Glossary
Platforms & Applications
Adaptive Insights – A cloud-based planning and budgeting platform used for financial forecasting, workforce planning, and modeling.
ADP – A global leading payroll and human capital management solution.
CentralReach – A specialized EHR platform used in ABA practices.
KIPU – A specialized EHR platform used in addiction treatment and behavioral healthcare.
Lightning Step – A specialized EHR platform used in addiction treatment and behavioral
healthcare.
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Microsoft Dynamics – An enterprise suite including CRM and ERP for operations, billing, and workflows.
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SAGE Intacct – A cloud-based financial management solution used for accounting, budgeting, and reporting.
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Salesforce – A leading CRM platform used for managing sales, marketing, and customer data.
Databricks – A unified cloud data platform built on Apache Spark that supports engineering, machine learning, and analytics.​
AI & Analytics
AI/BI Dashboards – Visual tools combining artificial intelligence and business intelligence for insight delivery and metric monitoring.
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AI/BI Genie – A Databricks-native tool for generating dashboards, KPIs, and automated reporting using natural language.
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AI Agents – Machine learning-powered systems that support validation, anomaly detection, and user interaction tasks.
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AI-Ready Data – Clean, structured, and governed data prepared for use in AI and machine learning applications.
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Hallucinations (AI) – Incorrect outputs from generative AI models that are presented as facts.
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Metrics Views – redefined, reusable logical views in Databricks that standardize business metrics and KPIs. Metrics views ensure consistent calculations across reports and dashboards, simplify querying, and support semantic layers for BI tools.
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​Architecture & Engineering
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Architecture (Data Architecture) – The structural design of data systems covering ingestion, storage, transformation, and analytics.
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Modern Architecture – A cloud-native, modular approach using layers like raw→silver→gold, with real-time governance and automation.
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Legacy Architecture – Traditional, static systems characterized by manual processes, poor scalability, and siloed data tools.
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Config (Configuration Layer) – Metadata-driven logic that governs pipeline behavior and transformation processes.
​​Data Infrastructure
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Data Lake – A centralized repository for raw, semi-structured, and unstructured data supporting flexible analytics.
Data Warehouse – A structured environment for storing transformed data optimized for querying and reporting.
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Catalog (Unity Catalog) – A Databricks feature for managing data assets, schemas, access, and lineage.
Raw → Silver → Gold – A layered architecture for progressing data from raw ingestion to clean, analytics-ready models:
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Raw: Unprocessed, ingested data
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Silver: Cleaned and joined data
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Gold: Final, curated datasets for reporting
DLT (Delta Live Tables) – A Databricks tool to build reliable ETL pipelines with quality checks and real-time observability.
ETL (Extract, Transform, Load) – A data process involving extraction from sources, transformation, and loading into storage.
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ELT (Extract, Load, Transform) – A modern data integration process where raw data is first extracted from source systems, then loaded into a centralized repository (like a data lake or warehouse), and finally transformed within the destination system. ELT is optimized for cloud environments and supports scalable, in-place transformations.
Pipelines – Automated workflows that manage data flow and transformation between systems or layers.
Compute – The processing power used to perform data operations, typically scaled dynamically in cloud environments.
​Governance & Quality
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Governance – The standards and policies that ensure data quality, security, and traceability across users and systems.
Unity Catalog – Databricks’ native governance framework for managing access, schema consistency, and lineage tracking.
Mapping Tables – Reference tables used to align and standardize data values across multiple systems.
Validation (Data Validation) – Techniques and checks that ensure data accuracy, completeness, and conformance to business rules.