10:45 AM
What’s Next in Data & Analytics Architecture
Length: 45 Minutes
Description: A recent survey of IT leaders found that the majority view hybrid or multi-cloud data warehousing as one of the most important data warehousing-related trends. Today, the question is not whether to move to the cloud, but rather, which cloud platform is best for each organization’s needs.
Title: Learn, Unlearn, Relearn: Embracing the Future of Cloud Analytics
Time: 10:45 AM - 11:30 AM
Description: Companies now collect more data than ever before, but challenges remain for accessing and analyzing them. Armlin suggests learning about the forces changing enterprise data architectures, unlearning the shortcomings of current architectures, and relearning a powerful new approach to data analytics in the cloud.
Title: Journey to Cloud Analytics: How Companies’ Analytics Challenges Can be Solved by Moving to the Cloud
Time: 10:45 AM - 11:30 AM
Description: When moving analytics and BI workloads to the cloud, companies must develop, design, and deliver their data analytics differently. Faced with so many options to choose from, however, companies are unsure exactly what their analytics journey to the cloud will entail. Hoblitzell explains the key benefits and advantages of moving to the cloud, new capabilities for analytics, and what to look for when considering a partner to assist with an analytics cloud migration.
The Future of Data Warehouses, Data Lakes, & Data Hubs
Length: 45 Minutes
Description: The coming decade is going to require a modern data warehouse to meet demanding new requirements for machine learning, data variety, and real-time analytics—while still satisfying the more traditional need for analysis of structured data at scale.
Title: How to Select Your Cloud Data Warehouse Platform Strategically
Time: 10:45 AM - 11:30 AM
Description: Vendor claims to the contrary, data warehouse scale, performance, and operational issues are trickier than ever in the cloud. Choosing the wrong cloud database can result in millions of dollars of excess cost, unacceptable performance problems, or a drastically compromised database design. Winter provides a concise, strategic view of the cloud data warehouse landscape, highlighting how cloud database engines differ and how to choose one that will work for you. This will help avoid platform mistakes that can get you into deep trouble in the coming years.
Title: Making the Case for Legacy Data in Modern Data Analytics Platforms
Time: 10:45 AM - 11:30 AM
Description: Modern data analytics platforms that fuel enterprise-wide data hubs are critical for decision making and information sharing. The problem?
Integrating legacy data stores into these hubs is just plain hard, and there is no magic bullet. However, the best data hubs include all enterprise data. This session explores best practices for integrating legacy data sources, such as mainframe and IBM, into modern data analytics platforms such as Cloudera, Databricks, and Snowflake.
Database & DevOps Boot Camp
Length: 45 Minutes
Description: DevOps and databases share many common characteristics. They shouldn’t be positioned as being at odds with each other.
Title: What Is DevOps & Why Should DBAs Care?
Time: 10:45 AM - 11:30 AM
Description: You may have heard the term “DevOps” a lot lately, but is this just one of those buzzwords that gets thrown around and means something different depending on who’s talking? While traditional software methodologies pit developers and operations folks against each other, DevOps requires that they work together for a common goal. And, ultimately, shouldn’t the software project’s success be everyone’s goal? Come and learn how DevOps is changing the DBAs world for the better.
Title: How Data Professionals Can Participate in DevOps
Time: 10:45 AM - 11:30 AM
Description: Those of us who administer production databases have had to endure significant change relative to how the databases and applications we support are managed. This session introduces DevOps concepts and explain what their impact is on how database administrators and developers manage their infrastructures.
AI & Machine Learning Summit
Length: 45 Minutes
Speaker(s):
Bashyam Anant, Senior Director, Product Management, Sumo LogicDescription: Data within the enterprise is useless if it can’t be found and used. AI and ML provide some pathways to data discovery that give companies competitive advantage.
Title: Accelerating Your DevOps Journey With ML
Time: 10:45 AM - 11:30 AM
Description: Machine learning is both a buzzword and the Holy Grail, depending on how you use it. Enterprise cloud companies use machine learning to accelerate or supercharge their data journey—it helps them work at a faster pace, with more efficiency and greater accuracy. Once AI is fully in use, teams need to be able to answer this question: How do we know if this is working correctly? In order to do this, teams must extend their existing observability approaches to cover their AI and ML capabilities. Today, there’s a big gap there, and that creates risk as organizations can’t manage those assets properly. This session takes a deep dive into how companies utilize machine learning to take every competitive advantage to advance their ability to use all the data at their disposal.
11:45 AM
What’s Next in Data & Analytics Architecture
Length: 45 Minutes
Description: The Internet of Things (IoT), once an emerging space, is quickly transforming industries by enabling machinery and products with network connectivity. From optimizing industrial machinery and manufacturing processes to powering connected cars and healthcare equipment, IoT-centered innovation is bringing about a future powered by data.
