Name: Real-time, Scalable Applications Powered by a Modern Data Platform
Start: 2023-04-19T17:00:00Z
End: 2023-04-19T17:01:01.000Z
Location: BrightTALK
Rating: 5

Redis makes apps faster, by creating a data foundation for a real-time world. It is the driving force behind Open-Source Redis, the world’s most loved in-memory database, and commercial provider of Redis Enterprise, a real time data platform. Redis Enterprise powers real-time services for over 8,000 organizations globally. It builds upon the unmatched simplicity and speed of Open-Source Redis along with an enterprise grade data platform that offers robustness of modern data models, management, automation, performance and resiliency to deploy and run modern applications at any scale from anywhere on the planet.

With so many people – like your CIO – hyped about artificial intelligence, machine learning, and generative AI products like ChatGPT, you’re likely under a lot of pressure to develop applications with those technologies for your company.

Like, yesterday.

But how do you turn terabytes of unstructured data – ranging from text to images, audio, and video – into useful information for the AI application?

The answer is a vector database. Vectorizing converts unstructured data into numerical representations known as vector embeddings, and Redis stores, indexes, and retrieves them. Vectors make searching for similar images or text or documents – vector similarity search (VSS) – faster and easier. Moreover, an external vector database with domain-specific, proprietary, or more recent data – used with public general purpose large language models – improves AI application responses.

Not to mention, keeping it from making things up.  
In this tech talk, find out how to use Redis’s vector database capabilities for real-time AI apps. We show the elements of building generative AI applications – using Redis technology you already know.

Watch to find out more about it.
* What’s a vector database
* Examples
* Why LLMs need real-time response
* Redis Enterprise VSS features and differentiators

Driving AI Innovation: Making It Easy to Build Generative AI Apps with Redis

In this tech talk, find out how to use Redis’s vector database capabilities for real-time AI apps. 

We show the elements of building generative AI applications – using Redis technology you already know.

Driving AI Innovation: Making it easy to build Generative AI Apps

Everyone wants to reduce costs and operate efficiently. But costs for database services can quickly get out of hand as you scale your applications.

Join Redis and Google for a virtual discussion on how Redis Enterprise can help reduce costs and deliver an efficient way to scale application performance on Google Cloud.

Cost Efficiency at Scale With Redis and Google Cloud

IT organizations are increasingly deploying fleets of Kubernetes clusters that need to be centrally managed. There is no shortage of options when it comes to deploying a management platform but managing Kubernetes clusters at scale, running everywhere from the network edge to the cloud, presents IT teams with a unique set of challenges.

Container Kubernetes Management

Everyone is looking to reduce costs and be more efficient. But as you scale your applications, costs for database services can quickly get out of hand.

Join this webinar to learn how Redis Enterprise can help reduce costs as the most cost-efficient way to scale application performance on AWS.

Cost Efficiency at Scale With Redis and AWS

While the application development world continues to thrive from the adoption of DevOps, data management at many companies today lacks the speed, interoperability, and flexibility desired by businesses to effectively compete on intelligence.

Data is still managed through rigid and, oftentimes, manual processes in siloed domains where collaboration is less constant.

There are numerous reasons organizations are hesitant to fully embrace Ops practices in the realm of databases. Concerns over data consistency, testing and deployment, legacy systems integration, and overall complexity can make introducing modern tools and practices challenging.

At the same time, plenty of organizations are finding ways to successfully bridge the gap between data management and DevOps. In fact, as the adoption of cloud-native apps, microservices, and containers continues to grow this will become ever more important.

Achieving Greater Agility: Emerging Trends in Data Management and DevOps

Join Redis Principal Solution Architect, Anton Umnikov, for a discussion on generative artificial intelligence, large language models, and databases. Anton will discuss how large language models have taken the world by storm. There will be specific emphasis on real use cases. While there’s a ton of hype, there’s also an explosion of real potential. Thanks to emerging first-party capabilities such as vector storage and search, Redis fundamentally changes how organizations leverage natural language processing, LLMs, GenAI models, and real-time machine learning workloads.

Redis Real-Time Data in Action from ChatGPT to Hello World!

In this webinar, speakers Sam Partee, Principal Applied AI Engineer, Redis and Microsoft’s Kyle Teegarden, Senior Product Manager, Azure Cache for Redis will walk you through it step by step. This session will provide a deep dive into how vector databases can be used in conjunction with large language models (LLMs) and demonstrate how to make a custom AI-based semantic answer engine entirely using Azure managed services.

Combining Redis with Azure OpenAI Service

Amid the tragedy, the COVID-19 pandemic presented unique opportunities for e-commerce firms, especially for online delivery services prepared to handle an escalating volume of orders. 

At iFood, one of the largest food delivery companies in Latin America, the technology team shifted into high gear as millions of new users began relying on its service, and thousands of new restaurants joined its growing platform.

