Name: How To Choose a Data Warehousing Solution in An Ever-Changing Data Landscape
Start: 2022-06-23T22:00:00Z
End: 2022-06-23T22:00:51.000Z
Location: BrightTALK
Rating: 4

The Enterprise Architecture channel  presents the next generation of Enterprise IT: recognizing the strategic importance of digital transformation and the data center to make companies more nimble and competitive. These presentations will help demanding IT and BizOps professionals achieve flexibility, scalability and performance with reduced risk and complexity.

Modern infrastructure is a dynamic ecosystem where workloads move fluidly across cloud regions, edge nodes, and on-premises systems. Traditional management methods are no longer sufficient to handle the scale, speed and complexity of these environments. As organizations adopt AI-driven architectures and face increasing regulatory demands, the need for intelligent, autonomous cloud management has become critical. 

At the same time, zero-trust security models are reshaping how businesses protect their systems requiring constant validation of every user, device and transaction. This is a challenge that adds significant operational strain. 

Join Ekambar Kumar Singirikonda, Director of DevOps Engineering at Toyota, to discover actionable strategies for building resilient, predictive, and cost-efficient hybrid infrastructures that align with business priorities.

Key Takeaways:
- Learn how to unify cloud, edge, and on-prem systems with intelligent orchestration platforms that optimize workloads for performance, cost, and compliance.
- Discover how to implement zero-trust security at scale without disrupting operations or user experience.
- Explore AI-driven architectural patterns, including predictive resource management, policy-based orchestration, and self-healing systems.
- Understand strategies to reduce hybrid infrastructure complexity through automation frameworks and unified management tools.
- Gain a roadmap for transitioning from manual oversight to predictive, business-aligned infrastructure operations.

Simplify Hybrid Infrastructure with Autonomous Cloud and Zero-Trust Strategies

Modern CIOs are pivotal drivers of business transformation. As AI reshapes industries, organizations have to embed intelligence into every aspect of their operations to remain competitive. From automating processes and enhancing decision-making to elevating customer experiences, AI's potential to drive change is huge —but unlocking its value requires strategic leadership and a clear roadmap.

This session will explore how CIOs can leverage AI as a catalyst for enterprise-wide transformation, aligning initiatives with business goals and delivering measurable outcomes. 

Join Ekambar Kumar Singirikonda, Director of DevOps Engineering at Toyota, to uncover how to embed AI into your enterprise DNA and lead with confidence in the AI era.

Key Takeaways:
- Discover strategic AI integration techniques that embed AI across enterprise functions to achieve measurable business impact.
- Understand how to evolve as a modern CIO, building executive influence and aligning AI initiatives with business objectives.
- Explore frameworks for evaluating emerging technologies and demonstrating ROI.
- Gain practical approaches to foster innovation, manage change, and upskill teams for AI adoption.
- Learn how to structure multi-year transformation plans that balance innovation with operational stability.

Harnessing AI to Drive Enterprise Transformation and Leadership Excellence

CIOs face a dual mandate with AI: they oversee the infrastructure that sustains trust in data and AI systems, but they must also narrate that infrastructure persuasively. Technical excellence means little if boards see only cost centres and regulators see only opacity. The session addresses how to measure and communicate AI value in terms that resonate beyond IT.

Effective measurement requires distinguishing two forms of transparency that executives often conflate. Data lineage describes how information flows through systems: origins, transformations, dependencies. Data provenance records the documented history of specific values: who created or modified them, when, under what parameters. Lineage answers architectural questions relevant to planning and change management; provenance answers forensic questions relevant to audit and dispute resolution. Metrics derived from each capability speak to different audiences. Time to audit readiness concerns board risk committees; value reconstruction time concerns legal and compliance; data preparation hours concern COOs and budget owners.

The session presents a metrics framework that translates governance capabilities into business language. Participants will examine how lineage coverage, provenance depth, error detection time, and downstream impact incidents can be reported in ways that demonstrate value rather than merely documenting activity. The goal is not dashboards for their own sake, but communication that builds confidence among stakeholders who will never examine the underlying systems directly.

