Denodo in the age of containers: how to simplify operation of your virtual layer

Traditional operational tasks like installation, version upgrades, infrastructure scaling and cluster management has been radically transformed with the advent of cloud platforms, containers and orchestration systems. Denodo can take advantage of these environments to create an environment where infrastructure management is a thing of the past, with the goal of reducing operating costs and operating in a much more elastic fashion. 

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Denodo as the core pillar of your API strategy

Most people associate data virtualization with BI and analytics. However, one of the core ideas behind data virtualization is the decoupling of the consumption method from the data model. Why should the need for data requests in JSON over HTTP require extra development? Denodo provides immediate access to its datasets via REST, OData 4, GeoJSON and other protocols, with no coding involved. Easy to scale, cloud friendly and ready to integrate with API management tools, Denodo can be the perfect tool to fulfill your API strategy!

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Why Advanced Monitoring is Key for Healthy Enterprise Deployments

The use of Data Virtualization as a global delivery layer means that Denodo is a critical component of the data architecture. It cannot fail, needs to be fault tolerant and perform as designed. In this context, enterprise level-monitoring is key to make sure the virtual layer is in good health and proactively detect potential issues. Fortunately, Denodo provides a full suite of monitoring capabilities and integrates with leading monitoring tools like Splunk, Elastic and CloudWatch.

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Building a Logical Data Lake with Data Virtualization

Historically data lakes have been created as centralized physical data storage platform for data scientists to analyze data. But lately the explosion of big data, data privacy rules, departmental restrictions among many other things have made the centralized data repository approach less feasible. Using Data Virtualization allows organizations to create a logical - or virtual data lake - without having to physically copy and centralize all of their data.

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Data Virtualization: An Introduction

In an era increasingly dominated by advancements in cloud computing, AI and advanced analytics it may come as a shock that many organizations still rely on data architectures built before the turn of the century. But that scenario is rapidly changing with the increasing adoption of real-time data virtualization to provide a secure, logical data layer. No longer does disparate data sources have to be physically moved to a data warehouse and transformed before it can be used by the business.

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Virtualization for Business Users with Denodo's Data Catalog

A successful data virtualization initiative bridges the gap between two very different perspectives of data management: IT and business.

However, most of the emphasis in these initiatives is put on the IT side, modeling, performance, security, etc. Business users are often left with a large library of data sets, hard to use and navigate.

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Multi-Cloud Data Integration with Data Virtualization

"By 2020, over 90% of Enterprises Will Use Multiple Cloud Services and Platforms" - IDC FutureScape: Worldwide Cloud 2018 Predictions

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Big Insights from Your Big Data using Data Virtualization

Data lakes have grown to be a popular architecture that enables modern analytics and data science. However, complete replication of all corporate data into giant data lakes is unfeasible. Data volumes are too high, and replication to multiple systems creates brittle point-to-point connections. Out-of-synch data and uncontrolled replication leads to “data swamp” scenarios. On top of the physical data lake, a logical approach is more feasible: a logical layer that connects different systems (the data lake among them) and exposes them as one.

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Minimizing the Complexities of Machine Learning with Data Virtualization

Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format.

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Accelerate Migration to the Cloud using Data Virtualization

Organizations are adopting cloud at a fast pace and migration of critical enterprise information resources could be a challenge when dealing with complex and big data landscape.

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Enabling Self-Service Analytics with Logical Data Warehouse

What makes data scientists happy? Of course data. They want it fast and flexible, and they want to do it themselves. But most classic data warehouses are not easy to deal with for agile data access.

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Data Virtualization: An Introduction

What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.

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Advanced Analytics and Machine Learning with Data Virtualization

Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spent most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.

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Stay Ahead of the Pack with Data Virtualization in the Cloud

Explore the endless possibilities of how data virtualization can help drive success in this fast-paced, ever-growing complex data landscape.

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Innovative Data Strategies for Advanced Analytics Solutions and the Role of Data Virtualization

Join Neudesic and Denodo for an interactive webinar to learn how you can apply data virtualization to your advanced analytics strategy for the purpose of achieving growth objectives.

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The Role of Data Virtualization in an API Economy

How data virtualization works as a service container, as a source for microservices and as an API gateway.

