Name: Data Platform Capabilities for Modern Data Management Architectures
Start: 2023-04-19T18:00:00Z
End: 2023-04-19T18:00:59.000Z
Location: BrightTALK
Rating: 5

Large, knowledge-rich, regulated enterprises can turn scattered, fragmented data into trusted decisions, fast today, scalable tomorrow, so teams ship work sooner, reduce risk, and prove ROI with human-centric, contextual AI. 

Hear from organizations — our customers — on how they’re creating trustworthy, accurate, and cost-effective AI applications and business insight engines with the Progress Data Platform for AI.

Discover how to build enterprise-ready AI systems with insights from Progress experts — developers, engineers, and knowledge managers — who will share proven strategies and best practices for success.


Connecting to REST APIs shouldn’t be a manual, code-heavy process. Progress DataDirect Autonomous REST Connector makes it easy to integrate any REST data source with any SQL-based application or BI tool—without coding.

This intelligent connector automatically interprets REST API responses and maps them to relational models, giving your business real-time, SQL-based access to even the most complex APIs.

In this short Coffee Chat, you'll learn how to:

Build custom connectors for any API quickly
Reduce dev costs and time-to-market using autonomous data connectivity
Speed up analytics, BI and reporting with universal API access

Coffee Chat- Instantly Connect to Any REST API with Autonomous REST Connector

Are You Ready for an AI-Powered Future?

In today’s business landscape, data is your most powerful asset, but only if it’s accessible, digestible and ready for implementation. Progress recently surveyed enterprise leaders to understand the state of data readiness, uncovering critical insights across four key areas.

In this on-demand webinar, you'll hear David Loshin, Data and Information expert from Knowledge Integrity, Inc., dive deeper into the findings from Progress’ latest report. The report results highlight that while many organizations recognize the potential of AI, data challenges are still a major barrier.   

You’ll leave the webinar with key takeaways around topics like these:

- Data Literacy & Accessibility: Are teams able to find and use the data they need?
- Business Initiative Support: How well does your data landscape enable innovation and growth?
- Data Readiness for AI: Is your data prepared to fuel AI and advanced analytics initiatives?
- Data Governance & Risk Management: Are proper controls in place to facilitate  compliant, trustworthy AI outcomes?

Unlock the AI Insights Your Data Holds: Progress Survey Findings

Technology teams know their platforms deliver value but proving that value in financial terms can be an uphill battle. ROI often feels abstract and, without a clear framework, it’s difficult to baseline benefits, align with business expectations or justify future investment. 

In this on-demand webinar, Gerard Rebalsky, Senior Strategic Account Executive at Progress, shares practical methods for turning ROI into a concrete story that resonates across the organization. You’ll discover techniques for:

- Simplifying ROI for data and knowledge management  projects and making it easier to measure
- Setting a baseline of value and tracking ROI progress over time          
- Translating performance metrics into business impact   
- Communicating ROI effectively to leadership

Whether you’re an IT leader, architect, business owner or part of a digital transformation team evaluating a new solution or validating an existing one, you’ll gain practical approaches to prove ROI and accelerate adoption. We’ll illustrate how the Progress Semaphore platform’s semantic approach helps organizations demonstrate tangible value from their knowledge management and metadata investments.

Making ROI Real: Proving the Value of Data and Knowledge Management Investments

Over decades of research, Syngenta has accumulated vast amounts of R&D knowledge locked away in legacy systems, siloed repositories and spreadsheets. However, the challenge has been how to make this hidden knowledge findable, accessible and reusable for today’s scientists. 

In this webinar, a Syngenta expert shares how the Synapse platform, a new-generation enterprise search solution, is helping researchers uncover relevant insights from early research, reduce time wasted on searching and accelerate innovation in crop science.   
 
Powered by Progress MarkLogic and Progress Semaphore technologies, and delivered in partnership with Datavid, Synapse uses semantic enrichment, AI and knowledge graphs to connect information across diverse sources and turn it into actionable intelligence. 

In this session, you’ll discover: 

- Ways that semantic search and knowledge graphs empower scientists to retrieve critical lab results in seconds 
Real-world impacts of Synapse on researcher productivity and scientific innovation 

- Efficiency gains from reducing study duplication and surfacing historical experiments 

- Future opportunities to extend this approach across life sciences and AI-driven R&D

Unlocking Historical Knowledge to Discover Untapped Scientific Value

Alysia Eve, Head of Pricing and Commercialization, Progress Software & Eudald Camprubi, Co-Founder and former CEO, Nuclia will be talking through Data, Analytics & AI leaders.

