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Analytics field-manual-complete Stream Analytics Azure Stream Analytics is a managed real-time analytics service for processing fast-moving event streams. It connects to inputs such as Event Hubs, IoT Hub, Blob Storage, or Data Lake Storage, runs SQL-like queries over the stream, and writes transformed results to supported outputs. Stream processing fundamentals 5 commands Aliases: Azure Stream Analytics, ASA, stream processing job, real-time analytics job, Stream Analytics Quick peek Open full term page

Analytics field-manual-complete Stream Analytics job A Stream Analytics job is the thing that actually runs your real-time processing. You give it input sources, a SQL-like query, optional functions, output destinations, and capacity settings. When the job starts, it continuously reads events, applies the query logic, and pushes results somewhere useful. For a learner, think of the job as the streaming application container. For an operator, it is the Azure resource you start, stop, scale, monitor, troubleshoot, secure, and include in deployment automation. Streaming analytics fundamentals 5 commands Aliases: Stream Analytics job, Azure Stream Analytics job, ASA job, streaming job, real-time analytics job Quick peek Open full term page

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 Quick peek Open full term page

Analytics field-manual-complete Synapse Link 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 advanced 7 commands Aliases: Azure Synapse Link, Synapse Link, Synapse Link for SQL, link connection, near real-time analytics, synapse Link, synapse link, synapse-link Quick peek Open full term page

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 Quick peek Open full term page

Databases field-manual-complete Synapse Link for Cosmos DB Synapse Link for Cosmos DB is the legacy Azure integration that exposes Cosmos DB analytical store to Synapse for near-real-time analytics without custom ETL. Microsoft now directs new projects toward Cosmos DB Mirroring in Microsoft Fabric, so existing deployments need careful governance and migration planning. Azure Cosmos DB advanced 5 commands Aliases: Azure Synapse Link for Azure Cosmos DB, Cosmos DB Synapse Link, Cosmos DB analytical store link, Synapse Link analytical store, HTAP for Cosmos DB Quick peek Open full term page

Analytics field-manual-complete Stream Analytics no-code editor The Stream Analytics no-code editor is a visual way to build a streaming job without typing the query yourself. You connect supported inputs, shape the data with drag-and-drop transformations, preview records, and choose outputs. Azure then creates a Stream Analytics job behind the experience. It is useful for analysts, platform teams, and engineers who need a quick, governed pipeline but do not want every small stream-processing task to become custom code. It still needs engineering review before production. Stream Analytics intermediate 5 commands Aliases: Stream Analytics no-code editor, ASA no-code editor, no-code Stream Analytics, drag-and-drop Stream Analytics editor, Stream Analytics visual editor Quick peek Open full term page

Analytics field-manual-complete Stream Analytics query A Stream Analytics query is the logic that turns incoming events into useful results. It looks like SQL, but it is built for streams, time windows, late events, joins, reference data, and continuous output. The query decides what fields to keep, what events to filter, how to group data, and where each result goes. For operators, a query is not just code; it is production behavior that can change alerts, dashboards, and records within seconds. Streaming analytics fundamentals 5 commands Aliases: Stream Analytics query, ASA query, Stream Analytics SQL query, streaming query, ASAQL query Quick peek Open full term page

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 Quick peek Open full term page

Analytics premium Delta time travel Delta time travel is the ability to query or restore earlier versions of a Delta table by using the table history stored in the Delta transaction log. Delta Lake intermediate 5 commands Aliases: Delta Lake time travel, table version query, query previous Delta version, restore Delta table version Quick peek Open full term page

Databases premium Cosmos DB point-in-time restore Cosmos DB point-in-time restore means the ability to restore supported Cosmos DB resources to a selected timestamp when continuous backup is enabled in Azure Cosmos DB. In plain English, it is the thing developers and operators check when they need to understand how data access really works. It connects the application model to recovering from bad writes, accidental deletes, corruption events, or deployment mistakes without guessing which backup to use. For a production team, it turns vague database talk into a specific thing to inspect in the portal, SDK code, templates, metrics, and incident notes. Azure Cosmos DB intermediate 4 commands Aliases: No aliases yet Quick peek Open full term page

