Tag: terminology

  • DevOps Deeper Dive: IT Terminology Matters

    DevOps Deeper Dive: IT Terminology Matters

    Much like any other profession, the IT industry does not exist in a vacuum. Social upheaval will have as much impact on IT professionals as it does on any other. As conversations concerning race relations become more heated and, hopefully, elevated, many in the IT industry are reconsidering the use of terminology such as master/slave to describe the relationship between applications and devices.

    GitHub this week announced this week it will be changing the term “master” as it is applied to its repository to a neutral term. Use of the phrase master/slave as a means to describe relationships between software and hardware has been employed since the very early days of IT. In fact, some would note the concept of slavery stretches back to the dawn of time.

    Nevertheless, too many of the terms used in IT evoke a sense of domination that reflects a white, male view of the world, said Dr. Shuli Goodman, executive director of LF Energy, an arm of the Linux Foundation committed to combating climate change by modernizing the power grid using open source software.

    “Language reflects our mental models,” she said.

    The IT industry in general would be better off embracing, for example, biological models rather than terminology that suggests domination and submissiveness, noted Goodman.

    Even IBM, which provides some of the oldest and most venerable platforms in the industry, is open to having this conversation.

    The company is committed to recognizing the experiences of everyone in the IT community, said Sanjay Chandru, director of IBM Z DevOps.

    “If that means we need to have a conversation about terminology, we’re open to that,” he said. “Personally, I think GitHub did the right thing.”

    The time to make a mental shift might never be better, said JFrog CEO Shlomi Ben Haim. The IT industry is transitioning toward a more distributed approach to computing that relies less on centralization. As more workloads shift to the edge, IT environments will need frameworks that span everything from the edge to the cloud, he said.

    As a result, the IT industry as a whole is shifting more toward a peer-to-peer relationship between software and hardware, he added. “We need IT to be more flexible.”

    The history of IT is marked by pendulum swings between centralized and distributed computing. Arguably, the rise of cloud computing represented a shift back toward a more centralized approach to computing that has a lot in common with the way mainframes were deployed and managed more than 50 years ago.

    PCs, on the other hand, were at the forefront of a wave of distributed computing that in many cases simply became too unwieldy to manage. Cloud computing afforded IT organizations an opportunity to centralize the management of workloads. However, with the rise of edge computing, workloads might soon be more distributed than ever.

    As is often the case when it comes to IT trends, it’s likely new terminology will soon emerge to describe the relationship between highly distributed instances of software and hardware. Hopefully, more perspective will be brought to bear not only on what those terms mean from a technical perspective but also how they might be perceived both inside IT and socially.

  • Useful Big Data Terminologies, Part 1

    Useful Big Data Terminologies, Part 1

    As data continues to increase at an evermore rapid pace, organizations struggle to deal with this data torrent, let alone use it to analyze and capture value. The ways used to understand this big data also is increasingly rapidly, which introduces myriad terms used to define these methods.

    The follow is an attempt to provide natural explanations to some of the significant terms and technologies you will come across when you’re getting into big data.

    Algorithms: Mathematical and analytical formulas that also include statistical processes used to analyze data. Algorithms are implemented in software to analyze, process the input data and produce output or results.

    Analytics: The course of depicting conclusions based on the raw data. With the help of analysis, otherwise-meaningless numbers and data can be converted into something useful. The emphasis here is on interpretation and not on big software systems. That may be why data analysts are very experienced in the art of storytelling.

    Biometrics: Using analytics and technology to identify people by one or many of physical characteristics, such as fingerprint recognition, face recognition or iris recognition.

    Cassandra: A very well-known open-source database management system managed by the Apache Software Foundation, which has been constructed to handle high volumes of data throughout distributed servers.

    Cloud: A term used to describe data or software running on remote servers rather than locally. Data stored in the cloud is usually reachable over the internet, wherever the owner of that data in the world might be.

    Database: A systematized collection of data, such as schemas, charts or tables. A database management system (DBMS) is software that helps in data analysis and exploration.

    Data Mining: This term can mean different things for different context. To the layman, it means the automatic examining of large databases. To an analyst, it refers to the pool of statistical and machine learning methods used in the databases.

    Dark Data: The information collected and managed by a business that is never put to use, yet sits waiting to be studied. Most companies don’t realize they have a lot of this kind of data lying around.

    Data Scientist: A skilled expert in extracting value and insights from data. A data scientist typically is someone with skills in computer science, analytics, mathematics, creativity, statistics, communication and data visualization, as well as strategy and business.

    Gamification: The process of creating a game-like environment in areas that typically would not have games, such as websites, to attract users and increase engagement. In the terms of big data, a gamification is a powerful tool for incentivizing the data collection.

    Hadoop: An open-source software structure that works mainly by processing and storing files and data. Hadoop is known for its big processing power, which makes it easy to run a host of tasks alongside. It helps companies access, save and analyze enormous amounts of data.

    IoT: An acronym that stands for internet of things. Principally, it defines an ecosystem of things, from diapers to self-driving cars, that can communicate with each other via the internet. Their sensors generate a large amount of data that can be analyzed.

    Machine Learning: A highly casual way performing data analysis. Machine learning mechanizes logical model building and trusts on the ability of the device to adapt. With the use of algorithms, models dynamically learn and improve themselves every time they process any new data. Machine learning is not new; however, it is receiving massive attraction as a modern tool for data analysis. It allows devices to grow and acclimatize without demanding numerous hours of extra work by the scientists.

    MapReduce: A model for programming, generating and processing massive data sets. It does two different things: the Map, which includes rotating one dataset to the other, more valuable and fragmented dataset made of bits known as tuples; and the Reduce, which takes all of these fragmented tuples and breaks them even further. It results in a useful breakdown of information.

    NoSQL: Database management systems that do not use relational tables used in most old-style database systems. The data retrieval and storage system is designed for managing massive volumes of data without tabular categorization.

    SaaS: An acronym for software as a service, a method of application delivery in which vendors host applications and make them available through the internet. SaaS providers deliver their services via the cloud.

    Spark: An open-source computing structure developed at the University of California, Berkeley, and donated to the Apache Foundation. It is used for interactive analytics and machine learning.