Title: Solving the IoT Data Management Puzzle With Gateways to the Cloud
Time: 11:45 AM - 12:30 PM
Description: As sensor technology becomes more affordable, companies of all sizes will have the ability to embrace IoT strategies to build innovative products and services and establish new revenue streams. Yet, as with any promising technology, challenges remain. To reap the full value of IoT devices, companies need to migrate petabytes of IoT data quickly and securely to the cloud. As organizations across industries explore IoT offerings to strengthen consumer experiences and build new revenue streams, they must first understand its unique data management and orchestration challenges. This presentation explores best practices for activating data from the edge to cloud environments and activating this data to build new revenue streams.
The Future of Data Warehouses, Data Lakes, & Data Hubs
Length: 45 Minutes
Speaker(s):
Mark Lyons, VP Product Management, DremioDescription: As data storage and analytics in the cloud continue to grow, organizations are evaluating the essentials for success, such as scalability, efficiency, affordability, and security.
Title: Building the Open Data Lakehouse
Time: 11:45 AM - 12:30 PM
Description: Data consumers need data for BI and analytics to make business decisions. But for most organizations, their current data infrastructure isn’t keeping up with demand. Developing analytics and getting them into production takes weeks to months. Plus, data tied to proprietary formats makes it difficult to support different types of analytics, such as BI and data science. Data teams struggle with brittle data pipelines, stale data, slow turnaround, and increasing costs. A data lakehouse built on an open data architecture enables data users to access data in their data lake directly via SQL queries, simplifies complexity, and makes life easier for data teams. Learn why more organizations are moving their analytics and BI to an open data lakehouse and how you can build a successful lakehouse strategy.
Database & DevOps Boot Camp
Length: 45 Minutes
Speaker(s):
Brian Gilmore, Director, IoT and Emerging Technology, InfluxData Andrew C. Oliver, Senior Director of Product Marketing, MariaDB CorporationDescription: The care and feeding (aka management) of databases takes on new meaning in the Internet of Things era.
Title: Rethinking the Database in the IoT Era
Time: 11:45 AM - 12:30 PM
Description: The internet has evolved from a human-centric, client-server-based architecture to one where humans and assets (or things) are equal stakeholders. Do our databases, middleware, and client applications still stand up? Gilmore outlines the unique challenges of data operations and analytics in the IoT environment, examines how human and machine interactions drive architecture and deployment, and identifies where we could work to improve our data strategies to fully leverage the IoT opportunity.
Title: Scaling Both Writes and Reads with Distributed SQL
Time: 11:45 AM - 12:30 PM
Description: Popular wisdom is that cache is king and you can easily scale by fronting your database with caching services like Redis. Moreover, you can scale out your relational database with read replicas. Finally, if that doesn't do it, you can choose a NoSQL database. However, what do you do when you have a lot of writes and a lot of reads, data integrity is critical, and downtime is a nonstarter? A new technology called distributed SQL borrows the best from both relational and NoSQL databases giving you both read and write scale while also ensuring the data is correct. As critical systems, financial systems, and the entire back office moves to the cloud, distributed SQL is key to ensuring data is consistent, available and scalable.
AI & Machine Learning Summit
Length: 45 Minutes
Description: A single view of the customer, powered by AI and ML, can help identify fraud, personalize recommendations, and contribute to outstanding customer care encounters.
Title: Customer 360 in a Box
Time: 11:45 AM - 12:30 PM
Description: With the advent of omnichannel, leveraging customer data has become paramount. In a behemoth such as Walmart, each customer’s identity exists as a silo rather than a single customer from the company perspective. Handling customer data for privacy, regulations, and deprecation becomes challenging with every new ID introduced in the system. A streaming ML platform that seamlessly combines data belonging to the same customer on a single box and runs ML models, which use this data as features, leads to a single view of a customer. Brar discusses the platform (built on Kafka ecosystem) and its important aspects.
2:00 PM
What’s Next in Data & Analytics Architecture
Length: 45 Minutes
Description: It is the goal of many organizations to continuously leverage intelligence from fast-moving event streams to help automate business decisions. Learn the best practices for utilizing streaming data to support modern applications.
Title: Automating Business Decisions With Continuous Intelligence Using Event Streams
Time: 2:00 PM - 2:45 PM
Description: To deliver continuously useful insights, apps always need to have an answer from the latest data. Algorithms have to analyze, train, and predict continuously, and each new event must be analyzed in real time as soon as it arrives. As a result, insights and predictions are necessarily “given data thus far,” and the outputs therefore also form a real-time stream. This session provides a working example using streaming events from Apache Kafka and show attendees how to build applications that analyze, learn, and predict on-the-fly. This approach enables applications to self-assemble from streaming data, and applications are millions of times faster than “microservice plus database” architectures. It also gives developers new ways to ensure timely responses and to manage the effects of partitioning on event-driven applications.
The Future of Data Warehouses, Data Lakes, & Data Hubs
Length: 45 Minutes
Description: New ways to store data and leverage it in different ways are being utilized by data-driven organizations hungry for flexibility and scale.