Enhancing Agentic RAG for Data, Analytics and AI Leaders - Fireside chat

Discover how the addition of Progress Agentic RAG into the Progress portfolio is transforming the user experience by delivering a ready-to-deploy solution. This session will showcase out-of-the-box data acquisition, enrichment, and seamless integration into websites and business processes. 

Learn how agent-based retrieval enables real-time, on-demand structured information that complements your existing data fabric, mesh, and graph. Join us to explore how this powerful combination accelerates insight and enhances operational agility.

Introducing the Progress Agentic RAG Platform | Progress Data Platform Summit 2025

When MHC, a leading intelligent document automation SaaS solution provider, set out to deliver advanced AI-powered features to its customers, they ran into a familiar obstacle—critical data was locked away in legacy, on-premises systems, often behind firewalls. Traditional integration approaches were complex, inconsistent and costly.

In this on-demand webinar, you'll hear how MHC overcame these challenges to securely connect cloud applications to on-premises customer data with minimal development effort and no need for data replication or migration.

We’ll explore how MHC:

- Unified access to siloed data for real-time AI processing
- Built a secure, scalable hybrid architecture
- Reduced development overhead and sped up deployment
- Gained a flexible foundation for future AI innovation

Whether you’re a data architect, IT leader or part of a SaaS team, you'll gain practical takeaways and technical insights you can apply to your own hybrid data strategy.

A Customer’s Journey to Secure Cloud Access and Automation

Progress® MarkLogic® Server 12 incorporates next-generation search capabilities into the most flexible database to empower users and LLMs with the right information. 

Join the Product and Engineering teams for an interactive session as they unveil what’s new in MarkLogic Server 12. Get expert insights, live demos and practical guidance to help you plan your upgrade and get more value from your data platform.

What You’ll Learn: 

- How to use the latest search capabilities to maximize LLM outputs
- The benefits of Virtual Views for ad-hoc analytics, data discovery and more
- Ways to scale clusters up and down to stay resilient during periods of high demand

What’s New in Progress MarkLogic Server 12

Explore how to get the most out of your ProgressÒ SemaphoreÔ implementation and how it fits into the broader Progress Data Platform in this exclusive coffee chat with the Semaphore team.

This session features Jim Morris and Biju George, who will share practical insights, real-world examples and strategic advice. Following the discussion, there will be an open Q&A session.

In this “coffee chat”, you'll discover:

- Actionable ways to maximize the value of the Semaphore platform
- Real-world use cases and success strategies
- Key components of the Progress Data Platform and what's in store

Whether you're new to the Semaphore product, or looking to expand your usage, this session is designed to help you unlock more value and learn what’s possible when the platform is part of a connected data strategy.

View the on-demand webinar today!

Coffee Chat: How to Maximize the Value of Your Metadata Platform Investment

Connecting to REST APIs shouldn’t be a manual, code-heavy process. Progress DataDirect Autonomous REST Connector makes it easy to integrate any REST data source with any SQL-based application or BI tool—without coding.

This intelligent connector automatically interprets REST API responses and maps them to relational models, giving your business real-time, SQL-based access to even the most complex APIs.

In this short Coffee Chat, you'll learn how to:

- Build custom connectors for any API quickly
- Reduce dev costs and time-to-market using autonomous data connectivity
- Speed up analytics, BI and reporting with universal API access

View the on-demand webinar today!

Coffee Chat: Instantly Connect to Any REST API with Autonomous REST Connector

Innovation and research go hand in hand, and staying ahead in R&D requires the right tools. Generative AI is transforming the field, opening up new possibilities and applications.

Leveraging AI and Future Ready Data to Accelerate R&D

Financial fraud techniques are evolving in scale and sophistication. Modern fraudsters operate across identities, devices and geographies and exploit blind spots within siloed data systems, making it increasingly difficult for analysts to rely on traditional methods to detect suspicious activity.  

With regulatory pressure mounting, tightened customer protection requirements and collaborative institutional intelligence sharing are necessary for effective fraud prevention. This is where multi-model data management and visualization offer a powerful remedy to this complexity.