Monitoring and Observability premium Log Analytics table A Log Analytics table is the named storage structure inside a Log Analytics workspace that holds one kind of Azure Monitor log record, with its own schema, plan, retention behavior, access considerations, and KQL query patterns for troubleshooting, analytics, and compliance. Azure Monitor Logs fundamentals 4 commands Aliases: No aliases yet Quick peek Open full term page

Storage learning-path-anchor Time-based retention Time-based retention in Azure Blob Storage is an immutability policy that keeps blob data in a write-once, read-many state for a specified retention interval. It can be applied at container or version scope to protect records from modification or deletion until the retention period expires. Blob Storage fundamentals 5 commands Aliases: Time-based retention, time based retention, Azure Time-based retention, Microsoft Learn Time-based retention, immutability policy, WORM retention, immutable blob retention, retention days, container immutability policy Quick peek Open full term page

Databases learning-path-anchor Time to live Time to live in Azure Cosmos DB automatically expires items after a configured number of seconds. TTL can be set at the container level and overridden per item, helping teams remove stale operational data, limit storage growth, and keep queries focused on current records. Azure Cosmos DB intermediate 4 commands Aliases: Time to live, time to live, Azure Time to live, Microsoft Learn Time to live, TTL, Cosmos DB TTL, defaultTtl, item expiration, container TTL Quick peek Open full term page

AI and Machine Learning verified Realtime endpoint In Azure Machine Learning, a realtime endpoint is an online endpoint that exposes a model for real-time inferencing over HTTPS. It receives input data, routes traffic to one or more deployments, and returns predictions or other model outputs for interactive applications. Azure Machine Learning intermediate 5 commands Aliases: online endpoint, real-time inference endpoint, managed online endpoint, Azure ML realtime endpoint Quick peek Open full term page

Analytics field-manual-complete Stream Analytics input A Stream Analytics input is a named connection between a Stream Analytics job and a data source. Inputs can represent live event streams or reference data, are used by name in the query, and define source type, serialization, authentication, and related connection settings. Streaming analytics fundamentals 5 commands Aliases: Stream Analytics input, ASA input, stream input, reference input for Stream Analytics Quick peek Open full term page

Analytics field-manual-complete Stream Analytics watermark A Stream Analytics watermark is the job’s best answer to the question, “How far through event time are we?” Streaming data does not always arrive in perfect order. Devices can be offline, brokers can buffer messages, and clocks can drift. The watermark lets the service decide when it is safe to close a time window and produce output. A larger tolerance accepts more late data but delays results. A smaller tolerance produces faster answers but may drop or adjust late events. Streaming analytics fundamentals 5 commands Aliases: Azure Stream Analytics watermark, ASA watermark, event-time watermark, watermark delay Quick peek Open full term page

Analytics field-manual-complete Stream Analytics windowing function A Stream Analytics windowing function lets you ask questions about events over time instead of one event at a time. You might count transactions every minute, average machine temperature over five minutes, detect user activity sessions, or compare current values with a snapshot. The window defines which events belong together before the query aggregates them. Choosing the wrong window can make alerts noisy, late, or misleading, so it is one of the most important design choices in a streaming query. Streaming analytics fundamentals 5 commands Aliases: Azure Stream Analytics windowing function, ASA window function, temporal window, streaming window function Quick peek Open full term page

Storage field-manual-complete Visibility timeout The Queue Storage interval that hides a received message from other consumers while a worker processes it. Azure Queue Storage intermediate 5 commands Aliases: queue visibility timeout, message visibility timeout, invisible timeout, queue message lease, visibility timeout Quick peek Open full term page

Analytics premium Azure Synapse Analytics Azure Synapse Analytics is Microsoft’s integrated analytics service for enterprise data warehousing, big data, data integration, and exploratory analytics. Synapse Analytics intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

Storage premium Data Lake analytics workload A production analytics workload that stores, transforms, governs, and serves large data sets from a data lake using Azure services such as ADLS Gen2, Data Factory, Databricks, Synapse, or Fabric. Data Lake Storage Intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