Join experts from Redis and Amazon Web Services to learn how iFood uses Redis Enterprise Cloud on AWS as the foundation of its rapidly evolving ML feature store to create an optimal experience for users.

You will learn how iFood’s Redis-based infrastructure:
- Delivers sub-millisecond performance for ML operations
- Powers a highly efficient microservices architecture that optimizes the user experience
- Allows for massive scalability, enabling rapid corporate growth
- Saves money on infrastructure costs

Optimize the User Experience with AI Leveraging Redis Enterprise on AWS

Join us for this free instructor-led professional services workshop that shows you how to simplify your microservices architecture and build performant, resilient, and highly available microservices using Redis Enterprise.

After completing this session, you will:

Learn how to implement 3 caching design patterns to address read latency challenges while maintaining independent, highly available services.
Understand how to use Redis Streams as an asynchronous message broker for interservice communications. 
See how Redis Enterprise supports multiple Redis data types and models including JSON and Search for Domain Driven Design.
Understand how to simplify Change Data Capture with Redis Data Integration tool including demo.

Redis Enterprise Microservices Virtual Workshop

In today’s market everyone is looking to reduce costs and be more efficient. ElastiCache is a great solution for starting out, but can become difficult to manage at scale.

Join this webinar to learn about the differences between Redis Enterprise and ElastiCache and hear from Redis customer iFood on how they successfully moved their online feature store from Amazon ElastiCache to Redis Enterprise.

Cost Efficiency at Scale: Redis Enterprise vs. ElastiCache

Organizations must make data-driven decisions and respond quickly to changing customer needs in today’s rapidly evolving digital landscape. Customers are demanding intelligent applications that can keep up with their needs.

However, building these intelligent applications creates its own set of challenges. That’s where we come in.

These applications harness the power of artificial intelligence and machine learning, employing ultra-low latency databases to provide real-time insights, enabling organizations to better serve their customers.

Watch our on-demand session to learn how the joint technologies from Redis and Amazon Web Services (AWS) can help make it easier for your company to build these intelligent applications leveraging Redis Enterprise Cloud on AWS.

During this on-demand webinar, you will learn:
- What benefits customers who choose Redis Enterprise Cloud on AWS are seeing
- How to create applications with Amazon SageMaker and Redis Enterprise Cloud that can more accurately predict what your customers need
- How a unified database can help you stay ahead of the competition and better serve customers with real-time insights

Developing Real-time Intelligent Apps with Redis Enterprise Cloud on AWS

Data modeling can be challenging.  We will go through 8 different scenarios and show how to model them in Redis along with code snippets.

8 Data Modeling Patterns in Redis

Microservice-based architecture is an architecture style that helps development teams launch new products faster, scale more easily, and respond better to customer demands while minimizing costs and complexity. The result is that enterprises reduce time-to-market for new service development, often from projects measured in months to days. 

In a microservices environment, services running in real time must compensate for networking overhead. Redis Enterprise delivers sub-millisecond latency for all Redis data types and models. In addition, it scales quickly and linearly to almost any throughput needed.

MS Series: Getting Started With Microservice-Based Architectures

A microservice architecture can be a game-changer that helps organizations reduce barriers to application modernization and cloud migration journeys – and let them beat their competition to market.

However, breaking up a monolithic application into microservices usually creates a more complex system. Redis Enterprise reduces that complexity by caching data and optimizing data transfer between microservices – which offers the additional benefit of reducing latency and, consequently, improving performance.

Microservices also improve resiliency and provide read-optimized queries for global data, which means an organization can simplify its processes and automate them to reduce risk and make its architecture more mobile – without adding overhead.

The result is a single data platform for caching that supports optimal data models  — enough to support each microservice in isolation — and acts as a lightweight message broker for inter-service communication. This enhances developer productivity, provides faster time to market, and streamlines an application’s architecture.

This webinar shows how Redis Enterprise has helped customers simplify microservice-based architecture challenges in four ways:
- Query caching
- CQRS pattern
- API gateway caching
- Interservice communication

Getting Started Series: Simplify Microservice-Based Architectures with Redis

As one of the world’s most loved and used databases, Redis is likely widely used across your company. But the truth is; ineffectively deploying 
and managing Redis across large complex organizations can be challenging, time-consuming, expensive, and wasteful. 

Join John Noonan, Sr. Product Marketing Manager and Manish Arora, Solution Architectat at Redis to explore winning strategies to implement Redis at scale. Learn when, why, and how you can set up Redis-as-a-Service or establish a Redis Center of Excellence to:

●        Standardize deployments 
●        Simplify and centralize operations
●        Cut costs and reduce duplication of efforts
●        Improving service speed and quality

Winning Techniques to Deploy and Operate Redis at Scale

Hybrid Cloud gives you the best of both worlds: the advantages of the cloud and the control of local infrastructure. And thanks to the portability of Kubernetes, Redis Enterprise makes hybrid deployment easier than ever, on the cloud and on-prem.

