With the rise of cloud-first initiatives and pay-per-use systems, forecasting IT costs has become a challenge. It's easy to start small, but it's equally easy to get skyrocketing bills with little warning. FinOps is a discipline that tries to tackle these issues, by providing the framework to understand and optimize cloud costs in a more controlled manner. The Denodo Platform, being a middleware layer in charge of global data delivery, sits in a privileged position not only to help us understand where costs are coming from, but also to take action, manage, and reduce them.Watch on-demand
In the face of growing data breaches, mitigating risks is paramount for any organization. This webinar explores how data virtualization, which offers a real-time, unified view of dispersed data, can help navigate these challenges. With insights from key reports, we underline the pivotal role of data virtualization in enhancing data security and regulatory compliance. Moreover, we delve into its ability to eliminate data silos, fostering improved collaboration and decision-making, and ensuring business continuity.Watch on-demand
In the era of data-driven decisions, how you manage and utilize your data is a critical determinant of your business's success. Understanding whether a Data Mesh or Data Fabric is suitable for you can revolutionize your data strategy, sparking innovation and providing you with the insights to drive your business forward.Watch on-demand
With the rapidly changing business landscape, growing data volumes and complexity, and the emergence of new technologies and data sources, an agile data architecture is crucial for a company's success. It enables data-driven decision-making, enhances data security and governance, and improves team collaboration and efficiency. The question many have is how to build this agile data architecture.Watch on-demand
Traditional Data Management approaches that rely on collecting data within a centralized data store, such as a cloud data warehouse or data lake, are core building blocks for your data modernization efforts. However, you need more to address the diverse needs of today's businesses. Hybrid/Multi-cloud, SaaS, and data privacy laws are just a few examples of why data will remain distributed.Watch on-demand
The cloud is no longer the future; the cloud is now. Companies continue to shift their workloads and data to the cloud to leverage the speed, agility, and efficient scalability the cloud offers. As a result, there is increasing attention given to cloud-based data warehouses, data lakes, and lake houses as a way for organizations to simplify managing their ever-increasing volumes of data. However, many find that these new technologies only complicate the data management process.Watch on-demand
Data lakes have been both praised and loathed. They can be incredibly useful to an organization, but it can also be the source of major headaches. Its ease to scale storage with minimal cost has opened the door to many new solutions, but also to a proliferation of runaway objects that have coined the term data swamp.
However, the addition of an MPP engine, based on Presto, to Denodo’s logical layer can change the way you think about the role of the data lake in your overall data strategy.
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Today we are seeing the evolution of organizations becoming more and more data centric. This has given rise to the need to provide data citizens with easy access to fully governed data marketplaces. In this webinar, we will hear from Denodo subject matter experts on the role of the AI powered Denodo recommendations engine in providing easy access to your enterprise data to assist with better decision making. We will also hear from industry expert Sherlock Holmes from Genware Computer Systems on his experiences in data marketplaces.
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The ever-growing data landscape drives initiatives to automate many aspects of the analytics lifecycle; such as data access, enablement of semantics, BI and others. Automation has become an integral part of our daily lives in the enterprise data fabric. The AI-driven initiative to automate the data access and provide guidance to the right data assets, correlates with the initiatives of the data scientists to get access to more curated data.
In this session we will discuss:Watch on-demand
Data Fabric and Data Mesh had been trending high in the data and analytics space, so what is a data fabric and what is a data mesh? How are they different and does it matter?
In this session, Ravi Shankar, Sr. VP and CMO at Denodo, will address the market and analyst views of these two approaches and how they complement each other. His presentation will be followed by an insightful and engaging fireside chat with Mark L Johnson, Executive Director, Client Solutions at Fusion Alliance to further discuss what it all means in terms of value to the customer.Watch on-demand
It’s almost impossible to find any organization that does not have data and analytics as one of their top priorities to further their business objectives. At the same time the data and analytics landscape is evolving faster than ever, making the data management ecosystem more complex than ever before. As data gets increasingly distributed across systems and locations, every forward looking organization should adopt a logical architecture to be future ready.
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We all heard about Self-Service initiatives, or maybe even implementing one. With the evolution of data landscape we have become data driven, when we should be information driven. Data without context lacks meaning. Data meaning is what drives the valuable business insights. We need to drive our consumers to the right information, while providing guided and governed experience across diverse consumer community.
Attend and Learn:
- What it takes to make Self-Service initiatives successful
- How Governed Data Service Layer enables guided data discovery
Semantics has been a topic often associated with academia with little impact in the analytics world. However, that tendency is starting to change, fueled by self-service and data democratization initiatives that broaden the scope of who’s accessing data and what those users expect.Watch on-demand
Data lakes and data warehouses offer organizations centralized data delivery platforms. The recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI we discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and that 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important.Watch on-demand
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too.Watch on-demand
Given the growing demand for analytics and the need for organizations to advance beyond dashboards to self-service analytics and more sophisticated algorithms like machine learning (ML), enterprises are moving towards a unified environment for data and analytics. What is the best approach to accomplish this unification?Watch on-demand
According to Dresner Advisory’s 2020 Self-Service Business Intelligence Market Study, 62% of the responding organizations say self-service BI is critical for their business. If we look deeper into the need for today’s self-service BI, it’s beyond some Executives and Business Users being enabled by IT for self-service dashboarding or report generation. Predictive analytics, self-service data preparation, collaborative data exploration are all different facets of new generation self-service BI.Watch on-demand
Why a logical data fabric is the cornerstone to Building a Unified Data Warehouse and Data LakeWatch on-demand
Real time analytics techniques promise to enrich your traditional analytics with real time data points. It's key for many scenarios like supply chain management or customer care. Data Virtualization is well known for offering real time connectivity to diverse sources and federation capabilities: the two base ingredients for real time analytics. However, building a strategy around these concepts can be challenging. Impacting delicate data sources, security and performance concerns are often mentioned.
