Big Data Quarterly Articles



When people talk about the next generation of applications or infrastructure, what is often echoed throughout the industry is the cloud. On the application side, the concept of "serverless" is becoming less of a pipe dream and more of a reality. The infrastructure side has already proven that it is possible to deliver the ability to pay for compute on an hourly or more granular basis.

Posted May 15, 2017

You will often hear experienced practitioners and consultants suggest that there is both an art and a science to effective data governance. The art is in the details of fine-tuning a data governance program to fit your culture and address specific business needs. But the fundamental principles of data governance are best understood and executed through science.

Posted May 15, 2017

The tide is changing for analytics architectures. Traditional approaches, from the data warehouse to the data lake, implicitly assume that all relevant data can be stored in a single, centralized repository. But this approach is slow and expensive, and sometimes not even feasible, because some data sources are too big to be replicated, and data is often too distributed to make a "full centralization" strategy successful.

Posted May 15, 2017

Over the next 6 years, the Internet of Things (IoT) market is expected to reach $883.55 billion, as connected devices continue to pour into just about every aspect of our lives. For enterprises, the IoT is helping to transform products into connected services, capable of creating recurring revenue streams, reducing costs, and enhancing customers' experiences.

Posted May 15, 2017

Data—now universally understood to be the lifeblood of businesses—is at risk like never before in the form of both malicious attacks and innocent indiscretions. Recently, Steve Grobman, CTO for McAfee, discussed the range of threats to data security and what companies must do to defend themselves.

Posted May 15, 2017

Organizations are embracing data visualization as more than a tool to "see" trends and patterns in data but as a pathway to a dynamic culture of visual data discovery. As with any type of cultural shift, there are going to be a few bumps along the road as innovative ways to transform data into actionable insights through the power of data visualization are sought.However, with a few considerations kept top-of-mind in the early stages of data visualization adoption, common problems can be avoided.

Posted May 15, 2017

Big data and analytics are all around these days. Most companies already have their first analytical models in production and are thinking about further boosting their performance. However, far too often, these companies focus on the analytical techniques rather than on the key ingredient: data. The best way to boost the performance and ROI of an analytical model is by investing in new sources of data which can help to further unravel complex customer behavior and improve key analytical insights.

Posted May 15, 2017

Today's headlines are filled with news about artificial intelligence (AI), proclaiming variously that robots will take our jobs, cure cancer, or change industries in ways unseen since the industrial revolution. One thing is clear to those of us watching closely, however: It's not all hype. In 2016 alone, the quantity of AI startup acquisitions was remarkable, but most of these massive investments were made by an elite corps of companies, such as Amazon, Google, Apple, Facebook and a few others.

Posted May 15, 2017

What are the enabling technologies that make enterprise architecture what it is today? There are a range of new-generation technologies and approaches shaping today's data environments. The key is putting them all together to help enterprise architecture fit into the enterprise's vision of itself as a data-driven organization. Tools and technologies emerging within today's data-driven enterprise include cloud, data lakes, real-time analytics, microservices, containers, Spark, Hadoop, and open source trends.

Posted May 15, 2017

Progress, a provider of application development and deployment technologies, recently acquired DataRPM, a privately-held provider of cognitive predictive maintenance software for the industrial IoT (IIoT) market. Mark Troester, vice president of strategy at Progress, discussed how the addition of DataRPM's predictive analytics and meta learning capabilities help to round out the Progress platform, enabling the company to embrace a "cognitive first" strategy.

Posted April 24, 2017

To meet the new demands of managing infrastructure in the cloud in a proactive manner, the new role of the "cloud keeper" has emerged. The cloud keeper is part technologist, part accountant, and part administrator. The cloud keeper has financial responsibility for keeping control of infrastructure expenses to prevent financial chaos. The role is part technical, since it requires an understanding of how and where resources are deployed. The cloud keeper must know how a resource is paid for and have enough technical expertise to know which resources can be spun up or down or would be better suited for one cloud paradigm over another.

Posted April 07, 2017

While companies often view processes from their frame of reference, "cutting" processes up according to department, business objective, or other internal aspect, customers obviously do not act according to the same taxonomy and—from the perspective of the company—appear to jump from process to process, from department to department, and from channel to channel, making it difficult for businesses to truly follow a customer through his or her whole journey.

Posted April 07, 2017

Governing and managing big data are not easy tasks. In fact, let's be honest—data management and governance for data of any size is no walk in the park. But, big data makes it even tougher. From integrating the disparate data sources of seemingly unending variety to curating the chaos in the heaps of unstructured data, managing the craziness we lovingly refer to as big data is not for the faint of heart. Even for those tough as nails, the challenges of big data management can be more than frustrating. So, now that you know you are not alone in dealing with this insanity, here are a few ways to make the frustrations of big data a little less intense.

