White Papers, Research Papers, and eBooks

How Confluent Completes Apache Kafka: Modernize your data infrastructure with Confluent

Apache Kafka is the foundation of modern data architectures, but the open-source technology alone doesn’t offer everything enterprises need to reach production quickly and implement data in motion use cases end-to-end. To remedy this, Confluent offers a complete and secure enterprise-grade distribution of Kafka and makes it available everywhere your apps and data reside. View Now


Measuring the Cost-Effectiveness of Confluent Platform

Setting your data in motion with Apache Kafka® is a valuable but costly endeavor for most organizations. Even for small projects, the time and resources required to deploy and manage Kafka yourself can overwhelm your people and budget. Confluent Platform completes Kafka with a set of enterprise-grade features and services to solve this challenge, reducing Kafka’s infrastructure footprint, day-to-day operational burden, and intangible costs stemming from downtime and security risks. View Now


Data Quality Assessment: A Methodology for Success

“The world’s most valuable resource is no long oil, but data,” stated The Economist. Many have noticed the increased value of data – from contact information to buying patterns – and how vital it is to so many aspects of business operations. Data can be used for marketing strategies, business intelligence, customer behavior patterns and so much more. But how can one be sure the data is reliable? Ultimately, bad data can lead to bad business decisions. View Now


The 2021 State of Cloud Data Governance

Organizations can manage and provide access to their data more efficiently when companies have reliable cloud data governance. However, while many organizations have started such programs, achieving the promise of cloud governance remains elusive. DataOps can help; it weaves cloud data governance activities together, supplying critical data effectively, while reinforcing necessary data governance policies and procedures to ensure regulatory and security requirements. View Now


How do Data Catalogs Built as Knowledge Graphs Enable an Enterprise Data Fabric

There is a new approach in the world of data and it’s getting a lot of buzz in the industry — Data Fabric. Gartner identified data fabric as the top trend for data and analytics in 2021. It has been rapidly gaining traction in the enterprise. Nearly 70% of our recent webinar attendees said they were either implementing or researching data fabrics. View Now


Creating a Successful Data Fabric for Your Enterprise

Data Fabric enables easier data management, security, reliability, and consistency. In this paper, Claudia Imhoff, Ph.D, covers what a data fabric is, along with its benefits. The reader will learn about the set of processes needed to give the user a sane and rational way to access the environment, understand and create needed analytical assets, and the ability to quickly make decisions with confidence. View Now


13 Essential Data Validations for Trustworthy Data in the Cloud and Lake

When data moves between different data platforms, like a Data Lake or a Cloud, the IT group and the data consumers all ask the same question: is the data trustworthy? The only way to create trust is to validate every piece of data every time it moves in and out of a repository. But the challenge is that each of the 1,000’s of tables and data files within has uniquely different data validation rules. Making it very hard to identify, create and maintain… View Now


Active Data Governance Methodology

Data governance can deliver high-quality, trusted, and compliant data, which is why data leaders are pursuing governance initiatives. They need an effective means to make it a part of their process. Alation offers a new, fresh approach. Active data governance builds a community of business experts committed to data literacy as a means to enhance their business’ effectiveness. It is based on an iterative process of continuous improvement — in contrast to data governance of old, which typically… View Now


Aerospike’s NoSQL Data Platform Increases Revenue Retention and Improves Customer Experience For Financial Services Organizations

In this modern time of digital transformation, financial services organizations are required to adjust to the challenges they are facing while maintaining their core systems and finding new avenues for growth. Forrester Consulting finds that Aerospike’s high performance NoSQL Data Platform… View Now


Data Platforms in Financial Services The NOSQL Edge

This image has an empty alt attribute; its file name is aerospike_logo_horizontal_hi-res-01-1024x90.png

This white paper explores the criticality of real-time data processing in financial institutions and the common challenges they face. The growth in online transactions and web applications is driving a focus on fraud prevention, customer experience, and a new digital imperative that requires new thinking on how data is stored, managed, and processed… View Now


The 3 Must-Haves for Every Data Catalog

Do these questions sound familiar? What does this data represent? Where is this data coming from? Who is responsible for this data? How can I use this data? Why is it taking me so long to get answers to these questions so I can effectively use the data? If so, you probably already know that a data catalog is what your organization… View Now


Top 10 Considerations for Choosing a Data Modeling Solution

Data modeling is an essential aspect of application modernization. As organizations migrate data and applications to the cloud and other modern forms of infrastructure, they need data modeling solutions that can facilitate the overall modernization lifecycle…

View Now


The Analytics Stack is Consolidating… Now What?

