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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 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

Integration premium Event Hubs checkpoint An Event Hubs checkpoint is a consumer-maintained record of progress, usually an offset or sequence number per partition, used to resume processing without rereading every event. Teams use it to remember how far a consumer has processed in each partition so stream processing can restart, scale, or fail over without losing its place. It is not the event data itself, a retention policy, a capture archive, or a guarantee that every downstream business action succeeded exactly once. Event Hubs intermediate 5 commands Aliases: Event Hubs consumer checkpoint, Event Hubs offset checkpoint, EventProcessor checkpoint Quick peek Open full term page

Integration premium Sequence number A sequence number in Azure Event Hubs is the ordered number assigned to an event within a specific partition. It helps consumers identify position in the partition log, resume or replay processing from a known point, and diagnose gaps, duplicates, or lag alongside offsets and enqueued time. Event Hubs stream processing intermediate 4 commands Aliases: Event Hubs sequence number, event sequence number, partition sequence number, stream sequence number Quick peek Open full term page

Integration premium Checkpoint In Event Hubs, checkpointing is the consumer responsibility of saving the current offset so processing can resume, fail over, or replay from a known position. Messaging and eventing intermediate 3 commands Aliases: No aliases yet Quick peek Open full term page

Analytics verified Reference data input A reference data input is the wiring between a Stream Analytics job and the lookup data it needs. The reference data is the table or file; the input is the job setting that says where that data lives, what alias the query Streaming analytics fundamentals 5 commands Aliases: Stream Analytics reference input, reference input alias, reference lookup input Quick peek Open full term page

Analytics verified Watermark In Azure Stream Analytics, a watermark is the engine's event-time progress marker. It advances from observed event times and configured tolerance windows, letting the job decide when a time window is complete enough to emit repeatable, timely results at scale. Stream processing intermediate 5 commands Aliases: Azure Stream Analytics watermark, event-time watermark, streaming watermark, watermark time Quick peek Open full term page

Integration premium Event Hubs processing unit An Event Hubs processing unit is reserved Premium tier capacity that provides isolated compute, memory, and storage resources for an Event Hubs namespace. Teams use it to size Premium Event Hubs workloads that need predictable streaming capacity, stronger tenant isolation, and room for busy producers and consumers. It is not a Standard throughput unit, a Dedicated capacity unit, a partition count, or an automatic guarantee that every consumer application will keep up. In production, confirm the namespace, event hub, partitions, identity, network path, consumer groups, checkpoints, metrics, owner, and rollback plan before treating the stream design as healthy. Event Hubs intermediate 5 commands Aliases: processing unit, PU, Premium processing unit Quick peek Open full term page

Integration verified Processing unit A processing unit, often shortened to PU, is the capacity block behind an Azure Event Hubs Premium namespace. Event Hubs intermediate 6 commands Aliases: No aliases yet 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 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 field-manual-complete Streaming unit A streaming unit is the capacity dial for an Azure Stream Analytics job. When the job needs more compute and memory to keep up with input events, you adjust streaming units rather than managing servers. More SUs can help a job process higher volume, but they are not magic. The query must be able to use the capacity, the inputs need useful partitioning, and the outputs must keep up. Operators treat SUs as a cost and performance lever that must be measured, not guessed. Streaming analytics fundamentals 5 commands Aliases: Azure Stream Analytics streaming unit, Stream Analytics SU, SUs, streaming units 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 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

Monitoring and Observability premium Alert processing rule An alert processing rule is an Azure Monitor rule that modifies fired alerts, such as adding or suppressing action groups, applying filters, or using schedules without changing the alert rule itself. Azure Monitor Alerts fundamentals 3 commands Aliases: Azure Monitor alert processing rule, notification suppression rule, alert action rule Quick peek Open full term page

Web field-manual-complete App Service log stream App Service log stream is the live tail for an App Service app. App Service diagnostics intermediate 5 commands Aliases: Azure App Service log stream, app service log stream Quick peek Open full term page

Containers verified Container Apps log stream A live log stream for a Container App replica or revision. Azure Container Apps intermediate 5 commands Aliases: No aliases yet Quick peek Open full term page