Join experts from Cambridge Intelligence and Progress for a live demo of advanced data visualization. You’ll learn how to:  

-> Unify your data intelligence into a single data platform to enable real-time collaboration and secure sharing across your organization and with external partners 
-> Visualize hidden fraud networks through intuitive interfaces to enable analysts to navigate and uncover complex relationship patterns other systems miss  
-> Accelerate investigation workflows that trace suspicious activity to transform weeks of manual investigation into hours of analysis

How to Prevent Financial Fraud with Real-Time Graph Analytics

Powering AI/ML & Analytics
with Smarter Data Engineering

AI/ML and advanced analytics are only as powerful as the data fueling them. Without a solid data engineering foundation, even the most sophisticated models and applications will fall short.

Join us for a special roundtable webinar to explore how smarter data engineering can accelerate AI/ML performance, streamline analytics, and enable real-time decision-making. Industry experts will share best practices for building scalable, efficient data pipelines that power intelligent applications.

You'll learn:

How to design high-performance data architectures for AI/ML
Strategies for optimizing data pipelines and reducing latency
Best practices for integrating real-time analytics into your stack
The latest tools and technologies transforming data engineering

Powering AI/ML & Analytics with Smarter Data Engineering

The publishing industry is facing mounting pressure to innovate with new digital products and monetize back catalogs. AI is challenging traditional workflows and user’s content consumption behavior, requiring new technology responses to keep pace with industry demands. 

Join Rodney Fulford, Assistant Director, Content and Technology Strategy, CAS and experts from Progress and Datavid as they explore how AI is transforming publishing systems and ways to use it to your advantage. 

Rodney will share the CAS data-centric approach to modernizing the content management pipeline and how they leveraged the Progress MarkLogic platform as a core technology to launch a new product line and accelerate time to market. 

In this session, you’ll learn about: 

• The importance of smart content and semantic search for question-answer systems and LLMs 
• Strategies to monetize catalogs through a portfolio of new digital products
• The power of data agility to meet demanding IT and business requirements within tight timelines

How Semantic Search and GenAI Unlock New Product Opportunities in Publishing

Software companies drive business and maintain market share by innovating and delivering the products their customers demand. For independent software vendors (ISVs), one critical piece of the software development process is the need for data access and connectivity to new types of data sources.

As new data sources appear, new connectivity methods become necessary. Software development and roadmap features must change quickly to support these needs. An embedded connectivity solution helps prevent product delays that could impact business’s bottom line.

In this webinar, you’ll learn why ISVs should consider embedded connectivity solutions. You’ll also hear how to:

- Reduce complexity while delivering a more comprehensive software solution
- Stay ahead of customer needs for reporting dashboards and analytics with an embedded data solution
- Enhance application performance and reliability

Learn how to gain essential connectivity and a competitive advantage in this on-demand session.

Why ISVs Embed Data Connectivity

Data architecture-as-a-service or DAaaS is a new self-service paradigm that is ideal for data meshes. It empowers local data owners to create architecturally compliant data repositories, domains, and pipelines without IT assistance. It is the culmination of self-service, where business units liberate themselves almost entirely from enterprise IT. If done right, DaaS eliminates data silos, reduces data bottlenecks, eases the burden on enterprise data teams, and empowers local domains to service their own data needs. It’s also a key ingredient in the data mesh, an emerging distributed architecture for data ownership and management. 
 
Data architecture-as-a-service is a verbal twist on cloud processing environments, such as software-as-a-service or platform-as-a-service. This moniker conveys that it’s possible to abstract architecture and build it into easy-to-use, customer-facing tools. When we abstract data architecture, we solve the most enduring data pain point in the data world: the proliferation of data silos and pipelines that wreak havoc on data consistency and trustworthiness.
 
You will learn:  
• What DAaaS is
• Why DAaaS is critical for governed self-service
• How DAaaS prevents data silos and empowers data domains

About the speaker:
Wayne Eckerson is an international thought leader in data and analytics who thinks critically, writes clearly, and presents persuasively about complex topics. He is a best-selling author, sought-after consultant, and noted speaker. Eckerson has advised a range of companies about how to implement data and analytics programs, architectures, and infrastructure, including Walmart, New Balance, and Children’s Hospital of Philadelphia. Eckerson is President of Eckerson Group, a consulting and research firm that helps organizations get more value from their data. He has degrees from Williams College and Wesleyan University.

