Azure DP-900 key terms

Data that fits neatly into a fixed schema of rows and columns, where every record has the same fields and each field holds a defined type of value, such as a customer table.

Data that carries tags or markers describing its own shape, such as JSON or XML documents, so individual records can differ in fields while still being machine-readable without a strict table schema.

Data with no predefined model or schema, such as images, audio, video, and free-form text, usually kept as files and processed by tools rather than queried column by column.

A collection of data organized into tables that relate to one another through shared key values, queried with SQL and managed by a database engine that enforces the schema.

A named two-dimensional structure in a relational database built from rows (records) and columns (fields), where each column declares an attribute name and a data type.

A column or combination of columns whose values uniquely identify each row in a table; duplicates and empty values are rejected, which keeps every record individually addressable.

A column in one table that stores a primary key value from another table, creating a link between the two tables and preventing rows that point at records which do not exist.

A supporting structure that keeps sorted references to table rows so the engine can locate matching records quickly instead of scanning the entire table, at the cost of storage and slower writes.

The practice of splitting related data into separate linked tables so each fact is stored once, which reduces duplicated values and the errors that appear when copies are updated inconsistently.

A workload made of many small, short read and write operations that insert, update, or delete individual records, typically supporting day-to-day applications such as order entry or banking.

A workload that reads large volumes of historical data and aggregates it to answer business questions, using fewer but much heavier queries than an application's day-to-day processing.

Handling data in large groups that are collected over a period and processed together by a scheduled job, rather than reacting to each event the moment it arrives.

Handling data continuously as individual events arrive, computing results inside a short time window so dashboards or alerts reflect what is happening right now.

A fully managed relational database service that runs the SQL Server engine in Azure, with backup, patching, and high availability handled for you and no operating system to administer.

A managed service that runs nearly the complete SQL Server engine inside a private Azure virtual network, giving close compatibility with on-premises features such as cross-database queries and SQL Agent.

An infrastructure-as-a-service option where you install and run your own SQL Server instance on an Azure virtual machine, keeping full control of the operating system, disks, and configuration.

A managed relational service for the open-source PostgreSQL engine, delivered in server tiers with automated backups, scaling, and patching so you focus on schema and queries rather than servers.

A shared set of compute and storage resources that several Azure SQL databases draw from, letting busy databases borrow capacity from idle ones and lowering total cost.

A billing option for Azure SQL Database that blends compute, memory, and read/write rates into a single unit, with the size chosen from fixed performance tiers.

A billing option where you choose virtual cores, memory, and storage independently, and may apply the Azure Hybrid Benefit to reuse existing SQL Server licenses.

A globally distributed, multi-model database service that stores items in containers, replicates data across regions, and exposes several APIs including SQL, MongoDB, Cassandra, Gremlin, and Table.

A data store that maps a unique key to an opaque value, giving very fast lookups when the key is known but little ability to search inside the stored value.

A store that keeps each record as a self-describing document such as JSON, where documents may have different fields and are read as whole units instead of rows spread across tables.

A store built from nodes representing entities and edges representing relationships, optimized for traversing connections such as social networks, recommendation paths, or fraud rings.

A store that groups columns into families so wide, sparse records can be read by column group and spread across many nodes, the model used by the Cassandra API in Cosmos DB.

A field whose value Cosmos DB uses to group related items into logical partitions, which are spread across physical partitions; choosing a high-cardinality value avoids hot spots.

The normalized cost of a Cosmos DB operation, combining CPU, memory, and I/O work; throughput is provisioned or auto-scaled as a number of request units per second.

A Cosmos DB setting that trades read freshness against latency and availability, ranging from strong through bounded staleness, session, and consistent prefix to eventual consistency.

Azure's object storage service for very large amounts of unstructured data such as files, images, and backups, held in containers inside a storage account and offered in hot, cool, cold, and archive tiers.

A storage account configuration that adds a hierarchical namespace and fine-grained folder permissions to blob storage, making it a practical landing zone for large-scale analytics.

A schemaless store in Azure Storage that holds large numbers of simple entities, each addressed by a partition key and a row key, with no fixed set of columns required.

An analytics platform that brings ingestion pipelines, a SQL data warehouse, Spark pools, and a shared workspace together, so teams can build end-to-end analytical solutions inside one service.

A Synapse component that provides provisioned, massively parallel processing storage and compute for relational warehouse tables, sized and billed separately from the surrounding workspace.

A central store of cleaned, modeled historical data gathered from many source systems and shaped for fast aggregate queries that feed reporting and business intelligence.

A storage repository that holds raw files of any format at very large scale, preserving the original data until it is transformed or queried for analysis.

A cloud data integration service where you define pipelines of activities that copy and transform data between stores, run on a schedule, or triggered by an event.

A managed Apache Spark platform in Azure with collaborative notebooks, used to process very large data sets using code in Python, SQL, Scala, or R.

A real-time analytics service that runs SQL-like queries over flowing data from sources such as IoT Hub or Event Hubs and writes the results to outputs for alerting or reporting.

Microsoft's business intelligence service for building interactive reports and dashboards on top of a data model, authored on the desktop and published for sharing in the cloud.