Databases premium Azure SQL point-in-time restore A database capability or setting in Azure SQL Database that helps teams store, query, scale, secure, and recover application data with clearer ownership, safety, and operational context. Azure SQL Database fundamentals 5 commands Aliases: No aliases yet Quick peek Open full term page

Security premium Just-in-time VM access Just-in-time VM access is the Microsoft Defender for Cloud control that locks down VM management ports and opens approved inbound access only for a limited time. Defender for Cloud intermediate 5 commands Aliases: JIT VM access, Defender for Cloud JIT, just in time access, JIT network access policy Quick peek Open full term page

Databases premium Point-in-time restore Point-in-time restore means choosing a time before something went wrong and restoring a database from backup history to that moment. It is not a magic undo button for every application issue; it usually creates a new database or restored instance that teams must verify, reconnect, or copy from. Operators use it after accidental deletes, bad migrations, data corruption, or failed releases. The important parts are the retention window, the restore target, the timestamp, and the validation work after restore. Azure SQL intermediate 5 commands Aliases: No aliases yet Quick peek Open full term page

Databases premium PostgreSQL log analytics PostgreSQL log analytics means collecting PostgreSQL flexible server logs in Azure Monitor so operators can search them, build alerts, and investigate problems with KQL. Instead of downloading log files manually or guessing from symptoms, teams route PostgreSQLLogs and related categories to a Log Analytics workspace. The value comes from patterns: failed connections, slow queries, audit events, PgBouncer signals, configuration effects, and workload changes. It is not free and not automatically useful; teams must choose categories, retention, workspace access, alert logic, and dashboards that match the database risk. PostgreSQL flexible server fundamentals 5 commands Aliases: PostgreSQL log analytics, postgresql log analytics, Azure Database for PostgreSQL flexible server Quick peek Open full term page

Databases premium Backup / point-in-time restore Backup point-in-time restore lets Azure database services restore a protected database or server to a specific earlier timestamp within the configured backup retention period. Database continuity advanced 4 commands Aliases: PITR, point in time restore, point-in-time restore Quick peek Open full term page

Identity premium Log Analytics Data Reader A Log Analytics Data Reader assignment is an Azure built-in RBAC role for letting approved users, groups, or workload identities query the Log Analytics logs they are allowed to view across workspaces and tables without granting workspace administration, table management, or broader monitoring control. Azure RBAC fundamentals 4 commands Aliases: No aliases yet Quick peek Open full term page

Monitoring and Observability premium Log Analytics workspace A Log Analytics workspace is the Azure Monitor Logs data store where collected log records are retained, secured, queried with KQL, and used by alerts, workbooks, Microsoft Sentinel, Application Insights, and operations teams to troubleshoot and govern Azure workloads in governed production environments. Azure Monitor Logs fundamentals 4 commands Aliases: workspace, LAW Quick peek Open full term page

Storage premium Message invisibility timeout Message invisibility timeout is the temporary hiding period applied to an Azure Storage Queue message after a worker receives it. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Queue Storage intermediate 4 commands Aliases: visibility timeout, storage queue invisibility timeout, message visibility timeout Quick peek Open full term page

Storage premium Message time-to-live Message time-to-live is the expiration period that limits how long a queued or brokered message remains useful for delivery. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Queue Storage fundamentals 4 commands Aliases: message TTL, queue message TTL, message expiration Quick peek Open full term page

Monitoring and Observability premium Metric time grain Metric time grain is the time interval Azure Monitor uses to aggregate metric samples for charts, queries, or alert evaluation. Teams should manage it with clear ownership, monitoring, rollback evidence, and production change discipline. Azure Monitor Metrics fundamentals 4 commands Aliases: metric granularity, time grain, aggregation interval Quick peek Open full term page