Key Takeaways:
- Lineage and provenance serve different purposes and speak to different audiences.
- Metrics must translate technical capabilities into business outcomes.
- Time-based metrics (audit readiness, error detection, value reconstruction) communicate operational value.
- Governance framed as enabling rather than defensive builds broader organizational support.

Measuring AI Value: Metrics That Speak to Boards and Regulators

Modernization presents a critical yet complex opportunity for enterprises striving to balance innovation with operational excellence. Technology leaders often find themselves performing a precarious trapeze act—innovation attracts funding, but operational failures dominate headlines, exposing organizations to significant risks. With 79% of application modernization projects failing and 88% of transformations falling short, the stakes have never been higher.

This session uncovers the root causes of modernization failures, with a focus on applications and data—the foundation of enterprise transformation. Attendees will gain practical strategies to navigate these challenges, ensuring their initiatives align with business goals and deliver tangible results.

Join Jeffrey Tefertiller, Founder of Service Management Leadership, to explore proven approaches that can significantly improve your chances of modernization success.

Takeaways:
- Identify the key reasons behind modernization project failures.
- Align application and data strategies with overarching business objectives.
- Apply actionable frameworks to mitigate risks in transformation initiatives.
- Examine real-world examples of successful modernization efforts.
- Gain insights to future-proof your enterprise technology landscape.

Driving Modernization Success in 2026

Technology initiatives fail most when organisations scale complexity before they’re ready. Teams are asking CIOs to fund ‘enterprise-grade’ platforms for data, AI, security and more before they’re even clear on data ownership, platform priorities and use cases. In turn, organisations invest in high-ticket technologies that teams don’t adopt, yielding unsatisfactory outcomes.

This session introduces a practical framework for starting small and scaling smart when implementing complex enterprise capabilities. Focusing on the real-world example of data catalog and data quality, we’ll explore how starting with small, user-driven solutions allows teams to get to what really matters - ownership, definitions, quality metrics and operational decisions. Rather than focusing on which technology to choose, it’s about sequencing - knowing what can be built first, what can be delayed and understanding when scaling makes the most sense.

The framework has a broad application that CIOs can use for any initiative where tooling, behaviour change and governance need to evolve together. Join Inigo Antolin, Head of Data Management and Governance at the British Council, to receive a practical mental model to reduce risk, preserve optionality and scale with confidence rather than hope.

Key Takeaways:
- Why scaling enterprise platforms too early results in wasted resources and lost trust.
- How starting small can strengthen decision-making across ownership, definitions and quality.
- Discover the Start Small, Scale Fast framework that you can apply across all new tech initiatives.
- How to use constraints as a strategic advantage rather than a limitation.

Start Small, Scale Fast: Hard Lessons from Data Catalog & Data Quality

In today’s evolving IT landscape, selecting the right storage architecture is critical for optimal performance, scalability, data governance, and cost-efficiency. Furthermore, AI workloads have uniquely influenced how we meet these demands from our storage infrastructure. This webinar provides a technical deep dive into three fundamental storage deployment models – on-premises, cloud, and hybrid – examining their architectures and operational trade-offs through the lens of two key concepts: indirection (accessing data through mapping layers that provide flexibility and abstraction) and redirection (rerouting data requests to enable failover, load balancing, and optimized performance). 

We are going to take some of the key stages of AI lifecycle development as sample use-cases (such as data ingestion, preparation, training, inferencing, and retrieval) and compare how each storage model can serve these use-cases across varying access patterns, data volumes, and performance requirements.

Attendees will gain a practical framework for aligning AI workloads with the most suitable storage architecture, balancing cost, scalability, and latency. Whether you are building AI infrastructure from scratch or optimizing existing deployments, this session will help you make informed decisions for AI-ready storage.