Unlocking the value of data mesh with Data Architecture as a Service

Data architecture provides a blueprint for how an organizations can collect, store, manage, and utilize their data to meet business objectives. It defines the rules, standards, and guidelines for managing data as a valuable organizational asset and supports the integration of data from disparate sources into a coherent and usable whole. 

Anti-patterns of data architectures refer to common mistakes and pitfalls that data architects may encounter while designing and implementing data solutions. These anti-patterns can lead to suboptimal performance, data inconsistencies, and high maintenance costs. For example, over-normalization, complex data models among other specific characteristics. 

This webinar from data industry thought leader Dr. Pragyansmita Nayak will identify and analyze the most prevalent anti-patterns in data architectures, along with their root causes and potential solutions. By understanding these anti-patterns, data architects can systematically avoid them and design more effective and efficient data solutions while continuing to meet the needs of their organizations.

Speaker bio:
Dr. Pragyansmita Nayak is the Chief Data Scientist at Hitachi Vantara Federal (HVF). She explores the "Art to the Science" of solution architectures orchestrating data, APIs, algorithms and applications. She has over 24+ years of experience in software development and data science (Analytics, Machine Learning and Deep Learning). She has led projects for several Federal Government agencies (DoD/Civilian) in the domain of Federal Accounting, Operational Analytics, Data Fabric, Object Storage, Metadata management, Records Management and Data Governance.  She holds a Ph.D. in Computational Sciences and Informatics from GMU (Fairfax, VA) and Bachelors of Science in Computer Science. For more information on Pragyan's professional experience, please visit her LinkedIn profile at https://www.linkedin.com/in/pragyansmita and Twitter profile at https://twitter.com/SorishaPragyan.

Anti-patterns of data architectures

Many businesses are looking to gain more impactful insights out of their data. Snowflake is a modern data analytics platform many businesses use to help gain those insights. However, going from the starting point of knowing what you want your data to provide to actually extracting valuable business insights is challenging. Whether you’re trying to figure out how to collect all of your disparate data from various sources or you’re struggling to analyze your data effectively - it’s hard work. It takes a ton of time, resources, and expertise to create and build the infrastructure required to collect the data and analyze it effectively.

Luckily, Growth Acceleration Partners (GAP) can help overcome those challenges. GAP has a team with the knowledge, expertise, and experience to help your business get what you want out of your data using Snowflake and Microsoft Azure. Learn how GAP can help any company accelerate their timeline and get to valuable data insights faster.

What you’ll get out of this webinar:
- A detailed understanding of what it takes to build the infrastructure required for a modern data analytics platform with Snowflake and Azure
- A better understanding of why Snowflake is the right choice for your data platform needs
- An overview of how the GAPAccelerator helps you get value out of your data faster
- Technical details of how the infrastructure is built and what goes into
- Description of how the experts at GAP can work with your team to provide continuous data analysis after implementation

Accelerating Time to Value with Snowflake

When organizations build new applications and services, they often need scalable and lightning-fast data management platforms to support their innovations. What does it take to support new, innovative applications? Register for this tech talk to hear from Fern Halper, VP of Research at TDWI Research, who specializes in advanced analytics, along with representatives from Redis and Ekata, a Mastercard company, as they discuss one such use case: supporting Ekata’s smart identity verification solution to detect fraud. 

Presented by: 
• Henry Tam, Principal Solutions Marketing Manager | Redis
• Fern Halper, Ph.D., VP Research | TDWI
• Milena Babayev, Product Marketing Manager | Ekata
• Jason Frazier, Software Engineering Manager |Ekata

Real-time, Scalable Applications Powered by a Modern Data Platform

In today’s rapidly evolving business landscape data plays a critical role. Modern data architecture provides the tools and practices to harness the power of data and turn it into strategic advantage.  Whether it is data lakehouse, data fabric, data mesh or cloud data governance modern data architecture is enabling business leaders to make better decisions, quicker.
 
In this talk, Rajeev Pai, Director of Technology Strategy & Transformation at Deloitte, will explore what does modern data architecture entail and how it can be used to unlock business value. 
 
