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Analytics
premium
Azure Data Explorer
A fully managed, high-performance analytics service for near-real-time analysis of large telemetry, log, event, and time-series datasets.
Real-time analytics
intermediate
5 commands
Aliases: ADX, Kusto
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Analytics
premium
Azure Databricks
A unified, open analytics platform on Azure for building, deploying, sharing, and maintaining enterprise data, analytics, and AI solutions at scale.
Data engineering and AI
intermediate
5 commands
Aliases: Databricks on Azure
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Analytics
premium
Kusto data connection
Kusto data connection links Azure Data Explorer to event or storage sources such as Event Hubs, Event Grid, or IoT Hub so data can be ingested into a database table.
Azure Data Explorer ingestion
Intermediate
5 commands
Aliases: ADX data connection, Event Hubs data connection, Event Grid data connection, IoT Hub data connection
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Analytics
premium
Kusto database
Kusto database is a logical container inside an Azure Data Explorer cluster that holds tables, functions, policies, permissions, and ingestion/query configuration.
Azure Data Explorer database
Intermediate
5 commands
Aliases: ADX database, Azure Data Explorer database, Kusto DB
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Analytics
premium
Kusto follower database
Kusto follower database is a read-only database attached from another Azure Data Explorer cluster so consumers can query leader data without copying it.
Azure Data Explorer data sharing
Advanced
5 commands
Aliases: ADX follower database, Azure Data Explorer follower database, followed database
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Analytics
field-manual-complete
Lake database
A lake database in Azure Synapse Analytics combines database design, metadata, and storage layout for data stored in the lake, helping teams describe structure and query files through shared Spark and serverless SQL metadata without turning the data lake into a traditional database.
Analytics platform
intermediate
4 commands
Aliases: No aliases yet
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Analytics
premium
Ingestion mapping
Ingestion mapping controls how streaming or batch data lands in the correct Kusto columns without corrupting schema, losing values, or forcing manual parsing later. Teams see it in azure data explorer tables, kusto databases. It is not a query projection, update policy, table schema alone, Data Factory mapping data flow, or index projection; confusing them can create null columns, failed ingestion. Use the term when reviewing access, monitoring, cost, recovery, or performance. It keeps architects, operators, security reviewers, and support teams focused on the same setting, resource, or behavior.
Azure Data Explorer
Intermediate
5 commands
Aliases: Kusto ingestion mapping, ADX ingestion mapping, table ingestion mapping, JSON ingestion mapping, CSV ingestion mapping
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Monitoring and Observability
premium
KQL
KQL controls how operators search telemetry, detect incidents, investigate performance, build dashboards, create alerts, and summarize operational evidence across Azure services. Teams see it in log analytics workspaces, azure monitor logs. It is not SQL, PromQL, OData filters, ARM template expressions, Azure Data Factory expression language, or application code; confusing them can create missed incidents, expensive queries. Use the term when reviewing access, monitoring, cost, recovery, or performance. It keeps architects, operators, security reviewers, and support teams focused on the same setting, resource, or behavior.
Query language
Fundamentals
5 commands
Aliases: Kusto Query Language, Kusto query, Azure Monitor query language, Sentinel query language
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Monitoring and Observability
premium
KQL function
KQL function controls how teams reuse common filters, joins, calculations, detections, and normalization logic across monitoring, security, and analytics workloads. Teams see it in azure data explorer databases, microsoft sentinel hunting queries. It is not an Azure Function app, SQL stored procedure, Logic Apps workflow, user-defined scalar operator, or ARM template function; confusing them can create stale detection logic, broken dashboards. Use the term when reviewing access, monitoring, cost, recovery, or performance. It keeps architects, operators, security reviewers, and support teams focused on the same setting, resource, or behavior.
Query language
Intermediate
5 commands
Aliases: Kusto function, user-defined KQL function, stored Kusto function, query-defined function
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Web
premium
Kudu console
Kudu console is the App Service Advanced Tools command console used to inspect runtime files, environment state, processes, logs, and deployment artifacts for a web app or function app.
App Service diagnostics
Intermediate
5 commands
Aliases: Kudu debug console, App Service advanced tools console, SCM console, Kudu Advanced Tools
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Analytics
premium
Kusto cache policy
Kusto cache policy controls which Azure Data Explorer data is kept in hot local cache versus colder storage for faster queries and managed cost.
Azure Data Explorer policies
Advanced
5 commands
Aliases: ADX cache policy, hot cache policy, Kusto hot cache, Azure Data Explorer caching policy
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Analytics
premium
Kusto cluster
Kusto cluster is the Azure Data Explorer compute resource that hosts databases, ingestion, query processing, scale settings, networking, and monitoring for Kusto workloads.
