Readers' Choice Awards 2019

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Trends and Applications

The ability to quickly act on information to solve problems or create value has long been the goal of many businesses. But with the combination of greater volume, variety, and velocity, the situation is becoming more vexing. Fortunately, there are business intelligence solutions today with capabilities to support strong data strategies.

To leverage the immense power of their data, organizations need a solid strategy that incorporates everything from security to data governance to the right big data technologies. Enabling both on-prem and cloud deployments—or a hybrid strategy—big data platforms today support data warehouses, data lakes, data science, engineering, machine learning, myriad database management systems, and much more.

Speed to insight matters more today than ever before. But dealing with the increasing volume of data, and the speed at which this data changes, can be a hindrance to data integration, timely analytics, and rapid decision making. Change data capture supports real-time analytics with less overhead to advance a range of initiatives such as data warehousing, real-time dashboards, data quality, and more.

DBAs and their organizations are moving to the cloud. A recent survey conducted by Unisphere Research, a division of Information Today, Inc., in partnership with Amazon Web Services, found that, on average, 25% of organizations' critical enterprise data is now managed in public clouds. The survey also found that for 60% of data managers the use of public cloud-based data resources and platforms has increased over the past year.

Cloud is now mainstream, a critical part of data environments,and this trend is only increasing. Gartner estimates that $206 billion will spent on public cloud services in 2019, up 17% from 2018, while IDC estimates that nearly half of IT spending was cloud-based in 2018, "reaching 60% of all IT infrastructure and 60%-70% of all software, services and technology spending by 2020."  

Cognitive computing pillars that are shared with AI include the ability to learn and be adaptive, be probabilistic, and use big data from diverse sources. Characteristics that are specific to cognitive computing include being meaning-based, interactive, contextual, iterative and stateful, and highly integrated.

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. Data-driven innovation is being embraced by almost every department in the enterprise, led by outward-facing departments, with line-of-business owners and marketing departments demanding the most innovation from data.

The pressure is on. Today, organizations are under tremendous pressure from business partners, consumers, and regulators to exercise care about how they handle sensitive data of all kinds. Data governance is becoming more challenging due to a confluence of factors, including data volumes exploding, data being collected from more disparate sources, and data no longer being stored centrally, but instead in a combination of on-premise, cloud, and hybrid scenarios.

In the era of big data, volume, variety, and velocity are often mentioned as critical issues. However, at Data Summit 2019, data integration was identified as one of the most critical and disruptive problems facing organizations today.

Data modeling tools can help organizations create high quality data models that enable them to shape, organize, and standardize data infrastructure, change structures, and produce detailed documentation. Moreover, data modeling solutions can also help companies visualize and manage business data across platforms and extend that data to users with varying job roles and skill levels across geographies and time zones.

The old adage, garbage in, garbage out, has never been truer. Not only is the problem not going away with the advance of technology and the growth of data volumes, velocities, and varieties—but it is getting worse.

Today, digitally savvy enterprises cannot afford downtime and, for this reason, many companies are developing strategies that ensure that the data they need—or their customers are viewing—will always be available, regardless of what happens behind the scenes. Data replication is considered a critical solution that supports infrastructure and data services.  Data replication is used to keep essential data available to users and customers.

Increasingly stringent data privacy regulations along with a generally lower tolerance for data mishandling, are making companies even more concerned about improving their data security postures and thwarting cyber risk.

According to an IDC report, the Global Datasphere will grow from 33 Zettabytes (ZB) in 2018 to 175ZB by 2025. "To keep up with the storage demands stemming from all this data creation, IDC forecasts that over 22ZB of storage capacity must ship across all media types from 2018 to 2025."

With data virtualizatio the business and IT sides of organizations can work closer together in a much more agile fashion, reducing complexity and boosting productivity. Data virtualization enables artificial intelligence/machine learning and data science initiatives by delivering all the available data to algorithms in real time, said Ravi Shankar, chief marketing officer, Denodo.

Data visualization tools have evolved beyond the standard charts and graphs used in Excel spreadsheets, displaying data in more sophisticated ways such as infographics, dials and gauges, geographic maps, sparklines, heat maps, and detailed bar, pie and fever charts. The images may include interactive capabilities, enabling users to manipulate them or drill into the data for querying and analysis.

With long downtimes simply unacceptable today, organizations seek solutions with capabilities such as the ability to manage backups seamlessly, manage and monitor backups, ensure data integrity, scale efficiently, restore quickly to any point in time, and provide security features to stay in compliance with local geographic and industry mandates.

For database development teams, maximizing competence, performance, adaptability, and readiness will help simplify development and allow automation to achieve repeatable processes, all while avoiding potential risks that create downtime. Companies want to generate queries and reports, perform SQL development and optimization, detect and diagnose database problems, automate administration tasks, and more

Social media, the Internet of Things, demands for mobile access, and real-time insights are just some of the factors that have increased the pressure on organizations to change how data is managed. And as a result there have never been so many data management choices to deal with it all.

With enterprises juggling more than ever?—from massive data volumes and multiple database platforms, to DevOps and the cloud?—database monitoring is more important than ever. Companies can't afford any database downtime.

Today's database administration solutions help to improve DBA productivity while simplifying repetitive administrative tasks, helping to locate and alleviate performance bottlenecks, and optimizing code.

After more than 15 years, there is still probably no technology more aligned with advent of big data than Hadoop. The Apache Hadoop framework allows for the distributed processing of large datasets across compute clusters, enabling scale up from single commodity servers to thousands of machines for local computing and storage. Designed to detect and handle failures at the application layer, the framework supports high availability.

In-memory databases and technologies enable decision makers to get to the information they are seeking rapidly and more readily. While in-memory technology has been on the market for many years, today, the demand for intelligent, interactive experiences requires back-end systems and applications operating at high performance, and incorporating movement and delivery of data faster than ever before.

The Internet of things (IoT) is the inter-networking of physical devices, vehicles (also referred to as "connected devices" and "smart devices"), buildings, and other items embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to collect and exchange data.

Highly customized, mission-critical applications have been built on MultiValue database technology, which is sometimes called the fifth NoSQL database technology, for many years now. The MultiValue database dates back to the mid-1960s, with Don Nelson and Dick Pick widely credited as the founding fathers of the technology. Also referred to as Pick or MultiDimensional, a key advantage of MultiValue, is the database structure's use of attributes that can have multiple values, rather than one single value as with relational technology.

Look to NoSQL for fast, highly scalable access to free-form data. This comes with a few costs, like consistency of reads and other safeguards common to SQL databases. But for many applications, those safeguards may well be worth trading for what NoSQL offers.

A relational database is a set of formally described tables from which data can be accessed or reassembled in many different ways without having to reorganize the database tables. The standard user and application programming interface (API) of a relational database is the Structured Query Language (SQL). SQL statements are used both for interactive queries for information from a relational database and for gathering data for reports.

Streaming platforms allow individuals to see data in real-time batches, enabling businesses to analyze data in motion, simplify the development of applications, and extend the value of existing systems by integrating with already implemented applications along with supporting both structured and unstructured data.

Each year, Database Trends and Applications presents the Readers' Choice Awards, providing a unique opportunity to recognize companies whose products are selected by experts whose opinions carry more weight than any others—you, the readers. Here we present the top three vote-getters in each category. Congratulations to all and thanks to everyone who submitted nominations and voted!