Glossary

Data & AI, defined

Short, plain-English definitions of the data platform, AI and customer data terms we use every day — each linked to the guides that go deeper.

A

Agentic AI: Agentic AI refers to AI systems that pursue a goal over several steps: a language model plans, calls tools or APIs, observes the results and decides what to do next, within guardrails set by people. It moves AI from answering questions to completing tasks.
Apache Spark: Apache Spark is an open-source distributed engine for processing large volumes of data in parallel across a cluster. It supports SQL, Python (PySpark), Scala and Java, and is used for batch ETL, streaming, analytics and machine learning.

Change data capture (CDC): Change data capture (CDC) is a technique for detecting inserts, updates and deletes in a source database and delivering only those changes to downstream systems, usually by reading the database transaction log. It keeps analytics platforms current without repeatedly copying whole tables.
Composable CDP: A composable CDP delivers customer data platform capabilities — identity resolution, Customer 360, segmentation and activation — directly on an organisation's existing data warehouse or lakehouse, instead of copying customer data into a separate packaged CDP.
Customer data platform (CDP): A customer data platform (CDP) collects customer data from every channel, resolves it into unified profiles, builds audiences and segments, and sends them to marketing, advertising and service tools. Its goal is a single, usable view of each customer.

Data governance: Data governance is the set of policies, roles, processes and tools that decide who can use which data, for what purpose, and to what quality standard. It covers access control, data quality, lineage, privacy, retention and compliance with regulations such as GDPR and India's DPDP Act.
Data lakehouse: A data lakehouse is a data architecture that stores data in open file formats on low-cost cloud object storage, and adds warehouse features on top — ACID transactions, schemas, governance and fast SQL — so data engineering, BI and machine learning work on one copy of the data.
Data mesh: Data mesh is an organisational approach to data in which business domains own and publish their data as products, supported by a shared self-service platform and federated governance, instead of routing all data work through one central team.
Decision intelligence: Decision intelligence is a discipline that combines data, analytics, machine learning and business rules to improve specific decisions — not just report on the past. It links each model or dashboard to the decision it informs and measures the outcome.
Delta Lake: Delta Lake is an open-source table format and storage layer that adds ACID transactions, schema enforcement, time travel and efficient upserts (MERGE) to Parquet files on cloud object storage. It is the default table format on Databricks and a foundation of the lakehouse.

Identity resolution: Identity resolution is the process of matching records that belong to the same person or household across devices, channels and systems — using deterministic keys such as email or customer ID, and probabilistic signals — to build one unified customer profile.

LLM fine-tuning: LLM fine-tuning is continued training of a pre-trained large language model on your own examples, so it learns a specific behaviour — an output format, a classification, a tone or a specialised task. Unlike RAG, the change is stored in the model's weights.
LoRA and QLoRA: LoRA (low-rank adaptation) fine-tunes a large model by freezing its original weights and training small low-rank matrices added to selected layers. QLoRA does the same on a 4-bit quantised base model, cutting memory further so large models can be tuned on a single GPU.

Medallion architecture: Medallion architecture is a way of organising lakehouse data into three layers of increasing quality: bronze holds raw data as ingested, silver holds cleaned and conformed data, and gold holds business-ready tables and aggregates for analytics, reporting and machine learning.
MLOps: MLOps (machine learning operations) is the practice of building, deploying, monitoring and retraining machine learning models reliably in production. It applies DevOps ideas — version control, automated testing, CI/CD and monitoring — to data, features and models.

Retrieval-augmented generation (RAG): Retrieval-augmented generation (RAG) is a technique in which a system first retrieves relevant passages from your own documents or data, then gives them to a large language model as context, so answers are grounded in current, private facts and can cite their sources.
Reverse ETL: Reverse ETL copies modelled data from a data warehouse or lakehouse back into operational tools — CRM, marketing automation, ad platforms, support desks — so teams can act on segments, scores and metrics inside the systems they already use.

Slowly changing dimension (SCD Type 2): A slowly changing dimension (SCD) is a dimension table whose attributes change occasionally over time. SCD Type 2 keeps full history by inserting a new row for each change, with effective-from and effective-to dates and a current-row flag, so reports can show data as it was at any point.

Unity Catalog: Unity Catalog is the Databricks governance layer for data and AI assets. It provides one place to manage access control, auditing, lineage and discovery for tables, files, machine learning models and functions across workspaces and clouds.

Vector database: A vector database stores embeddings — numeric representations of text, images or other data — and finds the items most similar to a query by distance between vectors. It powers semantic search and the retrieval step of RAG systems.

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