Azure Data Explorer infrastructure
Intermediate
5 commands
Aliases: Azure Data Explorer cluster, ADX cluster, Microsoft.Kusto cluster
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Analytics
premium
Kusto continuous export
Kusto continuous export periodically runs a Kusto query and writes results to an external table destination such as Azure Storage for downstream processing or archiving.
Azure Data Explorer export
Advanced
5 commands
Aliases: ADX continuous export, Kusto export policy, continuous data export
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Analytics
premium
Kusto function
Kusto function is a reusable KQL query or query fragment, stored as a database entity or defined ad hoc, that standardizes analytics logic.
Kusto query objects
Intermediate
5 commands
Aliases: Kusto stored function, ADX stored function, KQL function
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Analytics
premium
Kusto ingestion
Kusto ingestion is the process of loading data into Azure Data Explorer tables, where data is validated, mapped, indexed, encoded, and made queryable.
Azure Data Explorer ingestion
Intermediate
5 commands
Aliases: ADX ingestion, Azure Data Explorer ingestion, Kusto data ingestion
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Analytics
premium
Kusto materialized view
Kusto materialized view is a managed aggregation over a source table or another materialized view that keeps a continuously updated result for faster queries.
Kusto query optimization
Advanced
5 commands
Aliases: ADX materialized view, Kusto MV, materialized view in Azure Data Explorer
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Storage
premium
Trusted Microsoft services for storage
Trusted Microsoft services for Azure Storage are specific Azure services that Microsoft documents as eligible for storage firewall exceptions. Depending on the service, access may be based on tenant registration, supported operations, or managed identity authorization combined with Azure RBAC or Data Lake ACLs.
Storage platform
intermediate
5 commands
Aliases: trusted Azure services for Storage, storage trusted services list, Azure Storage trusted services, trusted services storage firewall
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Analytics
premium
ADX dashboard
ADX dashboard is an Azure Data Explorer dashboard made of KQL-backed tiles and visuals for exploring telemetry or time-series data. In everyday Azure work, teams use it to turn Kusto queries into shared operational views for incidents, trends, business metrics, or engineering reviews. The useful evidence is dashboard owner, data source, base query, tile query,
Azure Data Explorer
intermediate
4 commands
Aliases: Azure Data Explorer dashboard, Kusto dashboard, ADX visualization dashboard
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Analytics
field-manual-complete
Kusto private endpoint
A Kusto private endpoint connects clients on an Azure virtual network to an Azure Data Explorer cluster through Azure Private Link, using private IP addressing so query and ingestion traffic can avoid public internet exposure and follow private DNS and approval controls.
Azure Data Explorer
advanced
6 commands
Aliases: No aliases yet
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Analytics
field-manual-complete
Kusto retention policy
A Kusto retention policy controls when data is automatically removed from Azure Data Explorer or Fabric tables and materialized views. It is commonly used for continuously ingested data whose usefulness is age-based, and can be configured at database, table, or view scope.
Azure Data Explorer
intermediate
6 commands
Aliases: No aliases yet
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Analytics
field-manual-complete
Kusto table
A Kusto table is a database object in Azure Data Explorer or Fabric that stores ingested records in a named schema. Tables are created, altered, queried, secured, and governed through Kusto management commands, policies, ingestion mappings, and KQL queries in production.
Azure Data Explorer
intermediate
6 commands
Aliases: No aliases yet
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Analytics
field-manual-complete
Kusto table policy
A Kusto table policy is a management setting on a table that controls behavior such as retention, caching, update processing, ingestion batching, restricted view access, row-level security, or other operational rules supported by Azure Data Explorer and Fabric for governed analytics workloads.
Data engineering and analytics
intermediate
6 commands
Aliases: No aliases yet
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Analytics
field-manual-complete
Kusto update policy
A Kusto update policy is an automation rule that runs when new data is written to a source table, executes a transformation query, and inserts the transformed results into one or more target tables without requiring separate orchestration inside Kusto.
Azure Data Explorer
intermediate
6 commands
Aliases: No aliases yet
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Analytics
verified
OneLake shortcut
OneLake shortcuts are Fabric objects that appear as folders in OneLake while pointing to internal or external storage locations. They let lakehouses, KQL databases, Spark, SQL, and other engines read existing data through a unified namespace without copying the target data.
Microsoft Fabric OneLake
intermediate
5 commands
Aliases: Fabric shortcut, OneLake pointer, shortcut path, target path, OneLake shortcut, OneLake shortcuts, lakehouse shortcut
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Analytics
verified
Queued ingestion
Queued ingestion is an Azure Data Explorer ingestion method that accepts incoming data, stages it, batches it according to ingestion policy, and writes optimized batches into tables. It favors reliable, high-throughput loading over immediate per-record availability for operational analytics workloads.
