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Instructional Demos
A Customized HPCC Systems Cloud Deployment for Your Company
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A Customized HPCC Systems Cloud Deployment for Your Company
Jon will discuss the approach and methods used in a custom Azure cloud deployment, based on our own company experience. This includes how to perform robust deployments of HPCC Systems environments on the cloud, with a focus on security, maintainability and supportability, and using the precepts of zero touch and zero trust.
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Instructional Demos
All About the HPCC Systems Metrics Framework
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All About the HPCC Systems Metrics Framework
This presentation is for anyone interested in HPCC Systems metrics. It covers a description of the metrics framework and how its components operate, a brief explanation of how HPCC Systems components are instrumented for metric collection, configuration using helm charts, and a discussion of how HPCC Systems is planning on using metrics in areas of cluster health and scaling.
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Instructional Demos
A Simple HPCC Systems Cloud Deployment for Open Source Users
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A Simple HPCC Systems Cloud Deployment for Open Source Users
This talk will cover how to setup and implement a basic HPCC Systems cluster in the cloud using Azure Kubernetes. We will walk through the deployment configuration leveraging Terraform, GitOps/Flux2 and storage settings. Note: This talk is intended for the wider open source HPCC Systems community. It is advised to check with your organization for any specific security protocols.
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Proven Use Cases
Athlete 360: Leveraging HPCC Systems and RealBI for Athlete Wellness and Performance
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Athlete 360: Leveraging HPCC Systems and RealBI for Athlete Wellness and Performance
There is a lot that plays into an athlete being able to perform at their best when it matters most. Not only are there physical demands, but factors that come from outside of their sport that affect their wellbeing and readiness to perform. In team sports, there are many external variables that cannot be controlled, which makes the process of gauging performance of individual athletes difficult. The better the understanding of what an athlete does and how their body responds, the better we can support them to be at their best. Within collegiate athletics, and sports in general, there is a struggle to be able to interpret data from different streams together in a single report. Furthermore, streamlined data collection, can further aid our understanding of what an athlete does and how their body responds. This involves data from all aspects of an athlete’s day including wellness questionnaires, practice training loads, weight room training loads, and weight room assessments of strength, power, and fatigue. In the past we have shown the impact of using HPCC Systems with the NC State Men’s soccer team. Here you will see some solutions using HPCC Systems and RealBI to provide insight from data collected with the NC State Women's basketball team as well as how this system can serve not only the Strength and Conditioning department, but the athletics department as a whole.
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Keynotes & Plenary
Awards Ceremony, Academic Panel Discussion, and Wrap-up
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Awards Ceremony, Academic Panel Discussion, and Wrap-up
Community Recognition & Poster Awards Ceremony: Join us as we announce the recipients of the 2021 HPCC Systems Community Recognition and David Kan Ambassador Awards. Winners of the 2021 Poster Competition will also be unveiled. 
Panel Discussion: Academia and Industry – BFFs (Best Friends Forever) - Industry and Academia have a long and prosperous partnership history, providing mutual benefit through project collaboration, real-world opportunities for students, and preparation for the next generation of professionals into the workforce. This partnership needs work from both sides to thrive, needing mutual respect, equal contribution, and alignment to goals and outcomes. This discussion will be focused on the opportunities and challenges of this partnership. Our guest panelists will feature industry and academic experts talking through their experience in establishing value, best practices, pitfalls, and methods to ensure a successful long-lived relationship. This panel discussion will be moderated by Bahar Fardanian, Technology Evangelist, LexisNexis Risk Solutions Group, who works closely with our community partners.
Wrap-up & Adjourn: Flavio Villanustre closes the exciting day with a wrap-up and thank you to our Community.
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Platform Features
Cloud Security & Authentication in HPCC Systems
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Cloud Security & Authentication in HPCC Systems
This talk will cover a discussion of some current and future technology enhancements around HPCC Systems platform security, with a primary focus on cloud deployments. This session will include a look at support for mutual transport layer security between internal components within an HPCC Systems environment, external facing TLS for securing access into the HPCC Systems environment, using cert-manager to generate TLS certificates for HPCC Systems services and components, installing externally created TLS certificates, secrets management, and a look at our plans for future support of OAUTH2 based authentication and authorization, with an initial focus on support for OAUTH2 integration with Azure Active Directory services.