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Businesses have benefited greatly from the ability to combine all of their cloud applications as well as on-premises systems. Migrating workloads to the cloud has led to increased cloud adoption while business leaders are realizing their digital cloud transformation has the potential to unlock their data in extraordinary ways. Data driven insights and strategies help ensure that cloud data integration can enable the platform modernization initiatives in your organization.
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What is a data fabric? Gartner defines it as “one architecture that can address the extreme levels of diversity, distribution, scale and complexity in organizations’ data assets that are adding tremendous complexity to the overall data integration and data management design.” But do you know that data fabric can be logical or physical? What’s the difference? Which one to use and when?
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Digital Transformation has changed IT the way information services are delivered. The pace of business engagement, the rise of Digital IT (formerly known as “Shadow IT), has also increased demands on IT, especially in the area of Data Management.
Data Services exploits widely adopted interoperability standards, providing a strong framework for information exchange but also has enabled a growth of robust systems of engagement that can now exploit information that was normally locked away in some internal silo with Data Virtualization.Watch on-demand
Advanced data science techniques, like machine learning, have proven to be extremely useful to derive valuable insights from your data. Data Science platforms have become more approachable and user friendly. With all the advancements in the technology space, the Data Scientist is still spending most of the time massaging and manipulating the data into a usable data asset. How can we empower the data scientist? How can we make data more accessible, and foster a data sharing culture?Watch on-demand
Self service is a major goal of modern data strategists. Denodo’s data catalog is a key piece in Denodo’s portfolio to bridge the gap between the technical data infrastructure and business users. It provides documentation, search, governance and collaboration capabilities, and data exploration wizards. It’s the perfect companion for a virtual layer to fully empower those self service initiatives with minimal IT intervention. It provides business users with the tool to generate their own insights with proper security, governance and guardrails.
In this session we will see:Watch on-demand
Gartner has predicted that organizations using Data Virtualization will spend 40% less on data integration than those using traditional technologies. Denodo customers have experienced time-to-deliver improvements of up to 90% within their data provisioning processes and cost savings of 50% or more. Join us for this webinar to discover how Data Virtualization can help accelerate your time-to-value from data while reducing the costs at the same time. As Rod Tidwell (Cuba Gooding Jr.) said in the movie 'Jerry Maguire', "Show me the money!"Watch on-demand
Today’s enterprise data ecosystems look different than in the past. In retrospect, the idea of physically consolidating all data into a single location seems quaint. As digital business initiatives focus on exploiting data rather than merely storing it, the growing communities of savvy enterprise users demand connectivity to data assets wherever they exist. Just like how the Web links data from anywhere, data virtualization enables connectivity to data from anywhere through APIs to use and share data for business processes and AI/ML analytic models.Watch on-demand
A shift to the cloud is a common element of any current data strategy. However, a successful transition to the cloud is not easy and can take years. It comes with security challenges, changes in downstream and upstream applications, and new ways to operate and deploy software. An abstraction layer that decouples data access from storage and processing can be a key element to enable a smooth journey to the cloud.
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Being able to maintain a well managed and curated Data Warehouse, along with keeping up with all of the demands of a very sophisticated consumer group can be a challenge. The new user wants access to data, they want to experiment, fail fast and if they do find usable insights/algorithms they want them productionized. This puts pressure on an IT organization and pushes them closer to a Bimodal operation where the regular IT processes that are highly curated, well defined and managed contrast sharply with the demands of the more sophisticated user.Watch on-demand
The current data landscape is fragmented, not just in location but also in terms of shape and processing paradigms. Cloud has become a key component of modern architecture design. Data lakes, IoT, NoSQL, SaaS, etc. coexist with relational databases to fuel the needs of modern analytics, ML and AI. Exploring and understanding the data available within your organization is a time-consuming task. Dealing with bureaucracy, different languages and protocols, and the definition of ingestion pipelines to load that data into your data lake can be complex.Watch on-demand
Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task.Watch on-demand
Data Lake strategies seem to have found their perfect companion in cloud providers. After years of criticism and struggles in the on-prem Hadoop world, data lakes are flourishing thanks to the simplification in management and low storage prices provided by SaaS vendors. For some, this is the ultimate data strategy. For others, just a repetition of the same mistakes. Attend this session to learn:
- The benefits and shortcoming of cloud data lakes
- The role and value of data virtualization in this scenario
- New development in data virtualization for cloud
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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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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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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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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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.Watch on-demand
"By 2020, over 90% of Enterprises Will Use Multiple Cloud Services and Platforms" - IDC FutureScape: Worldwide Cloud 2018 PredictionsWatch on-demand
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.Watch on-demand
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.Watch on-demand
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.Watch on-demand
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.Watch on-demand
Explore the endless possibilities of how data virtualization can help drive success in this fast-paced, ever-growing complex data landscape.Watch on-demand
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.Watch on-demand
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 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.
Attend this session to learn why data virtualization:Watch on-demand
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.Watch on-demand
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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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.Watch on-demand
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.Watch on-demand
Gartner states in its Predicts 2018: Data Management Strategies Continue to Shift Toward Distributed,Watch on-demand
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.Watch on-demand
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.Watch on-demand
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.Watch on-demand
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.Watch on-demand
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
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.Watch on-demand
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:Watch on-demand
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:Watch on-demand