The data analytics stack is consolidating, and a new class of unified data analytics platforms is emerging. But with so much at stake, when’s the right time for CIOs, IT leaders and application owners to consider a new approach? …

View Now


The Essential Guide to Data Lineage in 2022

Data lineage is imperative to every data user in your organization. Now it’s finally time to understand why. Download our latest eBook and you’ll gain a thorough understanding of what data lineage is… View Now


Four Steps to Analytics Governance

Our workbook, “Four Steps to Analytics Governance,” is a checklist to help you understand just how cloud-native data intelligence solutions can uplevel trust… View Now


Data Fabric Architecture Delivers Instant Benefits

Top performing enterprises are data driven. However, several challenges block them from fully exploiting all data; lack of data access, disparate data sources and data types and data integration complexities. With a data fabric, your business users and data scientists can access trusted data faster for their applications, analytics, AI and machine learning models… View Now


Data Virtualization for Dummies – Learn How to Put Data Virtualization to Work in Your Organization

With the advent of big data and the proliferation of multiple information channels, organizations must store, discover, access, and share massive volumes of traditional and new data sources.  View Now


The 7 Lies of Data Catalog Providers

The Data Catalog market has developed rapidly, and it is now deemed essential when deploying a data-driven strategy. Victim of its own success, this market has attracted a number of players from adjacent markets. These players have rejigged their marketing positioning in order to present themselves as Data Catalog solutions… View Now


Trends in Data Management: A 2021 DATAVERSITY Report

Digital transformation and the rise of the data-driven organization continue to drive Data Management across the globe. Increases in remote work and digital commerce, in part due to COVID-19 lockdowns, have only intensified these trends. Data stands at the center of digital transformation… View Now


Trends in Data Management: A 2020 DATAVERSITY Report

DATAVERSITY asked questions through the 2020 Trends in Data Management Survey. This paper details and analyzes the survey’s latest thoughts, trends, and activities indicated by study participants. View Now


What Happens When You Automate a Business Glossary?

Business glossaries are critical to an organization’s ability to speak the same data language across the entire company. Without trustworthy data, the enterprise may fail to realize … View Now


The 2020 State of Data Governance and Automation

The foundation of this report is a survey conducted by DATAVERSITY®. The 2020 State of Governance report explores where companies stand in automating the Data Governance processes that are so important to achieving Data Quality. View Now


Trends in Data Management: A 2019 DATAVERSITY Report

DATAVERSITY® asked what’s happening in Data Management through a 2019 Trends in Data Management survey. This paper details and analyzes the latest thoughts, trends, and activities indicated by those who participated in the study. View Now


Trends in Data Governance and Data Stewardship

The foundation of this report is a survey conducted by DATAVERSITY® that included a range of different question types and topics on the current state of Data Governance and Data Stewardship. View Now


Trends in Data Architecture

The foundation of this report is a survey conducted by DATAVERSITY® that included a range of different question types and topics on the current state of Data Architecture. The report evaluates the topic through a discussion and analysis of each presented survey question, as well as a deeper examination of the present and future trends. View Now


Emerging Trends in Metadata Management

This report evaluates each question posed in a recent survey and provides subsequent analysis in a detailed format that includes the most noteworthy statistics, direct comments from survey respondents, and the influence on the industry as a whole. It seeks to present readers with a thorough review of the state of Metadata Management as it exists today. View Now


Business Intelligence versus Data Science

The competitive advantages realized from a dependable Business Intelligence and Analytics (BI/A) program are well documented. Everything from reduced business costs and increased customer retention to better decision making and the ability to forecast opportunities have been observed outcomes in response to such programs. View Now