Key takeaways:  
- The need for newer ways for bringing, processing and distributing data in the enterprise. 
- Leading patterns and practices organizations are adopting in this journey with examples.
- Challenges organizations may face when adopting some of these new approaches.
- And how to overcome those challenges.

Whether you are a business leader looking to drive growth and innovation or a data professional seeking to help your business this talk will provide valuable insights from frontline and research.

About the speaker:
Rajeev is a leader in the space of Technology Consulting & Transformation with over 20 years of experience across the globe in the financial services industry, helping business leaders navigate change. He combines deep domain expertise in Capital Markets F2B Processes and Banking to provide thought leadership and tailored advise while challenging business leaders to achieve their vision. He is experienced in large scale System Integration, Platform Re-engineering, Enterprise Architecture, Enterprise Data Management (Data Strategy, Architecture and Management), Analytics & Data Visualisation, Operating Model constructs, Design Thinking, and has strong familiarity with Emerging Tech. He is a certified in Business Sustainability Management from the 
Cambridge Institute of Sustainability Leadership(CISL).

Data-driven success: How modern data architecture unleashes business value

Data lineage and knowledge graphs are data management capabilities that many companies would like to or have implemented. These capabilities assist in getting value from data and sustaining competitive advantage. However, several challenges with the implementation of these capabilities exist:
- Data management professionals have different views on these capabilities. Their definitions are not aligned within the data management community. These capabilities have a lot in common.
- The implementation of these capabilities is time- and resource-consuming. The correctly defined scope is one of the implementation success factors.
- Many companies still document data lineage and knowledge graphs manually in Excel.
- Plenty of various automated solutions exist. However, finding a proper solution is problematic as providers don’t have an aligned terminology to describe functionality. Often, quite differently labeled solutions deliver similar functionality.

In this session, we will discuss how to solve challenges and:
1. Demonstrate a metamodel of data lineage and knowledge graphs.
2. Show differences and similarities of these capabilities in terms of business drivers, architecture, and use cases.
3. Provide an overview and comparison of various data lineage and knowledge graphs IT tools.

About the speaker
Dr. Irina Steenbeek is a data management practitioner with more than 12 years of experience. The key areas of her professional expertise are the data management maturity assessment, implementation of data management frameworks, and data lineage. Irina has practical experience in software implementation such as ERP and DWH/BI, management consultation, financial and business controls, and data science.

Social links:
1. https://datacrossroads.nl/
2. www.linkedin.com/in/irina-steenbeek

Data Lineage & Knowledge Graphs: Similarities and Differences

As businesses strive to make data-driven decisions, choosing the right data platform has become a critical component in today's data-driven world. However, with a plethora of options available, it can be overwhelming to identify the best fit for your business. This webinar offers a comprehensive guide to the most popular data platforms, helping businesses evaluate critical factors such as data processing speed, scalability, cost-effectiveness, security, and integration capabilities to determine the ideal platform. 

Participants will gain insight into the advantages and limitations of both cloud-based and on-premise data platforms, and how different modern data platforms can support various business needs and objectives, such as data analytics, machine learning, and big data processing. By the end of this webinar, participants will have a clear understanding of which modern data platform is the best fit for their business context and objectives.

Key Takeaways:
1. Understand the strengths and weaknesses of the most popular data platforms available in the market today.
2. Learn how to evaluate crucial factors, such as data processing speed, scalability, cost-effectiveness, security, and integration capabilities while selecting a data platform for your business.
3. Recognize how different data platforms can support diverse business needs and objectives, including data analytics, machine learning, and big data processing.
4. Identify the advantages and challenges of cloud-based and on-premise data platforms and guidance on how to choose between them.

About the speaker:
Gopinath Manimayan is lead architect of digital experience at Moonraft Innovation Labs, he has over 15+ years of experience crafting exemplary technology solutions. You can always count on him for guidance with the best architecture to suit any need, or for learning about the latest technologies in the market.

Choosing the right data platform for your business

The problem with data warehousing and lakehousing is that they don’t go far enough. They don’t attack the root input problem: dumb, increasingly siloed and duplicative data and stranded, duplicated logic. It’s the proverbial garbage in, garbage out scenario, unless companies allocate their entire innovation budgets to integration.
 