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Machine Learning
Contributions to HPCC Systems - From Virtual Collaboration to Virtual Reality
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Contributions to HPCC Systems - From Virtual Collaboration to Virtual Reality
This talk focuses on the virtual collaborative work done between RV College of Engineering and LexisNexis Risk Solutions on recent contributions to the HPCC Systems Platform. These include plugins and extending Machine Learning bundles for HPCC Systems, followed by analysing the impact of skewed data distributions on most commonly used ECL operations. The talk concludes with case studies executed on HPCC Systems, including the implementation of a virtual reality application.
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Proven Use Cases
Cooperative actions between University of São Paulo and LexisNexis Risk Solutions
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Cooperative actions between University of São Paulo and LexisNexis Risk Solutions
Prof. Renato will discuss the successful conjoint initiatives being held between University of São Paulo (USP) and LexisNexis Risk Solutions in Brazil for leveraging HPCC Systems for teaching & learning, research and extensions activities in academia, including recent machine learning projects.
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Hands On Learning
Cruising the ML World with HPCC Systems - Part 1 – Introduction/Unsupervised Learning
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Cruising the ML World with HPCC Systems - Part 1 – Introduction/Unsupervised Learning
Getting Started with the ML Bundles, Preparing Your Data, Exploring DBSCAN and K-Means.

A three-hour workshop, Cruising the ML World with HPCC Systems, is open to all attendees who want to explore the Machine Learning Bundles of HPCC Systems. Code examples and lesson materials will be included. Attendees can attend any one-hour session or the whole workshop. All three sessions will be recorded for playback. Prerequisites: This workshop assumes that the attendee have a basic understanding of HPCC Systems and ECL. We recommend that you complete our online Introduction to ECL Courses (Part 1 and 2) and review client setup instructions available in GitHub (https://github.com/hpcc-systems/Community-Workshops) before attending.
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Hands On Learning
Cruising the ML World with HPCC Systems- Part 2 – Supervised Learning
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Cruising the ML World with HPCC Systems- Part 2 – Supervised Learning
Part 2 – Supervised Learning Exploring Linear and Logistic Regression A three-hour workshop, Cruising the ML World with HPCC Systems, is open to all attendees who want to explore the Machine Learning Bundles of HPCC Systems. Code examples and lesson materials will be included. Attendees can attend any one-hour session or the whole workshop. All three sessions will be recorded for playback. Prerequisites: This workshop assumes that the attendee have a basic understanding of HPCC Systems and ECL. We recommend that you complete our online Introduction to ECL Courses (Part 1 and 2) and review client setup instructions available in GitHub (https://github.com/hpcc-systems/Community-Workshops) before attending.
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Hands On Learning
Cruising the ML World with HPCC Systems- Part 3 – Neural Nets, Deep and Ensemble Learning
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Cruising the ML World with HPCC Systems- Part 3 – Neural Nets, Deep and Ensemble Learning
Part 3 – Neural Nets, Deep and Ensemble Learning Exploring GNN and Boosted Trees. A three-hour workshop, Cruising the ML World with HPCC Systems, is open to all attendees who want to explore the Machine Learning Bundles of HPCC Systems. Code examples and lesson materials will be included. Attendees can attend any one-hour session or the whole workshop. All three sessions will be recorded for playback. Prerequisites: This workshop assumes that the attendee have a basic understanding of HPCC Systems and ECL. We recommend that you complete our online Introduction to ECL Courses (Part 1 and 2) and review client setup instructions available in GitHub (https://github.com/hpcc-systems/Community-Workshops) before attending.
