Crosser is a Swedish company that builds a streaming analytics platform. The idea behind Crosser is to take the data from a connected, sensor-rich world and integrate it in real time to deliver faster insights and innovation. Primarily focused on the industrial IoT (IIoT) space, Crosser helps manufacturers gain insight into their machines and processes to drive improvements and to take advantage of newer trends and requirements that companies have for their data.

Accurate data is one of the most important aspects of any organizational function. It helps in decision-making and planning, and for most businesses, it also helps in generating revenue. The data can be anything from a list of clients and products to an inventory list. Nothing comes close to SQL timestamps regarding data accuracy, timeliness, and management. SQL Server timestamp is a critical component of relational databases, but they aren’t used on a daily basis by most database professionals.

This article was originally published in The New Stack and is reposted here with permission. A consequence of living in a rapidly changing society is that the state of all systems changes just as rapidly, and with that comes inconsistencies in operations. But what if you could foresee these inconsistencies? What if you could take a peek into the future? This is where time-series data can help.

So by now, you are probably aware that InfluxData has been busy building the next generation of the InfluxDB storage engine. If you dig a little deeper, you will start to uncover some concepts that might be foreign to you: These open-source projects are some of the core building blocks that make up the new storage engine. For the most part, you won’t need to worry about what’s under the hood.

This article was priginally published on the CNF blog and is written by Cameron Pavey. Scroll down for the author’s bio. Docker is an increasingly popular choice for businesses dealing with containerized applications. However, as with any new technology, Docker introduces complexities that need to be managed. Some of these complexities relate to infrastructure and application monitoring.

A look at what Apache Arrow is, how it works, and some of the companies using it as a critical component in their architecture. Over the past few decades, leveraging big datasets required businesses to perform increasingly complex analysis. Advancements in query performance, analytics, and data storage are largely a result of greater access to memory. Demand, manufacturing process improvements, and technological advances all contributed to cheaper memory.

This article was originally published in The New Stack and is reposted here with permission. Arrow makes analytics workloads more efficient for modern CPU and GPU hardware, which makes working with large data sets easier and less costly. One of the biggest challenges of working with big data is the performance overhead involved with moving data between different tools and systems as part of your data processing pipeline.

If you are working with large amounts of data that will primarily be used for analytics, a column database might be a good option. There are a lot of different options when it comes to choosing a database for your application. A common discussion seems to be the high-level SQL vs. NoSQL database argument of whether data should be stored in a relational database or in a NoSQL alternative like key-value, document or graph databases.