Ideally a portfolio of projects will support an organization’s strategic plan and the goals or missions the organization is charged with pursuing. We may also need to “get tactical” by delivering value to the customer or client as quickly as possible, perhaps by focusing on better-controlled and better-understood product centric data early on via a “data lake” approach. Doing so will be good for the customer and will help create a relationship of trust moving forward. Such a relationship will be needed when complications or uncertainties arise and need to be dealt with. In organizations that are not historically “data centric” or in organizations where management and staff have a low level of data literacy, an early demonstration of value from data analysis is especially important.
The drive toward cloud and the drive toward hybrid environments. If you look back, containers are not surprising because the need for portability became very critical. We ended up with a truly hybrid environment. Along with that, you see this movement towards an API Economy, a movement towards microservices, the movement of DevOps, of rapid transmission, of rapid delivery of small batches of changes–all those made containers very attractive. To me, not only is this something we’ll look back on and say 2015 is where the traction began, but it’s going to gain even more traction and transformation in 2016.
A data warehouse platform is typically based on a relational DBMS, and the data in it is structured and generally originates from an organization's operational and transactional systems. Data warehouses are accessed by business executives and analysts using BI dashboards, OLAP and reporting tools, and ad hoc SQL queries. Big data analytics, on the other hand, is typically supported by nonrelational technologies such as Hadoop, Spark and NoSQL DBMSes. The data can be both structured and unstructured, and can originate from every type of internal system plus external data sources, such as social media. Analytics are performed on big data for discovery and insight
Apache Ranger offers a federated authorization model for HDFS. Ranger plugin for HDFS checks for Ranger policies and if a policy exists, access is granted to user. If a policy doesn’t exist in Ranger, then Ranger would default to native permissions model in HDFS (POSIX or HDFS ACL). This federated model is applicable for HDFS and Yarn service in Ranger. ... The federated authorization model enables customers to safely implement Ranger in an existing cluster without affecting jobs which rely on POSIX permissions. We recommend to enable this option as the default model for all deployments. Ranger’s user interface makes it easy for administrators to find the permission (Ranger policy or native HDFS)that provides access to the user.
Data is flowing over the unsecured public data highway, so security is critical, particularly as more workers switch to remote and mobile work. Infrastructure and applications are exposed to the outside world. At this point in the cloud evolution, most new cloud 2.0 applications are architected specifically for the cloud. This means that the performance and response time is higher than the first generation of cloud applications, which were just old client/server applications retrofitted with web interfaces. Around 2013, Moore’s Law started to run into the constraints of the laws of physics. Approaching very small size, transistors are less reliable. Consumers of computing power have enjoyed riding the wave of inexpensive computing power in increasingly smaller devices.
Best practices mean different things to different people and organizations. This series of articles will focus on the major best practices applicable across all types of data centers, including enterprise, colocation, and internet facilities. We will review codes, design standards, and operational standards. We will discuss best practices with respect to facility conceptual design, space planning, building construction, and physical security, as well as mechanical, electrical, plumbing, and fire protection. Facility operations, maintenance, and procedures will be the final topics for the series. Following appropriate codes and standards would seem to be an obvious direction when designing new or upgrading an existing data center.
IBM's adoption of Design Thinking is important for the company's sheer market heft, but there is another reason, said Coleman. "IBM exists in the gap between the reality of a situation ('We've always succeeded this way.') and what could be," he said. "Design Thinkers say, 'Sure, that's great, but I have a vision," Coleman said. "Most people fear [a vision] because there's risk involved." He said that if Design Thinking succeeds at IBM, a company that for decades has typified how big business in the US works, large numbers of companies will likely follow suit. Indeed, IBM's Cutler said the company gives tours of its studio in Austin three times a week. Pushback still happens among employees and customers, of course. "Anytime something smells like a new process," Cutler admitted, some people are going to get defensive.
This year, venture capitalists and industry observers say the tech world should expect more of the same. "Most hot startups in 2016 won't be trying to lead revolutions or usher in whole new industries," says Igor Shoifot, an investment partner with TMT Investments. "Instead, they'll be enhancing existing technologies, products, services, or transactional ecosystems by saving users time, money, effort, and helping them make better choices more easily." However, the New Year has a few potential technology surprises in store, including the "Uberization" of manufacturing and mobile ecommerce in emerging markets. Here are 10 of the hottest technology startup categories, trends and opportunities (ranked in no particular order) experts expect to see in 2016.
For Thomas, containers represent a way to get the company’s developers and infrastructure workers to focus on the highest-value work. Too much time is spent on managing middleware systems and messaging buses that don’t add value for the bank. “Simplifying that and really flipping ratios of people who are just maintaining, supporting, managing applications, to people who are pushing the applications forward and bringing more value for our customers is the foundation of the goal,” he says. “It’s not about cost reduction, it’s about reinvesting the people and the talent we have to really business value added things for our customers.” Simplification means consolidation too. Thomas says Bank of America has condensed from 64 data centers last year to 31 this year. It plans to have only eight data centers by the end of 2016.
The primary board concern is that for certain potentially controversial initiatives, some gatekeepers may become “gun-shy;” i.e., may engage in self-protective conduct that frustrates valid board strategic initiatives and other appropriate efforts. This, despite the fiduciary or employment risks a gatekeeper may assume by acting in what may be perceived as his/her own interests, as opposed to the legitimate business interests of the company. Note that this is a concern separate and distinct from the concern, expressed by some knowledgeable observers, that the new DOJ policy will have a chilling effect on employees’ willingness to cooperate in their companies’ internal investigations. We’re talking here about a different kind of “chill.” Such self-protective conduct may manifest itself in both obvious and subtle ways
Quote for the day:
"The sharpest criticism often goes hand in hand with the deepest idealism and love of country." -- Robert F. Kennedy