Title: Finding the Hidden Value in Data Lakes
Time: 2:00 PM - 2:45 PM
Description: Long associated with Hadoop, in a cloud world the data lake is often ignored in favor of its more fashionable cousins—mesh, fabric, lakehouse, and warehouse. But ignore the data lake at your peril, as it has an important role to play in any modern analytics strategy. Jablonski focuses on the evolving role of the data lake with a particular emphasis on: Why a data lake is a critical component of any cloud analytics project; the role of the data lake in the battle over ETL vs ELT; the importance of metadata in data platform design; and how a data lake helps deliver business value, not just technical success.
Database & DevOps Boot Camp
Length: -675 Minutes
Speaker(s):
Michael Corey, Co-Founder/Chief Operating Officer, LicenseFortress Don Sullivan, Product Line Manager, Broadcom (VMware)Description: Licensing has always been tricky but the rise of trolling has created many new things to guard against.
Title: Beware Software License Trolls, the New Danger
Time: 2:00 PM - 2:45 AM
Description: Any organization that has gone through a vendor software licensing audit knows firsthand how expensive and draining a process can be for an organization. No one disputes that companies like Oracle, Microsoft, or IBM have a right to be fairly compensated for the use of their software. The same safeguards these organizations put in place to protect their intellectual property can easily be distorted by a software troll against an unsuspecting company to extort millions of dollars. Learn about software license trolls and simple steps to take to ensure your organization is not their next victim.
AI & Machine Learning Summit
Length: 45 Minutes
Description: As the world becomes increasingly data-driven, AI/ML algorithms are being incorporated in most business applications.
Title: The Era of Distributed AI Architectures
Time: 2:00 PM - 2:45 PM
Description: Historically, data in AI architectures was moved to a central location to perform both model training and inference. This centralized approach is becoming untenable due to cost, performance, and privacy reasons. In this talk, Kaladhar shares his thoughts on next-generation distributed AI architectures and presents the concepts of “AI Marketplaces” and “Federated AI” to demonstrate how these concepts are an integral part of distributed AI architectures.
3:00 PM
What’s Next in Data & Analytics Architecture
Length: 45 Minutes
Description: Product teams must realize the need for instrumenting the product to collect accurate data. Most teams tend to neglect the instrumentation process and later scramble to gather insights from data. If product teams don’t include or prioritize instrumentation as part of their road map, they run a risk of not capturing user behavior.
Title: How to Use In-App Ratings to Personalize and Improve Your User Experience
Time: 3:00 PM - 3:45 PM
Description: App Store ratings aren't very helpful when it comes to better understanding customers. In-app ratings and feedback, however, can provide valuable insights into the behaviors and preferences of your users. In this session, you'll learn how to use in-app feedback to personalize and improve your user experience, including through the development of Artificial Intelligence models.
The Future of Data Warehouses, Data Lakes, & Data Hubs
Length: 45 Minutes
Description: For data to be useful, it must be trusted. It is important to standardize data, track its lineage, ensure that it is high quality, and verify that it is appropriate for the organizational roles or applications that leverage it.
Title: Mapping Data Quality Concerns to Data Lake Zones
Time: 3:00 PM - 3:45 PM
Description: A common pattern in data lake and lakehouse design is structuring data into zones, with bronze, silver, and gold being typical labels. Each zone is suitable for different workloads and different consumers. For instance, machine learning algorithms typically process against bronze or silver, while analytic dashboards often query gold. This prompts the question: Which layer is best suited for applying data quality rules and actions? The answer: All of them. This presentation delves deeper into this answer, describing the purposes of the different zones and mapping the categories of data quality relevant for each by assessing its qualitative requirements. Bryson also covers data enrichment—the practice of making observed anomalies available as inputs to downstream data pipelines.
Database & DevOps Boot Camp
Length: 45 Minutes
Description: [This session has been canceled due to unforeseen circumstances. We apologize for any inconvenience.]
The notion of data exhaust, all that peripheral data floating around in your organization, can have value to your business operations. It could help with predictive analytics, for example, or customer analysis. But as big data gets even bigger, are we exhausted by all the data at our fingertips? At what point does technology step in to help and which technologies are best suited to deal with data exhaustion? That’s what this panel considers.
AI & Machine Learning Summit
Length: 45 Minutes
Description: Putting AI to work to improve healthcare requires having technology and infrastructure coupled with the right people.
Title: Insights for Unlocking the Potential of AI in Healthcare
Time: 3:00 PM - 3:45 PM
Description: Healthcare innovators are innovators are prioritizing AI and operationalizing it for better performance, better outcomes, and better patient experience. But getting into predictive and prescriptive analytics takes many of them out of their comfort zones. Yet with every opportunity for improvement, there is a risk that organizations won’t have what it takes to successfully develop, implement, or operationalize AI. Having the technology and infrastructure for AI is not enough. Having the right people—with the technical capability and healthcare expertise—is enormously important. In this talk, Mehrotra and Fernando provide insight into strategies and plans to attract and retain the talent needed to unlock the potential of AI.