Storage verified Queue batch processing A storage feature or access model in Queue Storage that helps teams store, protect, move, and govern application or analytics data with clearer ownership, safety, and operational context. Queue Storage fundamentals 5 commands Aliases: No aliases yet 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 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 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 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

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

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

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

Web premium Event Hub trigger An Event Hub trigger is an Azure Functions trigger binding that runs a function when events are available from an Azure Event Hub stream. Teams use it to process streaming events with serverless function code instead of running a dedicated worker service for every Event Hubs consumer workload. It is not the event hub itself, a producer, a capture archive, or a guarantee that downstream business processing completed successfully. In production, confirm the namespace, event hub, partitions, capacity, identity, network path, consumer group, checkpoint behavior, monitoring, and owner before treating the stream as safe. Azure Functions intermediate 5 commands Aliases: Azure Functions Event Hub trigger, Event Hubs trigger, Event Hubs trigger binding Quick peek Open full term page

Integration premium Event Hubs Azure Event Hubs is a fully managed, real-time data streaming platform for ingesting large volumes of events with low latency and routing them to consumers for processing. Teams use it to collect telemetry, application events, logs, clickstreams, and device data at scale before analytics, functions, or downstream services process them. It is not a queue for command messages, a workflow engine, a database, or an Event Grid routing topic for discrete platform events. In production, confirm the namespace, event hub, partitions, capacity, identity, network path, consumer group, checkpoint behavior, monitoring, and owner before treating the stream as safe. Event Hubs intermediate 5 commands Aliases: Azure Event Hubs, Azure Event Hubs service Quick peek Open full term page

Integration premium Event Hubs auto-inflate Event Hubs auto-inflate is a Standard tier feature that automatically increases throughput units up to a configured maximum when namespace traffic exceeds current capacity. Teams use it to absorb variable ingress or egress bursts without manually raising throughput units every time traffic spikes. It is not automatic scale-down, unlimited capacity, partition scaling, or a replacement for capacity testing and consumer lag monitoring. In production, confirm the namespace, event hub, partitions, capacity, identity, network path, consumer group, checkpoint behavior, monitoring, and owner before treating the stream as safe. Event Hubs intermediate 5 commands Aliases: Auto-inflate for Event Hubs, Event Hubs automatic throughput scaling Quick peek Open full term page

Integration premium Event Hubs batch An Event Hubs batch is a client-side group of events prepared for sending or processing together while respecting Event Hubs size, partition, and producer constraints. Teams use it to send or process multiple events efficiently instead of making every event a separate network operation. It is not an Azure resource, a Capture file, a partition, or a guarantee that every event in business logic was processed exactly once. In production, confirm the namespace, event hub, partitions, capacity, identity, network path, consumer group, checkpoint behavior, monitoring, and owner before treating the stream as safe. Event Hubs intermediate 5 commands Aliases: Event Hubs event batch, EventDataBatch, batch send to Event Hubs Quick peek Open full term page

Integration premium Event Hubs consumer offset An Event Hubs consumer offset is the position a consuming application uses to continue reading events within a specific partition, usually captured through checkpoint state. Teams use it to understand where a reader stopped, resumed, replayed, or skipped within a retained Event Hubs partition. It is not a global cursor for the whole event hub, a Service Bus dequeue count, or proof that downstream business processing succeeded. In production, confirm the namespace, event hub, partitions, identity, network path, consumer groups, checkpoints, metrics, owner, and rollback plan before treating the stream design as healthy. Event Hubs intermediate 5 commands Aliases: consumer offset, Event Hubs reader offset, stream consumer position Quick peek Open full term page

Integration premium Event Hubs geo-disaster recovery Event Hubs geo-disaster recovery pairs namespaces and uses an alias so applications can fail over namespace metadata access to a secondary namespace during a regional disaster. Teams use it to keep a stable connection endpoint for disaster recovery planning when an Event Hubs namespace must move to a paired secondary region. It is not automatic failover, a backup of retained event data in standard metadata Geo-DR, or a replacement for application-level replay and regional processing design. Event Hubs intermediate 5 commands Aliases: Event Hubs Geo-DR, Event Hubs disaster recovery alias, Geo-recovery alias Quick peek Open full term page

