Moogsoft

Moogsoft

Beam Capability Matrix

Beam Capability Matrix

Distributed Tracing

OpenTelemetry
TraceContext
Jaeger

News Event Exploration

Event Registry
GDELT
STICS
EMM
MediaCloud

Europe Media Monitor

EMM

Dweet.io

Dweet.io

PubNub

PubNub

Pulsar

Apache Pulsar

Outlier Detection

Anomaly Detection Benchmarks
ODDS
Outlier Detection Library
Skyline
Oculus
Anodot
Numenta
AnomalyDetection
awesome-anomaly-detection-timeseries
outlier detection survey
t-digest
Practical Machine Learning Anomaly Detection
anomaly detection with autoencoders

Beam Capability Matrix

Beam Capability Matrix

RocketMQ

RocketMQ

Appian

Appian

Vs Of Big Data

Volume - amount of data
Velocity - speed of data flowing into a system
Variety - different types of data from multiple sources
Veracity - accuracy of inbound and outbound data

Many tools exist that address the above characteristics of Big Data which can be split into three different purposeful strands:

• Data Transfer - e.g Kafka, Flume, Scribe, Scoop
• Data Storage - e.g Hadoop, GlusterFS, Cassandra
• Data Processing - e.g Storm, Flink, Spark, Samza

Delivery Patterns

• At-Most-Once : Messages may be lost, least desirable
• At-Least-Once : Messages may be redelivered - no loss but may include duplicates
• Exactly-Once : Messages are delivered once and only once - no loss and no duplicates, but difficult to guarantee

In order to manage state, messages may be stored in an embedded key-value store, a distributed file system, or use a higher level of abstraction.

Big Data Processing Frameworks

Batch-Only
Hadoop

Stream-Only
Storm
Samza

Hybrid
Spark
Flink

Snowplow

Snowplow Analytics

CEP & BPM

CEP:
Esper
Drools
Flink CEP

BPM:
Activiti
jBPM
Bonita
Talend

Timeseries Databases

Graphite
InfluxDB
M2X
Prometheus
TSDB
DalmatinerDB

Snowplow

Snowplow

Alternatives: KeenIO , RakamIO

Solace

Solace