics and analysis, focus on answering business performance questions, and provide polished presentations.
The nollejBase metadata that defines characteristics of a dataset, moves with the data through the transformations and presentation.
This is important because, as data moves through a DAZL workflow, the descriptive metadata about each dataset becomes more robust. The system learns more about the dataset as it progresses through various transformations.
The final presentations of tables. charts, and dashboards are the culmination of chaining together multiple discipline analyses and eventually telling the story and insights normally hidden in datasets.