Posted April 07, 2017

By now we are all in agreement: The business of data is changing. Business users are more empowered to work with data; IT is becoming less about control and more about enablement. New data science job descriptions—such as the data scientist—are springing up as companies everywhere look for the right people with the right skill sets to squeeze more value from their data. Data itself is getting bigger, hardware more economical, and analytical software more "self-service." We've embraced the paradigm shift from traditional BI to iterative data discovery. It's a new era.

Posted April 07, 2017

As the Internet of Things (IoT) revolution works its way through marketing hype and seeks its place of valuable contribution within companies and industries, you might pause to wonder how IoT can create opportunities for your company. Yet that assessment is difficult in part because the buzz does not always align with reality. In short, it's no simple task to discern the true potential of IoT today, leaving one to wonder: What is realistic, what difference could IoT make in my company, and how mature are other companies in embracing IoT potential?

Posted April 07, 2017

The concept of data lakes is a great one, but if not done correctly, this treasure trove of information can quickly turn into a black abyss for data analysts and scientists, let alone business users.

Posted April 07, 2017

Make no mistake: Big data is promising, exciting, and effective—when done right. Once considered an overhyped buzzword, it's now a potential tool that leaders in every vertical want to harness. Unfortunately, the majority of new big data projects—about 55% of them, according to Gartner—are shuttered before they even get off the ground.

Posted April 07, 2017

There has been a sea of change in how enterprises are thinking about Apache Hadoop and big data. Today, a majority of enterprises are thinking about the cloud first, not on-premises, and are increasingly relying on ecosystem standards to drive their Apache Hadoop distribution selection.

Posted April 07, 2017

It is difficult to find someone not talking about or considering using containers to deploy and manage their enterprise applications. A container just looks like another process running on a system; a dedicated CPU and pre-allocated memory aren't required in order to run a container. The simplicity of building, deploying, and managing containers is among the reasons that containers are growing rapidly in popularity.

Posted April 07, 2017

Alation and Trifacta say they are extending their partnership to jointly deliver an integrated solution for self-service data discovery and preparation that enables users to access the data catalog and data wrangling features within a single interface.

Posted March 15, 2017

SAP has announced advancements in the SAP Vora solution to help customers accelerate project implementations and improve their enterprise business analytics.

Posted March 15, 2017

Dataguise, a provider of sensitive data governance, has announced that DgSecure now provides sensitive data monitoring and masking in Apache Hive.

Posted March 15, 2017

The rise of big data and the growing popularity of cloud is a combination that presents valuable new opportunities to leverage data with greater efficiency. But organizations also need to be aware of some key differences between on-premise and cloud deployments, says Charles Zedlewski, senior vice president, products, at Cloudera.

Posted March 15, 2017

Ash Munshi, Pepperdata CEO, recently discussed the need for DevOps for big data, and the role of the Dr. Elephant project, which was open sourced in 2016 by LinkedIn and is available under the Apache v2 License.

Posted March 07, 2017

Tableau Software is releasing an updated version of its namesake platform, bringing advanced mapping capabilities to the analytics solution. Tableau 10.2 will make complex geospatial analysis easier, simplify data prep with new ways to combine and clean data, and give enterprises more tools to deliver self-service analytics at scale, according to the company.

Posted March 03, 2017

Kong Yang, head geek at SolarWinds, believes the rise of the mobile workforce and the pressure to implement new technologies means that modern IT professionals must be able to quickly evolve beyond the confines of on-premises and shift into the realm of hybrid IT. Here, Yang reflects on some of the ways that IT professionals can begin that journey.

Posted February 24, 2017

MapR Technologies, Inc., which provides a converged data platform, has introduced persistent storage for containers with complete state access to files, database tables, and message streams from any location. The MapR Converged Data Platform for Docker includes the MapR Persistent Client Container (PACC) that enables stateful applications and microservices to access data for greater application agility and faster time-to-value.

Posted February 07, 2017

As news of data breaches continues to grab headlines, data security is becoming a greater enterprise concern. However, at the same time, it is becoming clear that many organizations are actually doing things that make their data more vulnerable. Recently, Joe Pasqua, executive vice president of products at MarkLogic, provider of enterprise NoSQL database technology, discussed the ways organizations and their employees are unintentionally putting their data at greater risk—and the ways to address it.

Posted February 03, 2017

With all the talk about "big data" in the last few years, the conversation is now turning to: What can be built on this platform? It isn't just about the analytics—many people talk about data lakes, but in reality, organizations are looking beyond the data lake.

Posted February 03, 2017

As challenges to data security grow more sophisticated, data breaches are also becoming more expensive. A better under- standing of the emerging risks, stronger collaboration within organizations, and the use of remote capabilities combined with automation of routine tasks can help, according to industry research.

Posted January 27, 2017

vArmour has been awarded a patent by the USPTO for security technology for container microservices. Marc Woolward, CTO of vArmour described what's changing in the world of clouds, containers, and microservices.

Posted January 24, 2017

IoT has massive implications for businesses of all kinds, and for individuals at all organizational levels, as well. Bart Schouw, IoT solutions director at Software AG, recently reflected on the changes taking place and explained why 2020 will be a critical year for IoT.

