Apache Ozone is a distributed object store built on top of Hadoop Distributed Data Store service. It can manage billions of small and large files that are difficult to handle by other distributed file systems. Ozone supports rich APIs such as Amazon S3, Kubernetes CSI as well as native Hadoop File System APIs.
The more time I spend working with data, and watching how our customers work with data, the more convinced I am of two things: 1) the power to do extraordinary things is embedded within data and 2) all of us working or dealing with data have a role to play in using our knowhow and technology to apply data to benefit humanity and tackle some of the biggest challenges of our lifetime – the environment, equality, education, health and safety.
Cloudera Data Platform (CDP) is now available on Microsoft Azure Marketplace – so joint customers can easily deploy the world’s first enterprise data cloud on Microsoft Azure.
Right now, we are seeing the importance of trusted data in helping people navigate the situation we are currently facing. And by people, I mean everyone! A lot of people who would normally never look at a report or use a dashboard, are sharing reams of data on social media, discussing #flatteningthecure and infection/mortality rates. The list goes on.
Time Series as Fast Analytics on Fast Data Since the open-source introduction of Apache Kudu in 2015, it has billed itself as storage for fast analytics on fast data. This general mission encompasses many different workloads, but one of the fastest-growing use cases is that of time-series analytics. Time series has several key requirements: At first glance, it sounds like these requirements would demand a special-purpose database system built specifically for time series.
The telecommunications industry is in the midst of a fundamental reinvention and transformation. Faced with a range of emerging pressures – including consolidation, a changing competitive landscape, and commoditization of traditional services – communication service providers (CSPs) are seeking new revenue streams and novel business approaches.
The low code, no code paradigm is an evolution in development environments. Historically, if you wanted to build a product, you went to a developer and they started from scratch building the product from the first line of code. But now people with little or no coding experience can build simple applications themselves.
Hadoop ecosystem now serves as a comfortable home to Big Data now, and the Hadoop data stores now have a greater acceptance across the world by programmers, developers, data scientists, and database management experts. These ecosystems are as convenient as the data storages; however, the inherent reporting system of Hadoop poses a few challenges for the users to overcome.
The theoretical bases for Machine Learning have existed for decades yet it wasn’t until the early 2000’s that the last AI winter came to an end. Since then, interest in and use of machine learning has exploded and its development has been largely democratized. Perhaps not so coincidentally, the same period saw the rise of Big Data, carrying with it increased distributed data storage and distributed computing capabilities made popular by the Hadoop ecosystem.
Augmented analytics is when you take what was traditionally a very manual workflow and automate it. This gives you the ability to analyze data far more rapidly and to package up changes for humans to interpret. Essentially you’re augmenting a human experience, so rather than spending all your time looking for a needle in the haystack, the machine finds the needle and gives it to you.