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Data Lake
Data Cataloging with Tombolo
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Data Cataloging with Tombolo
It is easy for a Data Lake to grow out of control if appropriate measures are not put in place. When this happens, Data Engineer’s productivity can suffer, resulting in delays in customer commitments. A Data Lake can become a Data Swamp suddenly and without warning. The critical threshold is reached when the complexity of the Data Lake exceeds the capability of key personnel to hold the pattern of the Data Lake in their head. The goal of Tombolo, a Data Lake Curation tool, is to prevent such an event and allow the data lake to continue evolving rapidly as its complexity increases and as more personnel begin to participate. Tombolo provides the central operating environment for a Data Lake. The Tombolo Data Lake Curation System 1.0 is the first open-source Data Lake Curation system for the HPCC Systems Platform. It allows creation of documentation along with the data and analyses that provides a roadmap into all aspects (assets) of the Data Lake: Data Files, Data Providers and Consumers, Data Ingestion and Analytics, and User Queries. Its global find facility allows users to rapidly locate any asset, or browse hierarchically to get the lay-of-the-land.
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Data Lake
Data Visualization with RealBI
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Data Visualization with RealBI
RealBI is a new HPCC Systems business intelligence tool, used to empower HPCC Systems developers to shape and visualize their data in real time, regardless of the size of that data. RealBI saves users time and cost by communicating directly with HPCC Systems clusters. This eliminates the need to further secure or transport the data since it remains entirely within the cluster. RealBI gives users direct access to logical files and ROXIE queries. It also enables users to write and execute custom ECL scripts from within the application if that is desired. Users don’t need programming skill to use RealBI. All charts, filters, sorting, and many more options, are all available with a click of the mouse.
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Proven Use Cases
Deploying Digital Human Readers Leveraging HPCC Systems
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Deploying Digital Human Readers Leveraging HPCC Systems
With the newly launched NLP-Plugin for HPCC Systems and VSCode NLP Language Extension, the community now has the ability to incorporate human-like “digital readers” into HPCC Systems to mine information from free text that has up until now, been impossible to extract. Future projects will be discussed including reading radiology reports, business reports, and real estate documents the latter of which could open new markets across the industry. It is important for everyone to understand this new technology in order to spot potential applications for extracting unmined data that until now, was impossible to obtain. Sharing our own use case, the end goal is to create a NLP Center of Excellence that will serve the entire company with digital readers first in English, then, other languages to open new streams of revenue.
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Data Lake
Design Considerations for Migrating Your HPCC Systems Data Lake to the Cloud
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Design Considerations for Migrating Your HPCC Systems Data Lake to the Cloud
During this session, we will share lessons learned and design best practices through our own cloud migration experience. The beginning of our presentation will be a simple installation of our cluster on Azure using the community helm charts. During this demo we will hit topics such as how the HPCC Systems platform differs between the Kubernetes cluster that we are deploying and the bare metal installations that community members are familiar with. We will dive into helm for HPCC Systems, the value of .yaml files and a few different ways that the cluster can be configured, and explain storage in the cloud compared to bare metal. We will then talk about ROXIE and Thor usage in the cloud. Krishna will cover some details about getting query lists, suspended queries, and doing package file deployments. Michael will expand on basic security features that end users will want to enable in the cloud, including encryption in transit and at rest in a cloud environment such as Azure.
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Instructional Demos
HPCC Systems Logging in the Cloud and an Elastic Stack Solution
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HPCC Systems Logging in the Cloud and an Elastic Stack Solution
As HPCC Systems continues its journey to the cloud, one major challenge faced is the ephemeral nature of log data and the accessibility of distributed application-level logs. This presentation discusses these challenges, the HPCC Systems logging architecture, and a simple Elastic Stack-based solution to the challenge. We'll demonstrate in detail the end-to-end solution, which includes Helm-based deployment, Kibana configuration, HPCC Systems log exploration, querying, and filtering. We'll also discuss an advanced topic that improves log data query performance by utilizing Elastic Search Ingest Pipelines. Finally, we'll touch on other possible solutions such as Azure Log Analytics.
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Proven Use Cases
HPCC Systems Thor Monitor - Using Workunit Services and Power BI to Monitor Thor Activity
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HPCC Systems Thor Monitor - Using Workunit Services and Power BI to Monitor Thor Activity
The ECL Workunit Services standard library functions can be used to capture details about workunits running on Thor including processing time, errors, current state, and more. Capturing these details allows for monitoring, trending, error analysis, degradation, and other data points that can help improve the efficiency of your Thor environments. We will look at how to use this information to monitor the system with visualizations in Power BI.
