Big Data

  • Big Data and NoSQL: Characteristics and Architectural Trade-Offs

    NoSQL system and Big DataBig data is often characterized by the three Vs—volume, variety, and velocity. Volume obviously refers to the terabytes and petabytes of data that need to be processed, often in unstructured or semi-structured form. In a relational database system, each row in a table has the same structure (same number of columns, with a well-defined data type for each column and so on). By contrast, each individual entity (row) in an unstructured or semi-structured system can be structurally very different and therefore, contains more, less, or different information from another entity in the same repository. This variety is a fundamental aspect of big data and can pose interesting management and processing challenges, which NoSQL systems can address. Yet another aspect of big data is the velocity at which the data is generated. For data capture scenarios, NoSQL systems need to be able to ingest data at very high throughput rates (for example, hundreds of thousands to millions of entities per second). Similarly, results often need to be delivered at very high throughput as well as very low latency (milliseconds to a few seconds per recipient).

  • Big Data Use Cases and NoSQL databases

    Big Data & NoSQL use casesThe initial use of NoSQL technology began with the social media sites as they were looking at ways to deal with large sets of data generated by their user communities. For example, in 2010 Twitter saw data arriving at the rates of 12TB/day, and that resulted in a 4PB dataset in a year. These numbers have grown significantly as Twitter usage has expanded globally.

  • Data Science and Big Data

    Data Science and Big DataData science is a response to the difficulties of working with big data and other data analysis challenges we collectively face today. We examined this briefly in the introduction, but that was just scratching the surface. In fact, there is so much literature on big data that this whole chapter will still not be able to do it justice. It will, however, give you a good idea of its importance in today’s world. Furthermore, it will help you understand what all the hype is about big data (a hype that has increased significantly over the past year), and why data science is so important.

    Big data is a fundamental asset for today’s businesses, and it is not a coincidence that the majority of businesses today are using, or are in the process of adopting, the corresponding technology. Despite all the hype about it in various media, this is not a fad. There are specific advantages to using this asset, and the fact that it is growing more abundant is an indication that it is imperative to do something about it, and do it fast! Perhaps it is not useful for certain industries right now as big data tends to be quite chaotic or even non-existent for them. Those who do have it and make intelligent use of it, though, reap its benefits and stand a good chance of being more successful in today’s competitive economic ecosystems.

  • Developing Analytical Capabilities with Oracle on examples

    The IT industry has seen many evolutions, and it is in the midst of another major paradigm shift. Few technologies have captured more attention than big data, and there is tremendous interest in business use cases featuring big data and analytics. Gartner highlighted the top ten technologies and trends that will be strategic for most organizations in 2018. Strategic big data and actionable analytics were among these ten trends. In 2019, Gartner released its top ten IT trends again. This time, the list included mobile, Internet of Things, and smart machines. Big data and analytics become enablers - a hidden force that’s behind the scenes driving these businesses and IT innovations.

  • Importance of Data Science

    Importance of Data ScienceIn the previous blog note, we got a glimpse of how data science came about and how it is related to big data. We also looked into the major milestones of this field and why it has become popular in recent years. However, this was just scraping the surface, since data science has much to offer on many more levels. In order to get a better understanding, we will look into its history, the new paradigms it entails and the new mindset it brings about as well as the changes it brings.

  • Key Big Data analytical use cases in a variety of industries

    Big Data analytical use cases in industryMcKinsey Global Institute published a report on big data in five industries, including healthcare, public sector, retail, manufacturing, and personal-location data. It found that big data generated tremendous value in each of these domains. Many believe that the innovative use of big data is crucial for leading companies to outperform their peers. In this blog, we’ll describe key big data analytical use cases in a variety of industries.

  • Oracle Engineered Systems for Big Data

    Oracle Engineered Systems for Big DataOver the last few years, Oracle has been focused on purpose-built systems that are engineered to have hardware and software work together, and are designed to deliver extreme performance and high availability, while at the same time making them easy to install, configure, and maintain. The Oracle engineered systems that assist with big data processing through its various phases are the Oracle Big Data Appliance, Oracle Exadata Database Machine, and Oracle Exalytics In-Memory Machine. Figure 1 shows the best practice architecture of processing big data using Oracle engineered systems. As the figure depicts, each appliance plays a special role in the overall processing of big data by participating in the acquisition, organization, and analysis phases.

  • Oracle’s Approach to Big Data

    Oracle’s Approach to Big Data solutionsThe amount of data being generated is on the verge of an explosion, and according to an International Data Corporation (IDC) 2012 report, the total amount of data stored by corporations globally would surpass a zettabyte (1 zettabyte = 1 billion terabytes) by the end of 2012. Therefore, it is critical for the data companies to be prepared with an infrastructure that can store and analyze extremely large datasets, and be able to generate actionable intelligence that in turn can drive business decisions. Oracle offers a broad portfolio of products to help enterprises acquire, manage, and integrate big data with existing corporate data, and perform rich and intelligent analytics.