Azure Data Explorer
intermediate
5 commands
Aliases: ADX queued ingestion, Kusto queued ingestion
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Analytics
field-manual-complete
Streaming ingestion
Streaming ingestion is the path that gets fresh events into Azure Data Explorer quickly, often within seconds, instead of waiting for larger batch ingestion cycles. It is useful for operational telemetry, logs, alerts, and near-real-time analytics where people need to query the newest data quickly. It is not the best answer for every high-volume table; queued ingestion may be better for large sustained loads. The practical decision is latency versus throughput efficiency, table design, mapping quality, and operational cost.
Azure Data Explorer
intermediate
5 commands
Aliases: Azure Data Explorer streaming ingestion, ADX streaming ingestion, Kusto streaming ingestion, low latency ingestion
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Analytics
field-manual-complete
Kusto Query Language
Kusto Query Language, or KQL, is the query language used across Azure Data Explorer, Microsoft Fabric, Azure Monitor, and Microsoft Sentinel to explore structured, semi-structured, and text data, filter records, summarize results, detect patterns, and build operational analytics and dashboards.
Resource Graph
fundamentals
4 commands
Aliases: KQL
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Analytics
field-manual-complete
Kusto query performance
Kusto query performance describes how efficiently Azure Data Explorer, Fabric, Azure Monitor, or Sentinel executes KQL, including how much data a query scans, which operators it uses, how resources are consumed, and whether service limits or workload controls affect results.
Azure Data Explorer
intermediate
4 commands
Aliases: No aliases yet
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Analytics
field-manual-complete
Kusto workload group
A Kusto workload group groups queries and management commands by shared characteristics so Azure Data Explorer or Fabric can apply request limits and rate limits. Workload groups support workload management, concurrency control, prioritization, and protection from resource monopolization across tenants.
Azure Data Explorer
advanced
4 commands
Aliases: No aliases yet
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Analytics
field-manual-complete
Lakehouse architecture
Lakehouse architecture combines data lake scale with warehouse-style querying, governance, and analytics patterns. On Azure, it commonly uses Delta Lake, OneLake or ADLS storage, Spark, SQL experiences, BI tools, and layered data design such as bronze, silver, and gold at enterprise scale.
Azure Databricks
intermediate
4 commands
Aliases: No aliases yet
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Analytics
field-manual-complete
Stream Analytics output
A Stream Analytics output is where processed events go after the query runs. It might be a storage account, SQL table, Event Hubs stream, Cosmos DB container, Azure Data Explorer table, Power BI dataset, Azure Function, or another supported sink. The output name is used in the query, so a small naming or schema mistake can send results nowhere useful. For operators, outputs are the handoff point between real-time processing and the systems people actually read, alert from, or store.
Streaming analytics
fundamentals
4 commands
Aliases: Stream Analytics output, ASA output, Stream Analytics sink, output sink, query output destination
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Analytics
premium field-manual
Materialized view
A materialized view is a stored, continuously maintained result of a query that precomputes data for faster analytics access. Teams use it when teams repeatedly query the same summarized or transformed data and need lower query cost or latency. In plain English, it gives operators a named control for faster analytics, lower repeated query work, and clearer ownership of precomputed results instead of leaving the decision hidden in a portal setting, script, or deployment file. Treat it as production-ready only when the owner, dependencies, permission boundary, monitoring signal, and rollback evidence are clear.
Azure Data Explorer and Kusto
intermediate
4 commands
Aliases: Materialized view, ADX materialized view, Kusto materialized view, precomputed view, read model, materialized query result, precomputed analytical view, Azure Data Explorer and Kusto
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Storage
premium
Azure Storage Explorer
Azure Storage Explorer is a standalone app for Windows, macOS, and Linux that helps users connect to and manage Azure Storage data and resources through a graphical interface.
Storage tooling
fundamentals
4 commands
Aliases: No aliases yet
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Analytics
field-manual-complete
Synapse data explorer pool
A analytics platform concept in Synapse Analytics that helps teams move, transform, query, and govern data at scale with clearer ownership, safety, and operational context.
Synapse Analytics
fundamentals
6 commands
Aliases: Azure Synapse data explorer pool, Data Explorer pool, Kusto pool in Synapse, Synapse Kusto pool, Synapse data explorer pool, legacy Synapse Data Explorer, synapse data explorer pool, synapse-data-explorer-pool
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Databases
premium
Azure Database for MySQL Flexible Server
A fully managed Azure database service for MySQL workloads with configurable compute, storage, backups, networking, maintenance, and high availability options.
Managed MySQL
intermediate
5 commands
Aliases: MySQL Flexible Server
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Databases
premium
Azure Database for PostgreSQL Flexible Server
A fully managed Azure PostgreSQL service with configurable compute, storage, backups, networking, maintenance, extensions, and high availability options.
Managed PostgreSQL
intermediate
5 commands
Aliases: PostgreSQL Flexible Server
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Analytics
premium
Azure Databricks Unity Catalog
A unified governance layer in Azure Databricks for data and AI assets, including catalogs, schemas, tables, volumes, models, privileges, and lineage.
