Internet of Things (IoT) and streaming data analytics

Many analyst firms continue to tell us that the Internet of Things phenomenon (IoT) is expected to connect more than 30 billion devices by 2020 and therefore generate trillions of dollars in global economic benefit based on increased efficiency, new business models and new revenue streams.

While the figures may vary, one thing is indeed true: virtually every industry across enterprise and consumer market sectors will be impacted by the Internet of Things.

However, IoT-style streaming data is already a reality for many large-scale applications, where data continues to be generated and processed at fast speeds. When it comes to analyzing such flowing data, EXASOL has the technology to handle and analyze large-scale streaming data – today.

Internet of Things

Features at a glance:
  • Interactive performance on IoT streaming data: EXASOL can handle the analysis of hundreds of TBs with ease, allowing users to benefit from true, interactive analytics
  • Parallelized data load: EXASOL is able to load terabytes of data every hour that are required for real-time processing and analytics
  • Fast and unlimited scale-out: EXASOL clusters can be easily extended by adding further nodes as data volumes increase. Due to EXASOL’s intelligent algorithms, clusters can be enlarged and resized quickly – no new data redistribution is required

To ensure a seamless integration into an IoT infrastructure, EXASOL provides integration components that are based on Apache Kafka and allow fully parallelized, high performance data imports.

Furthermore, by using a stream data integration connector, it is also easy to connect EXASOL to any established IoT infrastructure (e.g. based on Apache Storm or Apache Spark).

Benefits of EXASOL and IoT/streaming data:
  • Direct data loading in parallel
  • Full-featured analytic performance layer for low latency and interactive adhoc data exploration
  • No need for manual administration, programming or tuning
  • Maximal performance through MPP, in-memory computing
  • Most scalable analytic RDBMS on the market
References
Accarda
adidas
ARZ
Atheon Analytics
Badoo
BIScience
Blue Yonder
BOKU
Cacau Show
CCV
COOP
Dataforce
datamobile
Digital Alchemy
Econda
Fyber
GfK Entertainment
Gruner + Jahr
IMS
King
Kreditech
Medion
myThings
Olympus
RatePAY
Semikron
sensalytics
Sony Music
Stayfriends
Turtle Entertainment
United Internet Dialog
Voest Alpine
Webtrekk
wefi
Wooga
Wunderdata
Xing
Xplosion
Zalando