June 2019

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Trends and Applications

With deep learning so much praised, you've probably read a lot about its positive side. Let's skip the positives and instead concentrate on the things that can go wrong with deep learning-based demand forecasts as well as offer some ways to overcome these problems. 

Over the past several years, we have seen a tremendous amount of pressure from consumers (and now regulators) regarding how organizations manage content, especially when that content contains information about employees, consumers, and any number of other individuals whose data has made its way into enterprise data stores. Although the enactment of the EU's General Data Protection Regulation (GDPR) in May 2018 seemed to monopolize public discussion, we have seen a multitude of other newsworthy moments since then.

An organizational shift is necessary to adapt to constantly changing regulatory requirements and satisfy new ethical concerns. Google's stiff fine for non-compliance with the EU's General Data Protection Regulation (GDPR) has demonstrated the potential impact on a company's bottom line in a way that has made GDPR a topic of discussion in many corporate boardrooms. Yet, a recent study by IT Governance revealed that only 29% of European-based organizations are GDPR-compliant.

As the volume of digital information being produced across industries grows at record rates, databases are becoming more integral to organizations than ever before. These data stores contain the lifeblood of an organization and the sensitive information within them must be protected from improper access and breaches, which continue to rise in frequency. In 2017, there were more than 1,500 data breaches reported in the U.S. alone—nearly a 45% increase year-over-year—and according to the Ponemon Institute, the average cost of a data breach rose to $3.86 million.

AI, machine learning, and predictive analytics are used synonymously by even the most data-intensive organizations, but there are subtle, yet important, differences between them. Machine learning is a type of AI that enables machines to process data and learn on their own, without constant human supervision. Predictive analytics uses collected data to predict future outcomes based on historical data.

While change has always been a part of the database credo, the growing emphasis on data-driven decision making in today's economy has resulted in a dizzying plethora of technologies and methodologies entering the market. The number and scope of game-changing technologies are too numerous to mention, and one thing is certain: Database management will never be the same. We have identified some of the most promising technology initiatives, based on discussions with and input from data experts from across the industry spectrum, gathering their views on the key technologies—well-known or under the radar—that are worth watching.

Web data is the world's largest untapped source of data. And now, web data in large volumes can be captured quickly, efficiently and cost-effectively with the introduction of web data integration, a practice that identifies, extracts, prepares, and integrates data for consumption by business applications, analytics, and processes.

Quantum computing will bring unprecedented advances in medicine, science, and mathematics—knowledge currently out of reach. Many secrets of the universe are on the verge of discovery. But, are we ready for everything to be unlocked? Are we prepared to manage what comes with quantum computing's limitless architecture?

IT executives and their business counterparts understand the importance of a strong data strategy and its value to their businesses, and most are starting to get the key pieces in place to drive transformation through next-generation technologies and processes. One in four enterprises now regards real-time data as critical to their ongoing operations—and another one in four is actively preparing to introduce real-time data capabilities into their infrastructures, according to a new Unisphere Research survey.

Columns - Database Elaborations

At times, there is a need to have security within the database be a bit more sophisticated than what is available.  On specific tables, there may be a need to limit access to a subset of rows, or a subset of columns to specific users. Yes indeed, views have always existed, and yes indeed, views can be established limiting rows or columns displayed. However, views only can go so far.

Columns - DBA Corner

What are the practices and procedures that you have found to be most helpful to automate in administering your databases? Yes, I know that automation has been a standard claim for most DBMS vendors, as well as third-party DBA tool vendors, for many years. But are you really anywhere closer to an "on demand," "lights-out," "24/7" database environment yet?

Columns - Quest IOUG Database & Technology Insights

Managing Oracle databases in the cloud may require some adjustments to the way you are used to doing things, so there is likely a learning curve when adding the additional tasks discussed in this article. In the end, database services in OCI are engineered to save time and avoid problems, among other things, so it is definitely worth the investment.

Columns - SQL Server Drill Down

Microsoft directly hosts a handful of big conferences every year, with the developer-oriented Build conference and infrastructure-oriented Ignite being to two that I pay the most attention to. The Microsoft Build 2019 conference just wrapped up and we have a couple important new announcements for the those of us working with the Microsoft Data Platform.

Columns - Emerging Technologies

The blockchain technology market is generally believed to be about $2 billion in 2019 and growing at an annual rate in excess of 50%—with projections for the market to exceed $10 billion by the end of 2025. Almost all of that new spending will be cloud-oriented; very few organizations consider running their own blockchain hardware. Therefore, it's not surprising to see cloud vendors actively promoting blockchain solutions.