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Machine Learning
HSQL: An SQL-like Language for HPCC Systems
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HSQL: An SQL-like Language for HPCC Systems
There is a steep learning curve to getting used to handling Big Data, especially in distributed systems, where the task of data processing is split amongst various nodes in clusters. HSQL is the new big-data query language of HPCC Systems and is an innovative and open-source solution to let users process their data at any scale. It is designed to work in conjunction with ECL which is the primary programming language for HPCC Systems, and it should prove itself to be easy to work with and robust for general purpose analysis. Made to provide a compact and easy to comprehend SQL-like syntax for performing visualizations, general data analysis, training of Machine Learning models, HSQL allows a modular structure to such programs and can easily integrate with VS Code IDE. In this presentation, learn why HSQL is important and how it adds more value to HPCC Systems users, its syntax, and see a couple of examples on different datasets and its installation and setup instructions.
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Machine Learning
New Advancements to Logistic Regression and the ML Library
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New Advancements to Logistic Regression and the ML Library
Logistic Regression is one of the most important analytic tools in the social and natural sciences such as natural language processing and image recognition. One of our Machine Learning advancements is to renovate the current HPCC Systems Logistic Regression bundle and add the ability to handle both binary and multi-classes predictions tasks. Another advancement is to improve the performance and remove the bottlenecks of the Preprocessing bundle. The improved version is more scalable and more efficient for Big Data preprocessing tasks.
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Proven Use Cases
Processing Student Image Data with Kubernetes and HPCC Systems GNN on the Cloud
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Processing Student Image Data with Kubernetes and HPCC Systems GNN on the Cloud
In order to foster a safe learning environment, measures to bolster campus security have emerged as a top priority around the world. In this session, I will share how HPCC Systems was leveraged to process student images with Kubernetes running on the Cloud Native Platform while utilizing the Generalized Neural Network (GNN) bundle for image classification. The result is a trained model which can be implemented on the autonomous security robot we built to help campus security personnel identify visitors, students, and staff.
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Platform Features
ROXIE Troubleshooting
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ROXIE Troubleshooting
ROXIE services on cloud/Troubleshooting: What changes will need to occur in the ROXIE code to run on the cloud native platform?
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Instructional Demos
Running a Containerized HPCC Systems Platform Locally
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Running a Containerized HPCC Systems Platform Locally
This session will include a demo where you can learn how to start up and use a containerized single-node HPCC Systems Platform cluster on a local machine running Docker Desktop or Minikube to use for ECL Development, testing, or training. This provides a platform for hands-on experience with HPCC Systems to experiment with and even create real-world data analytics applications--all on your desktop or laptop PC.
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Platform Features
Securing Your Cloud Native HPCC Systems with Service Mesh
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Securing Your Cloud Native HPCC Systems with Service Mesh
With the advent of Cloud and Kubernetes over the years, machines are no longer considered as attached to a data center. Machines are more ephemeral than ever before. The traditional architecture of the HPCC Systems environment harnessed the physical storage of each node and that in turn gave certain performance benefits. But with cloud, it is no longer necessary to have a fixed machine for a process. The true power of cloud can be harnessed only when we treat them as ephemeral. Therefore, a Thor worker node which is always on in a traditional HPCC Systems environment is spun up only when it is required in a cloud environment. With the latest cloud native version of HPCC Systems, we now have the flexibility to spin up the clusters only when required. In this session, we will cover how the latest cloud native platform is different from the bare metal version, explain service mesh and how it fits into the HPCC Systems scheme of things, and a comparison of service mesh Istio and Linkerd.
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Data Lake
Taming the Data Demon with the DataSeers HPCC Systems Appliance
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Taming the Data Demon with the DataSeers HPCC Systems Appliance
The core of any data solution lies in data management. What is needed is a solution that will integrate and coordinate compliance, reconciliation, fraud monitoring, and visualization. Hear from the DataSeers experts how they are helping companies in the FinTech and Banking industry to manage money, fight fraud and maintain compliance using a solution built from the ground up leveraging HPCC Systems.