Data governance
intermediate
5 commands
Aliases: Unity Catalog
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Databases
premium
Azure SQL Database
Azure SQL Database is a fully managed relational database service in Azure that provides SQL Server-compatible engines as single databases, elastic pools, and serverless or provisioned compute choices. Microsoft Learn presents it as platform as a service with built-in availability, backups, patching, scaling, and security integration.
Database
intermediate
5 commands
Aliases: Azure SQL DB, Azure SQL Database, Azure SQL pooled database, Azure SQL single database, SQL Database
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Databases
premium
Azure SQL database copy
Azure SQL database copy creates a transactionally consistent copy of an Azure SQL Database using portal, PowerShell, Azure CLI, or Transact-SQL.
Azure SQL Database
fundamentals
5 commands
Aliases: Azure SQL copy, Azure SQL database copy, CREATE DATABASE AS COPY OF, SQL database copy, database copy
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Databases
premium
Azure SQL Database DTU model
Azure SQL Database DTU model is a purchasing model that bundles compute, storage, and I/O resources into Database Transaction Units for Basic, Standard, and Premium tiers.
Azure SQL Database
fundamentals
5 commands
Aliases: Azure SQL Database DTU model, DTU model, DTU-based purchasing model, Database Transaction Unit, eDTU
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AI and Machine Learning
premium
Azure AI Search data source
Azure AI Search data source is the connection definition an Azure AI Search indexer uses to read content from a supported external data store.
AI platform and search
intermediate
4 commands
Aliases: Search data source, SearchIndexerDataSourceConnection, data source connection, indexer data source, search data source connection
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Storage
premium
Azure Data Box
An Azure service that transfers large amounts of data to or from Azure by shipping secure, managed storage devices for offline migration.
Data transfer
intermediate
4 commands
Aliases: Data Box
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Management and Governance
premium
Azure data plane
The Azure data plane is the part of Azure you use after a resource exists, when you interact with the capability that resource provides. Microsoft Learn anchors this term in Azure control plane and data plane, but this field-manual definition is intentionally wider than an older short glossary entry because the page must teach what to inspect, what can break, who owns the decision, and which evidence proves the Azure environment is behaving as intended. In field use, start with the technical boundary: Technically, data-plane operations are service-specific operations against.
Azure Resource Manager
fundamentals
4 commands
Aliases: No aliases yet
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Databases
premium
Azure SQL transparent data encryption
Azure SQL Transparent Data Encryption helps protect Azure SQL Database data, log files, and backups at rest by encrypting and decrypting database pages without requiring application changes.
Azure SQL Database
fundamentals
4 commands
Aliases: No aliases yet
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AI and Machine Learning
premium
Azure AI metrics
Azure AI metrics is the measurable signals used to observe Azure AI applications, model endpoints, agents, evaluations, safety checks, and business outcomes.
Azure AI services
intermediate
4 commands
Aliases: AI metrics, AI service metrics, Azure AI Metrics Advisor, Azure Monitor metrics for AI, Metrics Advisor, time series anomaly detection
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Management and Governance
premium
Azure Resource Graph
Azure Resource Graph is the fast inventory search engine for Azure resources. Microsoft Learn anchors this term in What is Azure Resource Graph?, but this field-manual definition is intentionally wider than an older short glossary entry because the page must teach what to inspect, what can break, who owns the decision, and which evidence proves the Azure environment is behaving as intended. In field use, start with the technical boundary: Technically, Azure Resource Graph extends Azure Resource Management by maintaining queryable resource data across the subscriptions available to the signed-in.
Fleet discovery
fundamentals
4 commands
Aliases: ARG
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Databases
premium
Cosmos DB database
An Azure Cosmos DB database is a unit of management for one or more containers within an account and acts as a namespace for child resources.
Cosmos DB
fundamentals
4 commands
Aliases: No aliases yet
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Analytics
learning-path-anchor
Databricks metastore
The top-level Unity Catalog container for catalogs, schemas, tables, volumes, models, functions, and governance permissions.
Databricks
intermediate
4 commands
Aliases: Unity Catalog metastore, Databricks Unity Catalog metastore, metastore
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Analytics
learning-path-anchor
Databricks MLflow
The managed MLflow experience in Azure Databricks for experiments, runs, metrics, artifacts, model lineage, and MLOps evidence.
Databricks
fundamentals
4 commands
Aliases: MLflow on Databricks, Databricks experiment tracking, MLflow tracking
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Analytics
learning-path-anchor
Databricks model serving
A managed serving endpoint pattern for exposing Databricks models or AI workloads for online inference with operational controls.
Databricks
intermediate
4 commands
Aliases: Databricks Model Serving, serving endpoint, model serving endpoint
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