Learning Objectives and Key Takeaways:

• Introduction to the 3 different types of storage deployment models – on-prem, cloud, and hybrid
• Trade-offs for each deployment model
• Importance of indirection and redirection
• Understand how AI-specific data types and access patterns (e.g., embeddings, checkpointing) influence storage performance and design
• Evaluate trade-offs in latency, scalability, security, and cost when choosing storage for different stages of the AI pipeline
• Gain a decision-making framework for selecting the right storage model based on workload characteristics and infrastructure goals

AI Meets Storage: Comparing On-Prem, Cloud, and Hybrid Architectures Across the AI Lifecycle

As AI workloads continue to scale, the physical infrastructure supporting them must evolve to deliver the performance, reliability, and efficiency required by modern data-intensive applications. This webinar will focus on the interconnect foundations of AI infrastructure, including storage. There will be an emphasis on the form factors, connectors, cables, and transceivers standards that enable scalable and interoperable systems.

We’ll explore how SNIA’s SFF Technical Work Group is contributing to the development of physical layer standards that support high-performance interconnects and storage devices used in AI environments. Topics will include the importance of transceiver innovation and the need to evolve; the role that form factors like EDSFF play; and how innovative cabling and connector designs support the adoption of high-speed signaling technologies like PCIe 8.0 and beyond.

This session is ideal for architects, engineers, and technologists who want to understand how physical infrastructure standards are enabling the next generation of AI systems.

Key Concepts:
• Transceiver development for out-of-box signaling 
• Connectors and cables for high-speed inbox signaling
• Copper vs. optical and the challenges of both
• SSD form factor innovations and their impact on density and thermal design

Accelerating AI Infrastructure: The Role of 400G and PCIe 8.0 in Next-Gen Interconnects

Storage security changes and adaptations are a fact of life to deal with the ever-changing threat and regulatory landscapes. A black swan event, a new standard, or a regulation often serves as a catalyst for organizations to review their controls and practices. The recent publication of NIST Special Publication 800-88 Rev. 2, Guidelines for Media Sanitization, is drawing attention to storage security, but it is not the only development worth noting. SNIA, Open Compute Project (OCP), Trusted Computing Group (TCG), and IEEE are developing or have developed standards and specifications that could be important going forward. One central theme underlying several of the activities is in storage sanitization (i.e., controlled eradication of data).

This session brings together a unique panel of experts who have served as editors of some of the most important storage sanitization standards. These experts also have broad knowledge of storage security. As a panel, they will provide insight into these new developments as well as observations on what organizations are experiencing. A moderator will engage the panel with thought-provoking questions, but participants are also encouraged to bring their own questions.

Join us to understand: 
• Changes to storage sanitization and what that means to organizations
• Primary drivers for storage security
• Difference between Storage Sanitization and Data Sanitization
• Interplay and interdependencies for storage security standards/specifications

The Storage Security Shake-Up: Adapt Now or Get Left Behind

Magnetic RAM, Resistive RAM, Ferroelectric RAM, and other new memory technologies have been pushed to the forefront by issues with Moore’s Law scaling, but the ancillary benefits they bring to computing will provide significantly more important longer-term benefits.  In this session, analysts Jim Handy and Tom Coughlin will explain how these new nonvolatile technologies will help accelerate and cost-reduce inference through compute-in-memory engines while standard computing will also see lower costs and higher performance by bringing inexpensive persistence closer to, and even within the processor chip itself.  The session will also address ways that new frameworks like CXL will help provide persistence-friendly structures at all levels of the computing hierarchy.

How New Memories Will Accelerate Both Inference and General-Purpose Compute

Smarter data management is the key to unlocking the full potential of next-generation data infrastructure for AI. This webinar explores how the SNIA Cloud Data Management Interface (CDMI™) standard enables government labs, HPC centers, and organizations leveraging the power of AI/ML to reimagine and innovate their infrastructure to meet next-generation data management demands. CDMI is a mature ISO standard (ISO/IEC: 17826:2022) with over 15 years of development and implementation, and is purpose-built for cloud data management. CDMI's extensive capabilities enhance key aspects of AI operations by transforming data management challenges into streamlined, intelligent workflows. 