Integration premium Event Hubs offset An Event Hubs offset is metadata that identifies an event position within a partition of an event hub. Teams use it to describe the exact location of an event in a partition so readers can reason about ordering, replay, and processing position. It is not a timestamp alone, a sequence number alone, a global event hub position, or confirmation that a consumer completed downstream work. In production, confirm the namespace, event hub, partitions, identity, network path, consumer groups, checkpoints, metrics, owner, and rollback plan before treating the stream design as healthy. Event Hubs intermediate 5 commands Aliases: event offset, partition offset, stream offset Quick peek Open full term page

Integration premium Event Hubs partition key An Event Hubs partition key is a value supplied by a producer that determines which partition receives related events, helping preserve order for events with the same key. Teams use it to route related events to the same partition when applications need ordered processing for a tenant, device, account, route, or business entity. It is not a database primary key, a Cosmos DB partition key, a security boundary, or a guarantee that the selected partition will never become hot. Event Hubs intermediate 5 commands Aliases: partition key, event partition key, producer partition key Quick peek Open full term page

Integration premium Event Hubs producer An Event Hubs producer is an application, service, device, or client that sends events to an event hub. Teams use it to identify the workload that publishes telemetry, transactions, logs, or business events into an Event Hubs stream. It is not a consumer, event processor, checkpoint, storage capture destination, or proof that downstream systems processed the event. In production, confirm the namespace, event hub, partitions, identity, network path, consumer groups, checkpoints, metrics, owner, and rollback plan before treating the stream design as healthy. Event Hubs intermediate 5 commands Aliases: producer, event publisher, Event Hubs publisher client Quick peek Open full term page

Integration premium Event Hubs throughput unit An Event Hubs throughput unit is pre-purchased Standard tier capacity shared by all event hubs in a namespace for ingress and egress. Teams use it to size Standard Event Hubs namespaces so producers and consumers have enough shared streaming bandwidth for expected traffic. It is not a Premium processing unit, Dedicated capacity unit, partition count, consumer group, or a guarantee that every application has dedicated capacity. In production, confirm the namespace, event hub, partitions, identity, network path, consumer groups, checkpoints, metrics, owner, and rollback plan before treating the stream design as healthy. Event Hubs intermediate 5 commands Aliases: throughput unit, TU, Standard throughput unit Quick peek Open full term page

Integration premium Event offset An event offset is position metadata that identifies where an event sits within an ordered partition or stream. Teams use it to describe where a specific event was read from so teams can resume, replay, investigate gaps, and compare processing progress. It is not a global event ID, timestamp-only bookmark, consumer group, checkpoint file, or guarantee that business processing completed successfully. In production, confirm the namespace, event hub, partitions, identity, network path, consumer groups, checkpoints, metrics, owner, and rollback plan before treating the stream design as healthy. Event Hubs intermediate 5 commands Aliases: offset, stream offset, partition offset Quick peek Open full term page

Integration premium Event ordering policy An event ordering policy is an architectural rule that defines how producers and consumers preserve required event order, usually by using stable partition keys and partition-scoped processing. Teams use it to decide which events must stay in order and how partition keys, consumers, retries, and downstream writes will preserve that order. It is not a single Azure Event Hubs setting, global ordering across all partitions, a timestamp sort, or a replacement for idempotent processing. In production, confirm the namespace, event hub, partitions, identity, network path, consumer groups, checkpoints, metrics, owner, and rollback plan before treating the stream design as healthy. Event Hubs intermediate 5 commands Aliases: ordering policy, event order strategy, partition ordering rule Quick peek Open full term page