Databases premium MySQL point-in-time restore MySQL point-in-time restore means a recovery operation that creates a new MySQL Flexible Server from backups at a selected time within the retention window. You see it when teams recover from accidental deletes, failed migrations, data corruption, ransomware impact, or bad application releases. Think of it as a new-server recovery workflow, not an undo button on the original database. It matters because the setting changes how teams design, secure, operate, and troubleshoot the workload. Before changing it in production, know the owner, dependency, evidence, expected result, and rollback path. Azure Database for MySQL fundamentals 4 commands Aliases: No aliases yet Quick peek Open full term page

Storage premium Blob last access time tracking Blob last access time tracking is a Blob service setting that records a LastAccessTime property for blobs when supported access operations occur. Blob Storage intermediate 3 commands Aliases: No aliases yet Quick peek Open full term page

Storage field-manual-complete Last access time tracking Last access time tracking for Azure Blob Storage records when a blob was last read or written so lifecycle policies can use access age. Microsoft Learn describes the lastAccessTime condition, daily update behavior, billing considerations, and limitations when moving or deleting data based on recent access. Storage platform intermediate 6 commands Aliases: No aliases yet Quick peek Open full term page

Databases field-manual-complete PostgreSQL point-in-time restore PostgreSQL point-in-time restore means bringing a flexible server back to what it looked like at a specific earlier time, but Azure does this by creating a new server. It is used after accidental deletes, bad deployments, data corruption, or to clone production for validation. The restore must be inside the server’s backup retention period. It is not a single-table undo button and it does not overwrite production. After the new server exists, teams compare data, redirect applications, export recovered rows, or keep it for testing. PostgreSQL flexible server fundamentals 5 commands Aliases: PostgreSQL PITR, PostgreSQL restore point, flexible server point-in-time restore Quick peek Open full term page

Storage verified Queue visibility timeout Queue visibility timeout is the period after a Storage Queue message is retrieved when it is hidden from other consumers. If the worker deletes the message before that time, processing completes; otherwise the message becomes visible for another processing attempt. Queue Storage intermediate 5 commands Aliases: message visibility timeout, Storage Queue visibility timeout Quick peek Open full term page

AI and Machine Learning verified Realtime API The GPT Realtime API in Azure OpenAI supports low-latency conversational interactions where audio, text, and model responses can stream during the same session. Applications can use supported transports such as WebRTC, SIP, or WebSocket to build voice agents, assistants, and live interaction experiences. Azure OpenAI advanced 5 commands Aliases: GPT Realtime API, Azure OpenAI Realtime API, realtime audio API, streaming voice API Quick peek Open full term page

Storage field-manual-complete Storage analytics logs Storage Analytics logs are classic Azure Storage logs that record details about successful and failed requests for Blob, Queue, and Table services. They help troubleshoot request-level behavior, but Azure Monitor storage logs are generally preferred for modern monitoring, querying, alerting, and centralized retention. Storage monitoring fundamentals 5 commands Aliases: classic storage logs, Azure Storage Analytics logging, storage request logs Quick peek Open full term page

Analytics field-manual-complete Stream Analytics cluster A Stream Analytics cluster is a dedicated single-tenant environment for running Azure Stream Analytics jobs. It is intended for demanding streaming workloads, gives teams control over which jobs use the cluster, and supports private connectivity scenarios that are not covered by ordinary multi-tenant job placement. Stream Analytics intermediate 5 commands Aliases: Stream Analytics cluster, ASA cluster, dedicated Stream Analytics cluster, single-tenant Stream Analytics capacity Quick peek Open full term page

Analytics field-manual-complete Stream Analytics compatibility level Stream Analytics compatibility level is a job setting that controls selected runtime behaviors for query processing. It lets teams keep older jobs stable or opt into newer processing behavior, so changes in query semantics, partitioning, and supported features can be managed deliberately. Streaming analytics fundamentals 5 commands Aliases: Stream Analytics compatibility level, ASA compatibility level, job compatibility level, Stream Analytics runtime compatibility Quick peek Open full term page

Analytics field-manual-complete Stream Analytics function A Stream Analytics function is custom logic that can be invoked from a Stream Analytics query. Functions extend the SQL-like language for operations such as specialized calculations, string handling, enrichment, aggregation, or machine-learning scoring when built-in query expressions are not enough. Stream Analytics intermediate 5 commands Aliases: Stream Analytics function, ASA function, Stream Analytics user-defined function, JavaScript UDF for Stream Analytics Quick peek Open full term page

