Real-Time Data Analysis
Ability to analyze data in motion
Cat avg: 8.5
Top Performing Features
Low Latency
How many milli-seconds or seconds it takes to ingest, analyze and respond to an incoming event or data-point
Cat avg: 8.4
Data Enrichment
Ability to enrich the data stream with static reference data
Cat avg: 7.7
Linear Scale-Out
Easy to scale out or scale down by visually changing the resource allocation. This allows changes in load or traffic to be handled without interruptions
Cat avg: 7.5
Worst Performing Features
Data wrangling and preparation
Tools to rapidly prepare data for analysis by normalization, data cleansing, etc.
Cat avg: 7.8
Data Ingestion from Multiple Data Sources
Ability to ingest data from many sources including Internet of Things (IoT) endpoint data, stock trading data etc, as well as static data
Cat avg: 8.7
Apache Flink Features from Reviews
Streaming Analytics
Streaming Analytics is performing analytic computations on streaming data. Data streams can come from devices, sensors, websites, social media, applications, infrastructure systems, and more.
8.7+6%
Real-Time Data Analysis
Ability to analyze data in motion
Category average: 8.5
Data Ingestion from Multiple Data Sources
Ability to ingest data from many sources including Internet of Things (IoT) endpoint data, stock trading data etc, as well as static data
Category average: 8.7
Low Latency
How many milli-seconds or seconds it takes to ingest, analyze and respond to an incoming event or data-point
Category average: 8.4
Data wrangling and preparation
Tools to rapidly prepare data for analysis by normalization, data cleansing, etc.
Category average: 7.8
Linear Scale-Out
Easy to scale out or scale down by visually changing the resource allocation. This allows changes in load or traffic to be handled without interruptions
Category average: 7.5
Data Enrichment
Ability to enrich the data stream with static reference data
Category average: 7.7