Watch our Tech Talk to learn how to maximize flexibility and scalability, while reducing complexity and costs. In this session, we also show you:
- How a hybrid deployment increases flexibility while ensuring data security
- How Kubernetes with Redis Enterprise lets you easily scale up and down to handle spikes in throughput
- How Hybrid Cloud simplifies disaster recovery without dedicated backup IT Infrastructure

Join our experts to explore the easy way to deploy Hybrid Cloud.

Getting Started Series: The Easy Way to Deploy Hybrid Cloud

Vital data can end up locked in legacy databases because it seems too complex, time-consuming, and costly to unlock its full potential.

Not with Redis Enterprise. At this half-hour Tech Talk, you’ll see how to use the Redis Connect tool to quickly and easily ingest and replicate data from sources into Redis Enterprise.

Whether you’re replatforming to a hybrid architecture, migrating databases, optimizing Redis Enterprise, or enriching microservices, you’ll learn how to:
1. Understand architectural patterns and main use cases
2. Optimize performance with change data capture (CDC)
3. Deliver real-time data to employees, customers, and partner applications
4. Run ETL and searches in real time for your source applications

Tech Talk Series 5: Rapid Data Ingestion with Redis Enterprise

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

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

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

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

Data Management

Scalability

Innovation

Application Management

Analytics

Identification

Verification

Cyber Crime

Cyber Security

Smart Protection

The application development community features top thought leadership focusing on optimal practices in software development, SDLC methodology, mobile app development and application development platforms and tools. Join top software engineers and coders as they cover emerging trends in everything from enterprise app development to developing for mobile platforms such as Android and iOS.

Application Development

The unexpected nature of natural disasters and other disruptive events means that preparation is key to managing disaster recovery and business continuity planning. Join the business continuity and disaster recovery community for live and recorded presentations discussing best practices for business continuity planning, disaster recovery programs and business continuity management. Learn from BCDR experts to develop your critical infrastructure and gain insight into tactical solutions to your BCDR issues.

Business Continuity / Disaster Recovery

Practicing business intelligence allows your company to transform raw data into sets of insights for targeted business growth. The business intelligence and analytics community on BrightTALK is made up of thousands of data scientists, database administrators, business analysts and other data professionals. Find relevant webinars and videos on business analytics, business intelligence, data analysis and more presented by recognized thought leaders. Join the conversation by participating in live webinars and round table discussions.

Business Intelligence and Analytics

Join thousands of engaged IT professionals in the application management community on BrightTALK. Interact with your peers in relevant webinars and videos on the latest trends and best practices for application lifecycle management, application performance management and application development.

Welcome to the big data and data management community on BrightTALK. Join thousands of data quality engineers, data scientists, database administrators and other professionals to find more information about the hottest topics affecting your data. Subscribe now to learn about efficiently storing, optimizing a complex infrastructure, developing governing policies, ensuring data quality and analyzing data to make better informed decisions. Join the conversation by watching live and on-demand webinars and take the opportunity to interact with top experts and thought leaders in the field.

Big Data and Data Management

As an IT professional, many of the problems you face are multifaceted, complex and don’t lend themselves to simple solutions. The information technology community features useful and free information technology resources. Join to browse thousands of videos and webinars on ITIL best practices, IT security strategy and more presented by leading CTOs, CIOs and other technology experts.

Information Technology

Get powerful banking and treasury insights for your business. Connect with experts and colleagues to get the most up-to-date knowledge on hot topics such as digital banking, payments disruption and non-bank competition. Learn how the latest treasury management strategies are helping to mitigate operational, financial and reputational risk.

Banking and Treasury

Join this new, vibrant community to learn and connect with top thought leaders, startups and banks who are disrupting the financial services industry. Keep up with the latest news and trends in Fintech and take part in lively debates on topical issues and challenges.

Fintech

The accounting and finance community on BrightTALK features presentations from leading accountants and finance professionals. The accounting community includes tax planning strategies, QuickBooks webinars and other accounting webinars, while the finance community features presentations on financial capital regulations and forensic data analytics. Join the conversation by attending live financial and accounting presentations and connecting with industry thought leaders.

Finance

Get powerful insights to run your business. CEOs, CFOs, CMOs, COOs, CTOs and CIOs are among the leaders in this community who connect with peers and experts to grow their businesses.

CxO Strategy

In the business management community, commercial experts will be sharing webinars and videos on an array of subjects. From sales and marketing, to finance, productivity and growth, this content will help you stay up-to-date with the current economic environment and provide timely management insight to drive business growth.

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

Presented by

About this talk

Redis