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Readers' Choice Awards 2016

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Trends and Applications

The rapid expansion of The IT market shows no signs of abating. The growth of data in all its forms—from traditional data sources and newer sources such as social media and connected devices—is driving swift innovation. To address the need to secure, integrate, and draw meaningful insights from all this data, a steady flow of products and services, as well as new features to long-established offerings, continues to emerge.

Today, data is being recognized and appreciated as an asset, and even, some have suggested, a kind of currency. But beyond the obvious businesses built on data - such as Airbnb's rental business, Uber's car service app, and Alibaba's online marketplace - every business today is striving to become a data-driven organization, with turn-on-a-dime agility and rapid insights into customer behaviors and desires.

The rise of new data types over the past 10 years has led to new ways of thinking about data, and new data storage and management technologies such as Hadoop, NewSQL, and NoSQL. However, despite all the new technologies that have emerged in the last 10 years, one thing is clear: the relational database management system which has been the enterprise workhorse for decades will remain a critical component of the data architecture.

Cloud solutions and services continue to grow in acceptance for many reasons - the ease of deployment and upgrades, elastic scalability, and the pay as you go simplicity. Cloud database options offer an approach that allows organizations to run a database on a cloud platform, either running the DB on the cloud independently or selecting a database service that is maintained by a public cloud service provider.

While relational database technology is still the undisputed leader when it comes to enterprise data management, it is also becoming increasingly apparent that it is no longer the only game in town. By now it is clear that Not Only SQL or NoSQL technology represents a key piece of a data management picture that is increasingly diverse.

Over the years, MultiValue technology has maintained its base of committed advocates despite the decades-long trend toward relational database management systems. And, now with an expanding appreciation for polyglot persistence, or put more simply, the selection of the best tool for the job, there is a growing recognition that different data management systems offer different benefits with some simply better suited for certain requirements than others.

There is more data available to organizations than ever before, but the goal remains the same - to unlock nuggets of gold, the useful information that will result in competitive advantage for the organization, allowing it to react to customer's needs with lightning speed, uncover new opportunities, and act fast to counter competitive threats.

If information is the lifeblood of organizations today, then delivering information where it is needed faster can be considered a matter of business health, and in some cases, even business survival.

In 2016, Hadoop marked its 10th anniversary and now represents much more than a platform for the storage and batch processing of vast quantities of data from disparate sources in many formats. The Apache Hadoop framework, consisting of Hadoop Common, Hadoop Distributed File System (HDFS); Hadoop YARN, and Hadoop MapReduce, remains central to most big data projects and to the creation of data lakes, but Hadoop has also expanded to represent a large ecosystem of more than 100 interconnected open source Hadoop-related projects.

With the growing appreciation of data as a valuable resource and the pressure on organizations to become data-driven, the role of database administration has become more critical than ever, and database administrators are increasingly appreciated for the critical role they play in delivering value to the organizations. Key concerns as far as database administration are availability, security, and integration across a variety of data types and storage mechanism so that analysts and business users are empowered to be able uncover key insights when they need it. It's a lot to think about, and that is why strong database administration solutions are highly valued.

Database development is growing more challenging all the time. Releases are expected to come out faster than ever, and teams are more spread out across, global geographies, time zones, and skill levels. Software deployment, meanwhile needs to be spread across cloud and onsite, and accessible through more devices than ever. What's needed is an integrated end-to-end environment with multi-platform support that helps simplify development and provides automation to achieve repeatable processes and avoid potential risks that can translate into unanticipated delays.

Unplanned downtime, never desirable in the past, today represents more than just an inconvenience. It packs a punch - with the potential to land a serious blow to a business, with potentially lethal effects. Today, there are database performance solutions that with lightning speed can help improve performance by identifying performance problems that affect user response times, correlating response times with additional metrics, and helping data professionals visualize trends for analysis of database performance over specified periods of time.

For fast-paced, turn-on-a-dime digital enterprises, with demands for 24-by-7 uptime, no activity is more vital than keeping database systems up and running. Today, database availability is no longer just a critical IT issue; it is a critical business issue. To be prepared in the event of system failures, infrastructure owners and DBAs have developed strategies to increase resiliency and assure availability of data.