CDMI 3.0 (in development now) will further extend the standard with:
• Automated resource discovery that accelerates AI workload efficiency
• Portable data movement that enables preservation of metadata fidelity during migrations, including graph relationships and knowledge graph
• MCP-based data management that enhances AI accuracy while improving access speed

Building on a proven, standards-based foundation, CDMI enables true machine-readable data management. CDMI 3.0 will further improve how teams discover resources, orchestrate AI agents, and protect data across complex cloud environments while establishing the infrastructure for automated, self-describing data ecosystems with full auditability that drive future innovation. 

Read the SNIA CDMI 3.0 white paper https://www.snia.org/educational-library/introduction-cloud-data-management-interface-cdmitm-30-2025

Intelligent Data Management: Shaping the Future for AI Workloads

Join us for an insightful webinar and demonstration as we explore the foundational process of training machine learning models, with a special focus on computer vision applications. This session will guide participants through the essential stages of model development—from data preparation to algorithm selection—highlighting how these steps influence performance and accuracy.
Through practical demonstrations and conceptual discussions, attendees will gain a deeper understanding of how vision models are built and validated, and how these techniques can be extended to broader predictive analytics. We’ll also examine common challenges and share strategies to overcome them, emphasizing the value of simplicity, modular design, and thoughtful evaluation.

Key Takeaways:
• Core steps in the model training lifecycle
• Why data quality is critical to success
• Understanding inferencing and metrics for model validation
• Shifting from engineering mindset to modeling intuition
• Avoiding common pitfalls in training workflows
• Embracing parsimony and modularity in design
• Using checkpoints to monitor and refine progress

From Data to Decisions: Understanding How AI Models Learn

Storage networking consumers are fully aware of the classic benefits of Fibre Channel as a dedicated storage networking solution that provides reliable and lossless data transfer as well as high availability and redundancy.  What is less known is that Fibre Channel supports several Upper Layer Protocols (ULPs) including SCSI, FICON, NVMe, Avionics and RDMA. In this webinar, we explore the FC-4 layer of the Fibre Channel architecture that provides the services used by the various ULPs deployed in Fibre Channel products. Join us for an exploration into:

• The history of Fibre Channel’s versatility 
• Fibre Channel architectural layers
• Overview of the current FC-4 transports: FCP, FC-SB, FC-NVMe, FC-RDMA

Attendees of the webinar will become well versed in the scope and capabilities of Fibre Channel and develop a well-rounded understanding of the technology.

Versatility of Fibre Channel: Multi-Protocol Magic

Edge computing is transforming AI deployment by processing data locally rather than in distant cloud data centers. This session explores how distributed intelligence enables both sustainability and capabilities that centralized architectures cannot provide.

Through two contrasting deployments—precision agriculture in resource-constrained environments and real-time manufacturing optimization in advanced facilities—we demonstrate how edge computing addresses fundamentally different challenges using the same architectural principles. Both achieve 50-70% energy reduction with compelling ROI, while solving problems where cloud computing fails due to latency, connectivity, bandwidth, or data sovereignty requirements.

Beyond the case studies, attendees learn decision frameworks for edge versus cloud architecture, strategies for deploying AI in environments with limited infrastructure, and how to build business cases that balance sustainability goals with operational performance. The session provides actionable guidance for organizations seeking to scale AI deployments while reducing their environmental footprint and enabling new capabilities.

Edge AI from Farm to Factory: Building Sustainable Intelligence across the world

This webinar will explain the process of model inferencing and deployment options. We will discuss how trained models are used to make predictions and the considerations for deploying models. Attendees will understand the challenges and strategies for optimizing model performance, covering:
 
• What is model inferencing  
• Model inferencing process 
• How inferencing deployment differs in Gen AI and visual inspection
         - Inferencing deployment software options 
          - Inferencing hardware considerations (e.g., on the edge, on-prem, etc.)
• Deploying AI models for production best practices and lessons learned 
• Strategies for maintaining model performance post-deployment

AI Model Inferencing and Deployment Options

On-premises enterprise data centers have been in decline since the creation of co-lo’s and the cloud, but a new age is emerging with the advent of AI. While most AI is served by the mega data centers mushrooming in “data corridors”, some applications require the low latency response times that edge computing was designed to provide. The industry readily builds large numbers of small processing centers at the edge to serve things like 5G cell service. But constructing AI data centers close to every high demand user is financially impractical and subject to local restrictions due to their enormous demands on power and water.

