As more developers shift to cloud-native practices, more applications are developed and deployed using containerization. The goal is typically to to enable micro-services architecture, which is highly scalable.

To manage containerization, developers use container orchestration platforms, like Kubernetes. However, it is not only developers who can make use of Kubernetes. Read on to learn how to use Kubernetes for big data.

Microsoft Azure offers a wide array of storage options for documents, objects, data and media. Companies working with big data need repositories that can hold large amounts of data in one place. Choosing the right storage for your data in Azure can be complicated. Read on to learn about Azure’s offerings for big data, and the appropriate use case for each service..

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Every year, digital environments accumulate more and more data. Big data systems take in these huge amounts of raw data, and then turn it into meaningful information. These systems can operate on-premises or in the cloud.

The increasing amount of available data has been a game changer for organizations of all sizes. This big data streams from myriad sources, such as social media and business applications, into company networks, which companies can leverage to improve their operations..

This article will describe the relevance of open source software and big data before describing five interesting and useful open source big data tools and projects.

Big data workloads are those that involve the processing, storage, and analysis of large amounts of unstructured data to derive business value from that data. Traditional computing approaches and data processing software weren’t powerful enough to cope with big data, which typically inundates organizational IT systems on a daily basis.

The widespread adoption of Big Data analytics workloads over the past few years has been driven, in part, by the open source model, which has made frameworks, database programs, and other tools available to use and modify for those who want to delve into these big data workloads..

Disaster recovery is a set of processes, techniques, and tools used to swiftly and smoothly recover vital IT infrastructure and data when an unforeseen event causes an outage.

The statistics tell the best story about the importance of disaster recovery—98 percent of organizations reported that a single hour of downtime costs over $100,000, while 81 percent indicated that an hour of downtime costs their business over $300,000...

Amazon Web Services (AWS) is a subsidiary of Amazon that provides cloud computing services, accessible to both individuals and companies.

While newer cloud providers like Microsoft Azure and Google grow at a faster rate, AWS still holds a commanding position at the top of the cloud provider market..

Data visualization describes techniques and tools that help people understand the meaning of data by representing it in a visual context. It’s often easier to identify important trends, correlations, and patterns in data which is presented visually rather than in its native format. Data visualization is particularly helpful in a Big Data landscape, in which many enterprises try to derive insights from huge data sets, with information obtained from myriad sources, including social media, enterprise software, and web analytics.

Read on to find out how data visualization is linked to another extremely relevant aspect of technology—responsive web design and responsive images..

In a world where organizations of all sizes gather vast quantities of data every single day, there is a real need to get insights from these huge data sets that drive better business decisions. After all, what is the point of collecting data from disparate sources if your business doesn’t get some benefit from all that information?

However, traditional database software cannot process and analyze such voluminous, complex data sets. This is, in essence, the problem that big data analytics solves, by providing powerful systems that can process and get insights from large data sets..