Data is increasingly appreciated by companies as their most valuable asset. But the problem is that this view is not just held by organizations themselves, there are others - including hackers and - who see it that way as well. IT and data managers can play a pivotal role in enterprise security because they are the insiders with trusted status and they are aware of where the data is stored and how best to reduce or eliminate threats. Newer security technology can also relieve many of the manual burdens associated with database monitoring.

Modern data modeling products offer a range of important capabilities such as ways to compare models and then synchronize them, reverse- and forward-engineer systems, visual interfaces for improved ease of use, ways for data assets to be visually documented and reused, and native support for new and emerging database platforms. Many data modeling solutions also provide features to help users map and understand their information architecture to prepare for technology upgrades and process improvements, as well as understand the impact of changes before they are implemented for better appreciation of risks and challenges.

The last thing companies want is tainted data merging with incorrect information. The process of maintaining data integrity enhances the reliability of information for use by a business. This is where tools to ensure data quality come in.

Effective data governance improves the usability, integrity, and security of enterprise data. As organizations gather, store, and access increasing volumes of data, strong data governance allows them to have confidence in the quality of that data for a variety of tasks as well adhere to security and privacy standards.

Data integration is critical to many organizational initiatives such as business intelligence, sales and marketing, customer service, R&D, and engineering. For many enterprises, however, the road to data-driven nirvana is stymied by the inflexible, calcified systems and processes that were laid out decades earlier and still control the data flow within many enterprises, according to Joe McKendrick, lead research analyst at Unisphere Research.

Data replication advances a number of enterprise goals, supporting scenarios such as distribution of information as part of business intelligence and reporting initiatives, facilitating high availability and disaster recovery, and as part of a no-downtime migration initiative.

A key component to data integration best practices, change data capture (CDC) is based on the identification, capture, and delivery of the changes made to enterprise data sources. CDC helps minimize access to both source and target systems as well as supports the ability to keep a record of changes for compliance, and is a key component of many data processes.

Data virtualization provides organizations with the ability to allow the business and IT sides of organizations to work closer together in a much more agile fashion, and helps to reduce the complexity.

Many enterprises are finding themselves with different options when it comes to moving, working, and storing data. While one tool may be right for one organization, another or a combination of tools may be just what the doctor ordered.

To evaluate data quickly and enable predictive analytics based on sources of rapidly changing data - including social media, sensors, and financial services data -- streaming data solutions come to rescue, providing the data when it is needed ... now.

Business intelligence encompasses a variety of tools that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop, and run queries against the data, and create reports, dashboards and data visualizations.

As data grows, organizations are looking for ways to dig up insights from underneath layers of information. Data mining solutions provide the tools that enable them to view those hidden gems and facilitate better understanding of new business opportunities, competitive situations, and complex challenges.

Query and reporting solutions are part of a comprehensive business intelligence approach in every organization. As long as enterprises need to gather data, BI groups look to utilize query and report programs as primary applications that produce output from information systems

The cloud continues to transform computing, making it less expensive, easier and faster to create, deploy, and run applications as well as store enormous quantities of data.

Efficient, secure storage plays a critical role in the enterprise. Tech companies are offering smarter storage solutions, enabling greater efficiency through data compression, information lifecycle management, and tiered storage strategies.

Everyone knows the three Vs, volume, velocity, and variety, of big data but what's required is a solution that can extract valuable insights from new sources such as social networks email, sensors, connected devices as sensors, the web, and smartphones.


Actions

Our goal at Amazon Web Services (AWS) is to enable our customers to do things that were previously not possible, and make things that our customers can already do simpler and better at a much lower cost.

To manage growing data volumes and pressing SLAs, many companies are leveraging Apache™ Kafka and award-winning Attunity Replicate with next-generation change data capture (CDC) for streaming data ingest and processing.

It is both exciting and validating to be selected as the number 1 data modeling solution by DBTA's discerning readers for the third consecutive year in a row!

The MapR team would like to thank the DBTA readers for the "Best Streaming Data Solution" award and their continued support. There is an exciting paradigm shift happening in terms of how we process event data in real time to better react to business opportunities or risks.

Navicat is all about innovation. We understand what time means to our customers — the need to leverage time in order to stay competitive in today's business world.

The potential of your business intelligence or data processing application is limited without a comprehensive data connectivity solution. Staying competitive and relevant requires a breadth of data connectivity options.

Applications are heart of all critical business functions and an essential component to nearly everyone's job, affecting productivity, job satisfaction and ultimately revenue.

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