Tune into this session presented by industry thought leader Bob McFarlane to learn how reviving enterprise data centers as edge facilities may be the answer to acquiring virtually instant response times. Despite significantly greater power and cooling demands, they are unlikely to reach the levels that concern local citizens, enabling them to be renovated or built where necessary without pushback. Companies with significant AI demands may need to start planning and budgeting for high capacity, high reliability infrastructures with costs and staffing beyond anything they have previously experienced.

Learning Objectives:
- Understand the key elements of Edge Computing, and how they relate to AI applications.
- Learn the limitations AI services providers confront in constructing edge facilities.
- Learn to identify AI applications that require the fast response times of the “edge”.
- Prepare for the challenges your organization may face in utilizing AI in critical applications.

Will Enterprise Data Centers Become ‘The Edge’ for AI?”

As breakthrough technologies like AI mature, they inevitably evolve from centralized to decentralized architectures. With global edge computing spending projected to reach $378 billion by 2028, organizations that continue relying solely on centralized AI risk falling behind in both efficiency and performance. The pervasive waste and expense of centralized generative AI models have underscored the urgent need for intelligence at the edge.

While edge computing concepts have existed since the 1990s with content delivery networks, today's businesses face the complex challenge of transforming their architectures to process data closer to its source. This session explores how leading organizations are navigating this transition, reducing latency and costs while competing with decades of established centralized cloud infrastructure.

Join Alan Morrison (Advanced Data Technologies Consultant and Writer) to discover how decentralized AI and edge computing are revolutionizing data processing across industries.

Key Takeaways:

- Understand why centralized AI approaches are becoming unsustainable and how edge computing addresses these limitations
- Learn practical strategies for implementing edge intelligence from organizations already succeeding with this approach
- Discover the economic benefits of processing data at the edge versus transmitting everything to centralized systems
- Gain insights into market growth projections and how to position your infrastructure investments accordingly
- Identify the architectural transformation challenges of edge computing and how to overcome them

Decentralized AI: The Future of Edge Computing

Today’s enterprise workflows don’t live in one place — they move across edge devices, cloud infrastructure, and on-prem systems in milliseconds. Some start at the edge — like sensor data or customer interactions — while others finish there, after running through cloud analytics or AI inference. The result is a constantly shifting architectural boundary where speed, security, and compliance collide.

This session looks at how to design cross-architectural workflows that maintain consistency, security, and data integrity from end to end. We’ll unpack how different architecture types — edge-native, hybrid, and multi-cloud — impact data handling, governance, and latency. And we’ll dive into the new layer of complexity AI introduces: model hallucinations, prompt injections, and evolving regulatory constraints when sensitive customer data moves from edge to cloud.

Join Eugina Jordan, CEO of YOUnifiedAI, as she breaks down how to architect distributed systems that are both intelligent and compliant — not in theory, but in practice.

Key takeaways:

- Understand how modern workflows traverse edge, cloud, and on-prem environments — and where control points break down
- Design architectures that protect customer data and maintain compliance even when processing happens across geographies
- Implement AI workflows that minimize hallucination risks and prevent prompt-injection attacks at the edge
- Balance performance with governance — using architecture, not just policy, to enforce trust
- Build an operational model that lets security, data, and AI teams collaborate on a single distributed workflow

Building Secure AI Workflows Across Edge, Cloud & AI Without Breaking Trust

Data Democratization is the final frontier of employee empowerment. Citizen developers are a widely discussed topic, but what about citizen analysts? Analytics can be an indispensable asset from the C-suite all the way to the frontline worker. But there are several challenges executives and non-technical users face in leveraging data for decision-making. How can AI help in democratizing data and transform your organization into a data-driven organization?

Join AI industry analyst Kashyap Kompella in this fast-paced webinar on the current state of data democratization, its challenges, and the best practices of leveraging AI.