Analytics field-manual-complete Stream Analytics reference data Stream Analytics reference data is the lookup table your stream uses while events are flowing. Instead of sending every business attribute inside each event, you keep stable or slowly changing information separately, such as device metadata, route codes, tariff bands, or product categories. The query joins live events to that reference set and produces richer output. The key idea is simple: events tell you what happened now, while reference data explains what that event means in the business context your operators care about. Streaming analytics intermediate 5 commands Aliases: Azure Stream Analytics reference data, reference data input, ASA reference data, lookup data for Stream Analytics Quick peek Open full term page

Storage field-manual-complete Time-based retention policy A time-based retention policy is an immutable Blob Storage rule that keeps protected blobs or blob versions in a WORM state for a specified number of days. Until the retention interval expires, authorized users can read the data but cannot delete or overwrite it. Blob Storage intermediate 5 commands Aliases: immutable retention policy, time based retention policy, blob retention policy, WORM retention policy, immutability policy Quick peek Open full term page

Web field-manual-complete Timer trigger A timer trigger is an Azure Functions trigger that starts a function on a defined schedule, using a CRON expression or TimeSpan where supported. It lets teams run serverless code for recurring jobs without a separate scheduler, VM, or always-on worker. Azure Functions intermediate 5 commands Aliases: scheduled function, Azure Functions timer, timer binding, scheduled trigger, CRON trigger Quick peek Open full term page

Analytics field-manual-complete Stream Analytics job diagram A Stream Analytics job diagram is a visual map of what the job is doing. Instead of reading only a query file and metric table, you can see inputs, query steps, outputs, and sometimes physical streaming nodes on one canvas. It is especially helpful when a job runs but produces no results, late results, or results that look wrong. The diagram gives developers and operators a faster way to narrow the problem before rewriting the query or scaling blindly. Stream Analytics intermediate 4 commands Aliases: Stream Analytics job diagram, ASA job diagram, Stream Analytics logical diagram, Stream Analytics physical diagram, job diagram preview Quick peek Open full term page

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 Quick peek Open full term page

Analytics learning-path-anchor Synapse Spark pool A Synapse Spark pool is the workspace compute definition Azure Synapse uses to start Apache Spark sessions. It records node size, node count, autoscale behavior, runtime version, packages, and idle timeout so notebooks, Spark jobs, and pipelines get repeatable distributed processing. Synapse Analytics fundamentals 8 commands Aliases: Apache Spark pool, Spark pool, Synapse Apache Spark pool, serverless Spark pool Quick peek Open full term page

Analytics premium Fabric capacity A Fabric capacity is a dedicated pool of compute resources that powers Microsoft Fabric workloads assigned to workspaces. Teams use it to provide shared compute for Fabric workspaces, reports, lakehouses, warehouses, notebooks, pipelines, and real-time workloads under an assigned capacity SKU. It is not a single workspace, a Power BI report, a storage account, a Databricks cluster, or a guarantee that every tenant workload has unlimited performance. In production, confirm capacity name, SKU, region, admin list, assigned workspaces, workload settings, metrics app evidence, throttling, refresh history, pause state, billing owner, and reservation or scaling plan before treating the design as. Microsoft Fabric intermediate 6 commands Aliases: Microsoft Fabric capacity, Fabric F SKU, Fabric compute capacity Quick peek Open full term page

AI and Machine Learning premium Batch endpoint An Azure Machine Learning batch endpoint is an endpoint for long-running asynchronous inferencing that receives input data references, starts a batch job, and writes outputs for later use. Machine learning advanced 5 commands Aliases: No aliases yet Quick peek Open full term page

Analytics premium Data flow debug session The active design-time session that keeps data flow debug compute available for previewing transformations, expressions, and pipeline debug activity behavior. Data Factory Intermediate 5 commands Aliases: No aliases yet Quick peek Open full term page