Posted January 20, 2017

Patrick Hubbard, head geek and technical product marketing director at SolarWinds, recently discussed key themes that will emerge on the IT front in 2017. Smarter use of container technologies, a greater emphasis on security, and the continued shifting of IT roles, he says are some of the key trends that will take hold in the year ahead.

Posted January 13, 2017

Arcadia Data, a provider of visual analytics software, has added new native integration features for Arcadia Enterprise and Cloudera Enterprise to deliver a real-time, Hadoop-native analytics platform.

Posted January 11, 2017

Hortonworks has forged an open source collaboration with Neustar, a provider of real-time information services, on security and identity management tools for IoT devices.

Posted January 11, 2017

Xplenty has announced new $4 million in funding from Bain Capital Ventures, True Ventures, and Rembrandt Venture Partners, and with participation in the funding round from existing Xplenty investors Magma Venture Partners and Waarde Capital.

Posted January 04, 2017

The "big data" era is still very much upon us, ushering in an age of constantly evolving technologies and techniques. Many wonder whether the enterprise data warehouse(EDW) still has relevance in the industry, particularly since many new alternatives exceed the technical capabilities of the traditional EDW at a drastically reduced cost.

Posted January 04, 2017

Pitney Bowes has joined Hortonworks Partnerworks in the Modern Data Solutions (MDS) partner program. According to the vendors, location-based data, in particular, is becoming more important in how businesses understand their customers because it is one of the most consistent ways to link people, places, and things.

Posted January 04, 2017

Despite the rise of Hadoop, data analysts still struggle to find and harness all types of data across the enterprise. Organizations are now adopting data lake management innovations to quickly and flexibly ingest, cleanse, master, prepare, govern, secure, and deliver all types of data on-premise or in the cloud.

Posted January 03, 2017

ZeroPoint technology focuses on analyzing documents, email, web content and server traffic for hazardous content such as malicious code

Posted December 13, 2016

When software providers consider transitioning to (or at the very least adding) a SaaS offering, they think about the impact to their business of moving from a perpetual license model to a recurring revenue stream. And while it's easy to remember and consider such migration costs as application-level rearchitecture, other upfront and ongoing costs - such as infrastructure and service-related costs - are often severely underestimated.

Posted December 12, 2016

The many compromises demanded by our current plethora of database technologies make selecting a database system far harder than it ought to be. Whichever DBMS you choose, you are probably going to experience a "not-quite-right" solution.

Posted December 08, 2016

It has become all too clear that no organization is immune from the risk of a data breach, and that anyone accessing data can pose a threat - including trusted employees and partners. Here, IT executives speculate on the impact newer technologies such as IoT, blockchain, and cloud, as well as the need for data protection, including disaster recovery plans, encryption, and comprehensive oversight.

Posted December 07, 2016

Many providers of cloud services market the idea that all critical computing functions should be run using their public cloud services because this paradigm is the future and the future is now. While we do share that long-term vision, the reality is less impressive, and the solution is not yet complete. Amazon itself does not run 100% of its critical business systems in the AWS Public Cloud, a fact that was revealed in The Wall Street Journal article, "Cloud-Computing Kingpins Slow to Adapt to Own Movement." This is also true for Google, Microsoft, and other top cloud providers.

Posted November 15, 2016

The definition of "data visualization" often varies depending on whom you ask. For some, it's a process of visually transforming data for exploration or analysis. For others, it's a tool to share analytical insights or invite discovery.

Posted November 15, 2016

Data as a service (DaaS) is a business-centric service that transforms raw data into meaningful and reusable data assets, and delivers these data assets on-demand via a standard connectivity protocol in a pre-determined, configurable format and frequency for internal and external consumption.

Posted November 04, 2016

New data sources such as sensors, social media, and telematics along with new forms of analytics such as text and graph analysis have necessitated a new data lake design pattern to augment traditional design patterns such as the data warehouse. Unlike the data warehouse - an approach based on structuring and packaging data for the sake of quality, consistency, reuse, ease of use, and performance - the data lake goes in the other direction by storing raw data that lowers data acquisition costs and provides a new form of analytical agility.

Posted November 03, 2016

A new semantic-based graph data model has emerged within the enterprise. This data model has all of the advantages of the relational data model, but goes even further in providing for more intelligence built into the database itself, enabling greater elasticity to absorb the inevitable changes to data requirements, at cloud scales.

Posted November 02, 2016

Data has become a disruptive force for global businesses and a catalyst for digital transformation. But data can only be leveraged for BI initiatives to the extent it can be accessed and trusted. And, while today's self-service BI and analytics tools satisfy a user's craving for more "consumerized" technology, they often leave an analyst stuck in neutral because the users, first and foremost, cannot find the data they need to perform any analysis.

Posted November 02, 2016

Kinetica, provider of an in-memory database accelerated by GPUs (graphics processing units) has introduced two new software and services offerings designed to help customers ingest and use streaming datasets through use of GPUs.

Posted October 31, 2016

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