Key Takeaways

• Executive overview of the what, why, and how of data democratization.
• Rationale and role of AI for data democratization, including:
o How AI can drive adoption of self-service analytics;
o How AI can enhance the user experience for analytics applications;
o How AI can improve data protection and privacy practices
• Use cases of examples of AI for data democratization.

Role of Artificial Intelligence in Data Democratization

At the core, Machine Learning Operations (MLOps) takes an experimental machine learning model into a production system. MLOps is an emerging practice distinct from traditional DevOps. ML lifecycle involves using patterns from training data, making MLOps workflow sensitive to data changes, volumes, and quality. Additionally, matured MLOps should support monitoring both ML lifecycle activities and production model monitoring.

Key Takeaways:
• Build cloud-native serverless analytics pipeline. 
• Learning different data automation pattern including data lake, lake house and data mesh architecture.
• Automate end-to-end data pipeline with MLOps.

Decoding Data Automation With Cloud-Native Analytics Pipeline and MLOps

Managing Resiliencies for edge data-centre pods (& Clusters)

The exponential growth of data and cloud services is driving demand for new data centers and upgrades to existing centers. But this growth cannot be sustained without considering the need to develop efficient Data Centers: they have to consume less energy and water and increasingly need to rely on green energies.
This presentation will investigate system engineering approaches which break the siloes between the different domains (building / cooling system / racks…). We highlight  system level models that leverage simulation to find the best design compromise and reach sustainability targets.
Bringing smart automation to this approach is key to efficiently optimize and test several rack layouts and configurations while monitoring KPIs.
To further improve on sustainability and energy efficiency, 3D simulation solutions can complement 1D methods, delivering the needed detail and granularity in results to drive impactful changes.

From Excessive Data Center Energy to Modular, Generative & Sustainable Designs

Companies can choose between two high-level architectures: data warehouses and data lakehouses. In data warehouses, data is stored by the warehouse in proprietary formats that can only be read by that data warehouse. In contrast, a data lakehouse is an architecture in which the data is primarily stored in the customer’s own cloud object storage (S3, ADLS, etc.), in open formats, and then processed/queried by one or more engines. 

Join Dremio product manager Ben Hudson to discuss open data lakehouses, and the benefits that vendor-agnostic table formats that are governed and developed by a diverse set of companies can provide.

Reimagining Analytics With an Open Lakehouse

Delivering microservices apps across clouds is not easy. The digital supply chain that underpins any app can be super complex and eye watering. 
The full cloud native stack can encompass physical and software abstraction, security, observability , on-premise, in the cloud and at the edge. 
Multiple teams manage this stack to bring all these components together to ensure a delightful user experience. 
In this session we will explore some of the trends, challenges and ultimate solutions when delivering the Full Cloud Native Stack.

Full Cloud Native Stack

Many enterprises today have large set of applications – whether Telco or otherwise – running on on prem infrastructure along with public cloud.  They are feeling need of moving towards cloud native architecture – driven due to various factors.  This webcast will talk about various factors to be considered while driving this cloud native transformation. Various risk factors calls out for a pragmatic approach to be adopted to move quickly as well as safely and to reach there on time.

Pragmatic way to accelerate cloud native transformation

Data Architecture & Engineering in 2022

Are you exhausted trying to keep up with all the new terminology around data lake architecture that supports your BI solutions? What exactly is a data mesh? Do I now need a data fabric? Or...maybe a lakehouse? Or just a data lake? 

Join Dr. Ries as he walks through today's data architecture design decisions -- and there is no shortage of them. This webinar will help you understand what is the best path forward for your BI challenges, so that you can confidently break down silos and democratize your data. The talk will also discuss how machine learning can be utilized in data architecture and dashboarding solutions.

How To Choose a Data Warehousing Solution in An Ever-Changing Data Landscape

Data Warehousing

Data Lake

Machine Learning

Data Analytics

Data Architecture

machine learning

data landscape

Data Analysis

Business Intelligence

Business Analytics

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How To Choose a Data Warehousing Solution in An Ever-Changing Data Landscape

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