Insights into Modeling NoSQL

The growth of NoSQL data storage solutions have revolutionized the way enterprises are dealing with their data. The older, relational platforms are still being utilized by most organizations, while the implementation of varying NoSQL platforms including Key-Value, Wide Column, Document, Graph, and Hybrid data stores are increasing at faster rates than ever seen before. Such implementations are causing enterprises to revise their Data Management procedures across the board from governance to analytics, metadata management to software development, data modeling to regulation and compliance. View Now


Navigating the Data Governance Landscape: Analysis of How to Start a Data Governance Program

This report analyzes many challenges faced when beginning a new Data Governance program, and outlines many crucial elements in successfully executing such a program. View Now


Cognitive Computing: An Emerging Hub in IT Ecosystems

Will the “programmable era” of computers be replaced by Cognitive Computing systems which can learn from interactions and reason through dynamic experience just like humans? View Now


Status of the Chief Data Officer: An Update on the CDO Role in Organizations Today

Ask any CEO if they want to better leverage their data assets to drive growth, revenues, and productivity, their answer will most likely be “yes, of course.” Ask many of them what that means or how they will do it and their answers will be as disparate as most enterprise’s data strategies. To successfully control, utilize, analyze, and store the vast amounts of data flowing through organization’s today, an enterprise-wide approach is necessary. View Now


Why Your Business Users Need to Love Metadata

No business likes to throw money out the window, or in the case of the modern day enterprise, down the electronic data stream. View Now


The Question of Database Transaction Processing: An ACID, BASE, NoSQL Primer

There are actually many elements of such a vision that are working together. ACID and NoSQL are not the antagonists they were once thought to be; NoSQL works well under a BASE model, but also some of the innovative NoSQL systems fully conform to ACID requirements. View Now


The Utilization of Information Architecture at the Enterprise Level

This report investigates the level of Information Architecture (IA) implementation and usage at the enterprise level. The primary support for the report is an analysis of a 2013 DATAVERSITY survey on Data and Information Architecture. View Now


Unstructured Data and the Enterprise

In its most basic definition, unstructured data simply means any form of data that does not easily fit into a relational model or a set of database tables. Unstructured data exists in a variety of formats: books, audio, video, or even a collection of documents. In fact, some of this data may very well contain a measure of structure, such as chapters within a novel or the markup on a HTML Web page, but not a full data model typical of relational databases. View Now


Three-Valued Logic

Much has been written and debated about the use of SQL NULLs to represent unknown values, and the possible use of three-valued logic. View Now

An Approach to Representing Non-Applicable Data in Relational Databases

Ever since Codd introduced so-called “null values” to the relational model, there have been debates about exactly what they mean and their proper handling in relational databases. View Now

NO E-R: Modeling for NoSQL Databases

Entity-relationship (E-R) modeling is a tried and true notation for use in designing Structured Query Language (SQL) databases, but the new data structures that Not-Only SQL (NOSQL) DBMSs make possible can’t be represented in E-R notation. View Now

Cardinality, Optionality, and Unknown-ness

This paper explores the differences between three situations that appear on the surface to be very similar: a data attribute that may occur zero or one times, a data attribute that is optional, and a data attribute whose value may be unknown. View Now

A Systematic Solution to Handling Unknown Data in Databases

Ever since Codd introduced so-called “null values” to the relational model, there have been debates about exactly what they mean and their proper handling in relational databases. View Now

The Hybrid Data Model

NoSQL database management systems give us the opportunity to store our data according to more than one data storage model, but our entity-relationship data modeling notations are stuck in SQL land. View Now

We use technologies such as cookies to understand how you use our site and to provide a better user experience. This includes personalizing content, using analytics and improving site operations. We may share your information about your use of our site with third parties in accordance with our Privacy Policy. You can change your cookie settings as described here at any time, but parts of our site may not function correctly without them. By continuing to use our site, you agree that we can save cookies on your device, unless you have disabled cookies.
I Accept