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Data Lake
Terasort with HPCC Systems on Azure Kubernetes Service and High Performance Storage
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Terasort with HPCC Systems on Azure Kubernetes Service and High Performance Storage
The speakers will discuss challenges, AKS considerations and storage options, including a demo covering the setup and configuration of HPCC Systems on AKS with Blob NFS 3.0 and performing a Terasort.
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Machine Learning
The Causality Analytics Toolkit for HPCC Systems
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The Causality Analytics Toolkit for HPCC Systems
Causal Reasoning is at the heart of most human thought and action, yet has only recently been formalized as a mathematical and scientific field of study. It is hard to conceive of achieving a true AI without such a capability. Although the science of Causality has not advanced to the threshold of AI, it can unlock capabilities that are beyond the realm of statistical observation. Current Machine Learning methods assess observational patterns, and learn to replicate the results of patterns previously detected. They make no effort to disentangle true causal effects from observed correlation. They lack the ability to respond to changes in the scenarios that generated the data, or to predict the effect of new actions on the outcome. Causal Science provides a path toward a deeper understanding of our data. It defines mechanisms that can separate causal influences from spurious correlation and infer causal effects from observational data. As these techniques evolve, they stand to revolutionize our understanding and uses of data. Causality 2021 is an HPCC Systems research and development program. The goal is to increase our understanding of the latest causal algorithms, assess and challenge the current state-of-the art, and develop a Causality Toolkit for HPCC Systems Platform. This project encompasses all three levels of the "Ladder of Causality": “Seeing”, “Doing”, and “Imagining”, as well as Causal Model Validation, and Causal Discovery.
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Machine Learning
The Forecast of COVID-19 Spread Risk at The County Level
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The Forecast of COVID-19 Spread Risk at The County Level
The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people's lives and restart the economy quickly and safely. People's social behavior, reflected in their mobility data, plays a major role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreaks in the United States. The daily data are fed to a deep learning model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p=0.005)) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. A lower correlation was reported for the counties with total cases of <1,000 during the test interval. The average mean absolute error (MAE) was 605.4 and decreased with a decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread. It showed that the average daily cases decreased with a decrease in the retiree percentage and increased with an increase in the young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also help with early and effective management of possible future pandemics. The project used the HPCC Systems platform for collecting, hosting, and analyzing the data.
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Keynotes & Plenary
Welcome, HPCC Systems Platform Vision, and Plenary Keynotes
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Welcome, HPCC Systems Platform Vision, and Plenary Keynotes
Flavio Villanustre kicks off the 8th annual HPCC Systems Community Summit and reflects on 10 years of our open source journey. Richard Chapman shares his vision and future direction of the platform and our ongoing progress towards making HPCC Systems cloud deployments seamless for our community users. The session concludes with keynotes from top industry leaders and technologists. 
Keynote: HPCC Systems on Azure, Present and Future Possibilities: The Microsoft team will discuss the strong collaboration between Microsoft and LexisNexis Risk Solutions. See how HPCC Systems can be the foundation for a Data Lake and explore Azure services integration possibilities. A high level overview on Azure certification paths will be discussed.
Keynote: A Crypto Customer Journey with HPCC Systems: Hear from C-level executives from the FinTech industry speak about market pressures in Cryptocurrency and how the DataSeers appliance based on HPCC Systems is going to solve some of the problems.
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Platform Features
What’s New in ECL Watch, IDEs and Visualization Framework
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What’s New in ECL Watch, IDEs and Visualization Framework
Gordon will discuss the latest features in ECL related development tools including "Modern" ECL Watch, Visualization updates, and the VS Code ECL Extension.
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Platform Features
What’s New in HPCC Systems and the Cloud Native Roadmap
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What’s New in HPCC Systems and the Cloud Native Roadmap
Gavin shares an update on the new features in the latest release including data handling in the cloud native version.
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