Since there is such an extreme amount of data available in a Big Data platform, it is important to understand the situational relevance of certain types of data needed to drive business value. Understanding and managing the semantics of the data is centrally important for optimizing its relevance.
Many organizations approach Master Data Management (MDM) by taking an inventory of the data in their source systems, defining policies to improve the quality and usefulness of that data, and building a consolidated hub of that data. This “build it and they will come” approach too often results in an MDM hub that fails to meet the needs of the processes that want to consume that data.
We have just released the 2014 version of our Big Data Ecosystem, our industry-leading reference architecture that categorizes Big Data vendors, tools, and technologies to help you determine the Big Data solution that is right for you.
This year’s Ecosystem features a new Data Security category, which we added in response to increased questions about options available to secure and protect data. Sub-categories also have been added to the Hadoop and NoSQL Data Management categories for faster and more accurate differentiation.
The four current biggest trends in IT are Social, Mobile, Cloud, and Big Data. In this post, I will discuss the intersection of Mobile and Big Data. Without going into too much detail, I’ll first review what Big Data is. The first aspect of Big Data is gathering and storing extreme volumes (often in petabytes) of data from databases, log files, social media, connected devices, external data marketplaces and internal systems. Data is “deep,” meaning it is transactional in nature and nothing is thrown away. Continue reading How Mobile Enables Big Data
2013 was arguably a banner year for Big Data, but the reality was that, beneath the hype, many organizations struggled to understand the value of Big Data. Now that 2014 is underway, more organizations are learning how to realize the potential of Big Data. Continue reading Four Big Data Trends to Watch in 2014
A comprehensive Data Governance strategy is the best way for an organization to ensure that its data is accessible, accurate, and secure. However, even the best Data Governance implementations can be derailed by misunderstandings and lack of buy-in. Continue reading Five Indispensable Best Practices for Communicating Your Data Governance Strategy
A number of people I have spoken to have a hard time differentiating between various solutions for traditional data warehousing and the upcoming big data solutions. At Knowledgent, we have a very simple way of looking at them.
More organizations are turning to Hadoop for their Big Data analytics needs, specifically storing and processing large datasets. Hadoop and its collection of components – HDFS, MapReduce, Pig, Hive, Zookeeper, etc. – provide a platform to store a large quantity of data and batch process this data for analysis. Continue reading Top 10 questions for choosing the best SQL-on-Hadoop solution