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From Single Purpose to Multi Purpose Data Lakes - Broadening End Users

Historically data lakes have been created as centralized physical data storage platform for data scientists to analyze data. But lately the explosion of big data, data privacy rules, departmental restrictions among many other things have made the centralized data repository approach less feasible. In his recent whitepaper, renowned analyst Rick F. Van Der Lans talks about why decentralized multi purpose data lakes are the future of data analysis for a broad range of business users.

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Self-Service Analytics with Guard Rails

Self-service BI promises to remove the bottleneck that exists between IT and business users. The truth is, if data is handed over to a wide range of data consumers without proper guardrails in place, it can result in data anarchy. 

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Big Data Fabric: A Necessity For Any Successful Big Data Initiative

The best of breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform and provide real-time data integration, while delivering self-service data platform to business users.

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A Successful Journey to the Cloud

By 2020, a corporate "no-cloud" policy will be as rare as a "no-internet" policy is today, according to Gartner, Inc. While cloud makes enterprises more flexible and agile, various cloud adoption scenarios such as hybrid cloud, infrastructure modernization and cloud based analytics present a few challenges of their own.

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Self-Service Information Consumption Using Data Catalog

Denodo 7.0 information self-service tool will offer data analysts, business users and app developers searching and browsing capability of data and metadata in a business friendly manner for self-service exploration and analytics.

Market research shows that around 70% of the self-service initiatives fare “average” or below. Denodo 7.0 information self-service tool will offer data analysts, business users and app developers searching and browsing capability of data and metadata in a business friendly manner for self-service exploration and analytics.

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In Memory Parallel Processing for Big Data Scenarios

As data volume and variety grows exponentially, Denodo Platform 7.0 will offer in-memory massive parallel processing (MPP) capability for the most advanced query optimization in the market.

Denodo Platform offers one of the most sought after data fabric capabilities through data discovery, preparation, curation and integration across the broadest range of data sources. As data volume and variety grows exponentially, Denodo Platform 7.0 will offer in-memory massive parallel processing (MPP) capability for the most advanced query optimization in the market.

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Evolving From Monolithic to Distributed Architecture Patterns in the Cloud

Gartner states in its Predicts 2018: Data Management Strategies Continue to Shift Toward Distributed,

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3 Reasons Data Virtualization Matters in Your Portfolio

Real-Time Analytics for Big Data, Cloud & Self-Service BI

Privacy, regulations, and the need for real-time decisions are challenging organizations’ legacy information strategy. This webinar will include an expert panel discussion on logical data warehouse, universal semantic layer, and real-time analytics.

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Parallel In-Memory Processing and Data Virtualization Redefine Analytics Architectures

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.

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A Practical Approach to Getting Started with Self-Service

Business users feel that self-service today is more complicated than expected and spawns more requests to IT than ever before. Data is diverse, distributed in many locations and on many platforms and has quality issues.

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Virtual Sandbox for Data Scientists at Enterprise Scale

The Virtual Sandbox is an overarching framework to support the enterprise-scale roll out of data science programs using the industry standard, CRISP-DM methodology.

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Three Dimensions of Data as a Service

Attend this session to learn how Data as a Service, fueled by data virtualization, overcomes the common data integration challenges.

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Accelerate Self-service Analytics with Universal Semantic Model

Self-service initiatives are successful when business users’ views of the data are holistic and consistent across distinct business functions as enabled by Universal Semantic Model across multiple analytical/BI tools.

Attend this session to learn how data virtualization:

  • Is the best fit technology to enable the Universal Semantic Model
  • Accelerates self-service BI initiatives
  • Provides a holistic view of the data

Agenda:

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Centralize Security and Governance with Data Virtualization

Security, data privacy, and data protection represent concerns for organizations that must comply with policies and regulations that can vary across regions, data assets, and personas.

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The Power of a Complete View of the Customer

A complete view of the customer encompasses a single view of the customer, the customer’s relationships, and the customer’s transactions and interactions, resulting in increased customer satisfaction and retention as well as increased revenue through up-sell and cross-sell opportunities.

Attend this session to learn how data virtualization supports complete view of the customer by:

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Big Data Fabric: A Recipe for Big Data Initiatives

Attend this session to learn how big data fabric enabled by data virtualization constitutes a recipe for securing big data end-to-end and providing easy access to data without having to decipher various data formats.

Big data fabric combines essential big data capabilities in a single platform to automate the many facets of data discovery, preparation, curation, orchestration, and integration across a multitude of data sources. Attend this session to learn how big data fabric enabled by data virtualization constitutes a recipe for:

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