More and more apps create the need for more and more reinvent-the-wheel integration, because each app has its own repository and its own data model. This is one reason why big banks have more than 10,000 databases each.
 
More and more apps with their own repositories is one reason we could be in the Yottabyte Era by 2030. If the world has to store two yottabytes of data per year, 40% of the economy could be dedicated to just storing data–90% of which is duplicated data that’s hard to reuse. 
 
For their part, applications duplicate the core description and predicate logic that should live with the data, where it can be shared and reused repeatedly within a knowledge graph.
 
A better, data-centric approach puts data and data models first. A FAIR data approach -- smarter data that’s designed to be findable, accessible, interoperable, and reusable–addresses the integration problem up front. No more garbage in, so no more garbage out. Data becomes self-describing with the help of the logic that used to be trapped in applications. 
 
This talk will examine how organizations in various industries use semantic knowledge graphs to solve their data integration and analytics problems at a fraction of the cost of data warehousing or lakehousing. It’s an organic approach to data and logic that can eliminate growing amounts of waste and complexity. Companies can run these systems at low cost in parallel with their legacy environments until they commit to graph data model-driven development, which is when even more substantial benefits will accrue.

FAIR data: Superior data visibility and reuse without warehousing

As companies build their data analytics practice, they quickly outgrow running analytics off their operational store that powers their applications. Building a read replica only buys them time until they hit scalability limits with their growing internal and customer demand. This is where one hits the crossroads of going all in with a cloud data warehouse or choosing an open data lake house approach to future-proof them for scale, performance, and cost efficiency. In this workshop, Matt Fuller and Tom Nats lead you through how you can easily build and manage an open data lake house architecture using open-source technologies such as Trino and Apache Iceberg to support your growing analytics. Trino is an open source highly parallel and distributed query engine built from the ground up at Facebook for efficient, low-latency analytics. Iceberg is an open source, high performant table storage format that enables an engine like Trino to perform data warehousing SQL functionality such as UPDATE, DELETE, and MERGE commands on the data lake house. In addition, Matt and Tom will lead you through combing these technologies to perform near real-time analytics with streaming ingestion with database functionality on the lakehouse. This workshop will use the Starburst Galaxy SaaS product making it simple to leverage these technologies for your modern data lake house without having to worry about the operational aspects of running Trino and other software.

Building an Open Data Lake House Using Trino and Apache Iceberg

Embedded analytics has become a vital aspect of business intelligence, enabling organizations to gain insights from their data at the point of decision, in their operational applications. 

This webinar will explore the architecture and experience of embedded analytics, showcasing how it can be effectively implemented to drive informed decision-making and enhance user experiences. Join us to learn about the various components, best practices, and real-world applications of embedded analytics in modern data architectures. With this webinar, you will:

- Understand the role of embedded analytics in the context of decision intelligence.
- Gain insights into the architecture of embedded analytics, including its components, scalability, performance and security.
- Explore best practices for designing effective embedded experiences.
- Discover real-world applications and use cases of embedded analytics across various industries.
- Stay informed on future trends and opportunities in the embedded analytics space including natural language.

Embedded Analytics: Architecture and Experience

Data Architecture Best Practices

Data management methods are evolving quickly, as enterprises invest in gaining data agility to accelerate insightful decision-making. Modern data management is being driven by an accelerated shift to data in the cloud and the subsequent innovation in data technologies and advanced analytics. Enterprises are recognizing new opportunities to derive value from their data and to save time and money, and they’re taking a fresh look at new data management approaches to reap the rewards of smarter and accelerated decision-making. 

In response to this demand for more modern data treatments, a confusing array of architectures, technologies, and approaches has sprung up – and it’s not easy to tell which ones will truly deliver better business outcomes and which ones are just hype. Should you invest in graph technology and metadata management? What exactly is a data fabric? How can you leverage the power of AI and machine learning? 

This session will take a look at trends in data management that are worth investigating, and explain how a modern data platform can help you implement them in a way that delivers business value for your enterprise.

Data Platform Capabilities for Modern Data Management Architectures

Data Architecture

Data Platform

Data Management

Data Visualization

Data Analytics

Data Fabric

Artificial Intelligence

Cloud Data

Data Integration

Data Insights

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Enterprise Applications

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Data Platform Capabilities for Modern Data Management Architectures

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Progress Data Platform | Power Trustworthy AI