Streaming Analytics is performing analytic computations on streaming data. Data streams can come from devices, sensors, websites, social media, applications, infrastructure systems, and more.
The emergence of the big data revolution means that vast amounts of data are flowing at high velocity, and corporations must process this streaming data in real time to achieve differentiation from their competitors.
Visualization dashboards
Real-time data analysis and reporting
Compatible with multiple data sources
Integrated development tools
Data wrangling/prep
Data enrichment
Automation through machine learning
Streaming analytics is uniquely important in real-time stock-trading analysis by financial services companies. it has also become crucial for real-time fraud detection; data and identity protection services, and analysis of Internet of Things data from sensors embedded in physical objects.
This category of tools is an evolution of Complex Event Processing (CEP) software, designed specifically for the big data era. Some of the differences between these two related categories are:
Stream Processing Engines tend to be distributed while CEP engines tend to be more centralized
Stream Processing Engines require custom code while CEP engines often have a SQL like a query language.
CEP engines are tuned for low latency and often they respond within few milliseconds while Stream Processing engines take close to a second to generate results.
Some companies elect to build their own solutions using sometimes components like message brokers, stream processing engines, data extraction tools, and more. However, the degree of expertise required to build easy-to-use scalable platforms can be daunting. For this reason, many prefer to purchase end-to-end platforms. These products are often provided as cloud services and pricing plans vary, making direct comparison difficult. Some products charge users by streaming units consumed and the amount of data processed. Other vendors charge by usage hours.
The TIBCO StreamBase® platform is a high-performance system for rapidly building applications that analyze and act on real-time streaming data. Using StreamBase, users can rapidly build real-time systems and deploy them at a fraction of the cost and risk of other alternatives.
IBM Streaming Analytics is a fully managed service that frees you from time-consuming installation, administration, and management tasks, giving you more time to develop streaming applications. It is powered by IBM Streams, an advanced analytic platform that you can use to ingest, analyze, and co...
Amazon Kinesis is a streaming analytics suite for data intake from video or other disparate sources and applying analytics for machine learning (ML) and business intelligence.
VIA, from Vitria in Menlo Park, is an analytics platform which can consume large quantities of data from disparate systems and produce multi-dimensional, time-series and predictive analyses.
San Francisco-based SQLstream offers SQLstream Blaze, a streaming analytics platform for high throughput data, supporting a variety of streaming sources, data discovery, data wrangling, real-time threat detection and analytics.
BlackLynx (formerly Ryft) offers their Accelerated Analytics service.
Used by Microsoft, Amazon, Google, and many more, Fluentd was invented by Treasure Data to easily collect, parse, and deliver massive amounts of data from applications, infrastructure, network devices, and log files. Enterprise Fluentd expands on that original vision and brings enterprise-grade s...
Microsoft offers Azure Stream Analytics for IoT and connected devices, supporting real-time analytics and reporting.
Apache Flink is a streaming analytics for tracking real-time events over event-based applications, for managing high throughput with guaranteed correctness and consistency.
Google offers Cloud Dataflow, a managed streaming analytics platform for real-time data insights, fraud detection, and other purposes.
SAS Event Stream Processing is a real-time streaming data analytics platform supporting high throughput workflows and processes such as IoT, sensors, and other transactions.
Impetus in Los Gatos offers StreamAnalytix, a streaming analytics platform for receiving multi-structured data (NoSQL and RDBMS), messaging services, and cloud data stores, and supplying real-time actionable analytics on high velocity data.
StreamSets in San Francisco offers their DataOps Platform, a subscription based streaming analytics platform including StreamSets Data Collector data source management, Control Hub for data movement architecture management, StreamSets Data Collector Edge IoT manager, DataFlow Performance Manager...
Oracle supports streaming analytics needs with Oracle Stream Analytics, currently in its 18th edition.
Amazon Kinesis Analytics is a streaming analytics product from AWS. The product provides real-time streaming data insights and allows users to write SQL queries on streaming data.
Esper Enterprise Edition is an enterprise ready, complex event and streaming analytics processing platform.
Apache Spark Streaming is a scalable fault-tolerant streaming processing system that natively supports both batch and streaming workloads.
Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing developed by the Apache Software.
Hitachi Vantara offers the Hitachi Streaming Data Platform (HSDP) supporting IoT operations with data-in-motion streaming analytics.
Altimetrik headquartered in Southfield offers the A3P analytics tool.
Kinetica headquartered in San Francisco offers the Kinetica Active Analytics Platform, a streaming analytics platform based on an embedded database.
Xavient Digital Data Ingestion Platform (DiP) provides a streaming analytics solution for telecoms.
