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Stream your Way to Quicker Decisions

Published on 07 October 17
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Stream analytics means the analysis of large, in-motion data called event streams, which comprise events that occur as the result of an action or set of actions that happen within a system at a point in time. Thanks to a growing number of connected devices—the Internet of Things or IoT — the events that surround business activity tend to increase in volume. Your business will derive increased benefits from streaming analytics in proportion to the data your business generates.Also called event stream processing, stream analytics complements traditional analytics by adding real-time insight to your decision-making toolbox. In fact, streaming analytics enables you to arrive at better business decisions by focusing on live, streaming data.Traditional approaches involve batch processing, where data is scored based on a schedule such as hourly, overnight or even weekly. Such approaches are at best reactive because they focus on aging information, which means businesses can only react to past events or conditions.

But stream analyticcan capture events, assess them, make decisions and share the outputs—all within specific time windows. It enables you to proactively respond to real-time analytic computations on streaming data. Stream Analytics examines high volumes of data flowing from devices or processes, sensors, web sites, social media feeds, applications, and infrastructure systems.

You can perform a variety of tasks with stream analytics. For example, you can do personalized, real-time stock-trading analysis and alerts offered by financial services companies. Also, you can achieve real-time fraud detection based on examining transaction data. You can analyse data generated by sensors and actuators embedded in physical objects (IoT).Stream Analytics also comes handy in customer relationship management (CRM) applications, such as issuing alerts when customer experience within a time frame is degraded.
Designed to be easy to use, flexible, scalable to any job size, and economical, stream analytics lets you create sophisticated analyses with no programming. You can edit queries in the portal, using IntelliSense and syntax checking, and you can test queries using sample data that you can extract from the live stream.

You can extend the capabilities of the query language by defining and invoking additional functions. You can define function calls in the machine learning service. You can also integrate JavaScript user-defined functions (UDFs) in order to perform complex calculations as part a stream analytics query.

Stream Analytics can handle up to 1 GB of incoming data per second. Integration with event hubs and IoT hub allows jobs to ingest millions of events per second coming from connected devices, clickstreams, and log files, to name a few.

As a cloud service, stream analytics comes at low cost. You pay as you go based on streaming-unit usage and the amount of data processed by the system.

As a managed service in the cloud, stream analytics helps prevent data loss and provides business continuity. If failures occur, the service provides built-in recovery capabilities. Spark is a lightning-fast cluster computing framework designed for fast computation. An open source data processing framework for performing Big data analytics on distributed computing cluster, sparkanalytics efficiently use more types of computations which includes Interactive Queries and Stream Processing.Spark, a cluster computing framework for processing complex workloads with synchronous and asynchronous operations, supports interactive processing -- including the kinds of pipelined operations that tend to get performed in analytic and DI processing.

Spark analytics isn't in any sense bound to Hadoop. Spark can run in other contexts, too. That's one of its most attractive features.Spark has several advantages when compared to other big data and MapReduce technologies like Hadoop and Storm. Spark offers low latency due to reduced disk input and output operation. Spark has the capability of in memory computation and operations, which makes the data processing faster than other MapReduce.
Stream analytics means the analysis of large, in-motion data called event streams, which comprise events that occur as the result of an action or set of actions that happen within a system at a point in time. Thanks to a growing number of connected devices—the Internet of Things or IoT — the events that surround business activity tend to increase in volume. Your business will derive increased benefits from streaming analytics in proportion to the data your business generates.Also called event stream processing, stream analytics complements traditional analytics by adding real-time insight to your decision-making toolbox. In fact, streaming analytics enables you to arrive at better business decisions by focusing on live, streaming data.Traditional approaches involve batch processing, where data is scored based on a schedule such as hourly, overnight or even weekly. Such approaches are at best reactive because they focus on aging information, which means businesses can only react to past events or conditions.

But stream analyticcan capture events, assess them, make decisions and share the outputs—all within specific time windows. It enables you to proactively respond to real-time analytic computations on streaming data. Stream Analytics examines high volumes of data flowing from devices or processes, sensors, web sites, social media feeds, applications, and infrastructure systems.

You can perform a variety of tasks with stream analytics. For example, you can do personalized, real-time stock-trading analysis and alerts offered by financial services companies. Also, you can achieve real-time fraud detection based on examining transaction data. You can analyse data generated by sensors and actuators embedded in physical objects (IoT).Stream Analytics also comes handy in customer relationship management (CRM) applications, such as issuing alerts when customer experience within a time frame is degraded.

Designed to be easy to use, flexible, scalable to any job size, and economical, stream analytics lets you create sophisticated analyses with no programming. You can edit queries in the portal, using IntelliSense and syntax checking, and you can test queries using sample data that you can extract from the live stream.

You can extend the capabilities of the query language by defining and invoking additional functions. You can define function calls in the machine learning service. You can also integrate JavaScript user-defined functions (UDFs) in order to perform complex calculations as part a stream analytics query.

Stream Analytics can handle up to 1 GB of incoming data per second. Integration with event hubs and IoT hub allows jobs to ingest millions of events per second coming from connected devices, clickstreams, and log files, to name a few.

As a cloud service, stream analytics comes at low cost. You pay as you go based on streaming-unit usage and the amount of data processed by the system.

As a managed service in the cloud, stream analytics helps prevent data loss and provides business continuity. If failures occur, the service provides built-in recovery capabilities. Spark is a lightning-fast cluster computing framework designed for fast computation. An open source data processing framework for performing Big data analytics on distributed computing cluster, sparkanalytics efficiently use more types of computations which includes Interactive Queries and Stream Processing.Spark, a cluster computing framework for processing complex workloads with synchronous and asynchronous operations, supports interactive processing -- including the kinds of pipelined operations that tend to get performed in analytic and DI processing.

Spark analytics isn't in any sense bound to Hadoop. Spark can run in other contexts, too. That's one of its most attractive features.Spark has several advantages when compared to other big data and MapReduce technologies like Hadoop and Storm. Spark offers low latency due to reduced disk input and output operation. Spark has the capability of in memory computation and operations, which makes the data processing faster than other MapReduce.

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