Internet of Things (IoT) Data Storage
IoT devices can quickly create a massive amount of data that seems unmanageable, but data lake engines can simplify the data pipeline and get greater value from IoT data.
Where data is stored affects both distribution and connectivity. By keeping data in the right type of storage with dedicated private lines, organizations can improve both performance and security.
Energy efficiency is a critical component to a retailer's everyday operations, and IoT sensors and data can help them save on energy consumption and costs.
As data floods in from IoT devices and applications move to the cloud, organizations should consider separating storage from compute with the NVMe/TCP standard.
All data must play a role in organizations hoping to keep a competitive edge with an IoT deployment. Vertica's Joy King explains how unstructured data will seem small compared to IoT data used for ...
Some IoT data is left to sit in data lakes, undermining performance. Co-Founder and CEO of Dremio, Tomer Shiran, discusses how organizations can migrate this unused data into analytics tools.
Data presents the greatest value in IoT, but also one of its biggest challenges. InfluxData's David Simmons discusses how to design with data in mind and manage the data deluge.
Waves of data can make real-time analysis difficult, but in-memory computing, explains GridGain Systems' Nikita Ivanov, can drive business processes with real-time insights into live and historical ...
Smart city growth will soon outpace city planners' ability to protect them. Cloud-to-flash technology, advises Nanolock Security's Yoni Kahana, helps build cities as safe as they are smart.
Using a time series database, says TimescaleDB's Ajay Kulkarni, IoT organizations can garner insights from connected data to build new features, automate processes and drive efficiency.
The exponential growth of IoT devices and the data they create is presenting a major challenge for database management. InfluxData's David Simmons explains how a time series database can help.
Blockchain doesn't inherently represent the state of the world. It takes IoT, says Red Hat's Gordon Haff, to represent physical objects more completely, automatically, timely and resistant to ...
Industrial IoT requires new methods of real-time data management and analysis. However, there are many misconceptions about it. Crate.io's Christian Lutz lists four IIoT misconceptions, and the ...
Nearly 99% of all IoT data collected is wasted – but why? HarperDB's Stephen Goldberg explains why the data management industry must transform to solve the problem.
Surveillance isn't just about security any longer, says Western Digital's Chris Bergey, it's about extracting value and intelligence, making smart video a reality.
Striim CTO Steve Wilkes discusses the three primary challenges of IoT data management and the importance of a streaming-first architecture.
The number of IoT devices being added to networks if the stat most talked about, but it's the network planning aspect of IoT that should conjure discussions.
Legacy distribution patterns aren't always a great fit for IoT systems. IoT data introduces a new distribution pattern your organization needs to accommodate.
If you're going to collect IoT data, you need to collect it somewhere. This is where database systems come into play.
With data engineering, the agility and accuracy of your IoT data can grow over time -- along with the business value, says Infostretch's Manish Mistry.
Before extracting value and intelligence from IoT data, companies must understand the difference between content and context relating to an IIoT environment.
As IoT data moves to the cloud, security is paramount. Utimaco's Malte Pollmann explains why a hardware security module may be the safeguard IoT needs.
Machine learning's promise of improving asset management is what keeps the spark going, says Saviant Consulting's Yatish Patil.
Creating a reliable, available and secure future-proof network ready to cope with unpredictable data demand is non-negotiable, says Ciena's Rob Tomkins.
The cloud can't process data quickly enough for IoT applications. OIES Consulting's Francisco Maroto discusses how fog/edge is stepping up to the plate.
Data lakes combined with the proper analytics technology are well suited for IoT implementation. Red Hat chief architect James Kirkland explains why.
Make sure IoT software development efforts stay focused on the user throughout the process, and across the software stack.
As the internet of things becomes ubiquitous, we must recognize the effect that increased connectivity is having on the data center.
Next-generation IoT platforms that incorporate machine learning will help companies eliminate IoT data exhaust and harness IIoT data as a business asset.
The IoT edge lets developers move application logic to exactly where the data is located at any moment in time for performance, reliability and security.
Knowing where data resides in an IoT world is critical; consider the benefits of identifying data, keeping it available and using it for more efficient use cases.
Deep data not only helps alleviate the stress of ever-increasing amounts of IoT data, but also provides actionable insights for decision makers.
Deciding what type and how much IoT data your organization is going to keep isn't an easy task. Think it through thoroughly – and think of the future, too.
Along with storing IoT data at the back-end, devices should also store data at the device itself; this will prevent outage issues and consumer frustration.
Internet of things benefits cannot be enjoyed if IoT data isn't readily available. Learn how providing availability at the back-end can help.
The impact flash storage has on the Internet of Things positions it as a critical fixture to the overall success of IoT initiatives.
As big data grows with the explosion of IoT, companies must take effective measures to leverage it properly. Learn how object storage can help.