Integration premium Event processor An event processor is an application component that reads events from partitions, coordinates ownership, processes events, and records checkpoints for recovery. Teams use it to scale consumers across partitions while keeping track of where processing should resume after restarts or failures. It is not a producer, event hub namespace, consumer group by itself, checkpoint store alone, or proof that downstream business work cannot fail. In production, confirm the namespace, event hub, partitions, identity, network path, consumer groups, checkpoints, metrics, owner, and rollback plan before treating the stream design as healthy. Event Hubs intermediate 5 commands Aliases: EventProcessorClient, stream processor, event processing application Quick peek Open full term page

Databases premium Cosmos DB change feed Cosmos DB change feed is a built-in way to read changes that happen in a container. Instead of constantly scanning for new or updated items, an application can process the feed and react when writes occur. Teams use it to update search indexes, build materialized views, move data, trigger workflows, or keep another store in sync. It is not the same as a general queue; it is tied to container writes, partitioning, leases, continuation state, and the mode used to read changes. Azure Cosmos DB intermediate 4 commands Aliases: Azure Cosmos DB change feed, change feed processor, Cosmos DB feed Quick peek Open full term page

Analytics premium Delta Live Tables Delta Live Tables is the former name for Lakeflow Spark Declarative Pipelines, a Databricks framework for declarative batch and streaming pipelines using SQL or Python. Azure Databricks intermediate 4 commands Aliases: DLT, Lakeflow Spark Declarative Pipelines, Databricks declarative pipelines, Lakeflow pipelines Quick peek Open full term page

Analytics premium Apache Spark job An Apache Spark job is a submitted Spark workload, often defined in Azure Synapse as a job definition, that runs batch or streaming code on a Spark pool. Azure Synapse Analytics intermediate 3 commands Aliases: No aliases yet Quick peek Open full term page

Integration premium Consumer group an Event Hubs entity that gives a consuming application its own view of the same event stream without sharing read position with other applications Event streaming fundamentals 3 commands Aliases: No aliases yet Quick peek Open full term page

Integration premium Consumer lag the backlog signal showing how far an event-processing application is behind the newest events available in Event Hubs or Kafka-compatible streams Event streaming intermediate 3 commands Aliases: No aliases yet Quick peek Open full term page

Analytics learning-path-anchor Databricks Photon The native vectorized Azure Databricks engine for accelerating eligible SQL, DataFrame, ETL, and streaming workloads. Databricks intermediate 4 commands Aliases: Photon engine, Photon acceleration, Databricks vectorized engine Quick peek Open full term page

AI and Machine Learning learning-path-anchor Text to speech Text to speech in Azure AI Speech converts written text or SSML into synthesized audio using neural voices, custom voice options, language support, and APIs. Teams use it to add spoken responses to apps, contact centers, accessibility tools, devices, and media workflows while governing region, keys, networking, and quota. Azure AI services fundamentals 4 commands Aliases: Text to speech, text to speech, Azure Text to speech, Microsoft Learn Text to speech, TTS, speech synthesis, Azure AI Speech synthesis, neural voices, SSML speech Quick peek Open full term page

Integration learning-path-anchor Throughput unit An Event Hubs throughput unit is a capacity setting in the Standard tier that controls how much ingress and egress a namespace can handle. Operators size TUs, enable Auto-inflate, and monitor throttling so event producers and consumers keep moving data without ServerBusy errors during traffic spikes. Event Hubs advanced 4 commands Aliases: Throughput unit, throughput unit, Azure Throughput unit, Microsoft Learn Throughput unit, Event Hubs TU, TUs, Event Hubs throughput units, Auto-inflate capacity Quick peek Open full term page

Databases premium Change feed processor A database capability or setting in Azure Cosmos DB that helps teams store, query, scale, secure, and recover application data with clearer ownership, safety, and operational context. Azure Cosmos DB fundamentals 15 commands Aliases: No aliases yet Quick peek Open full term page

Analytics verified Windowing function A windowing function in Azure Stream Analytics defines time boundaries for processing continuous events. Tumbling, hopping, sliding, session, and snapshot windows let queries aggregate, join, and detect patterns across event-time periods instead of evaluating each incoming event as a standalone record. Stream Analytics intermediate 6 commands Aliases: Stream Analytics windowing function, temporal window, streaming window, Azure Stream Analytics window Quick peek Open full term page