Sessions

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  • All Tracks
  • Platform Features
  • Data Lake
  • Machine Learning
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  • Instructional Demos
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  • Proven Use Cases
  • Hands On Learning
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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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place Online Session
card_travel Training
mic English
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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place Online Session
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mic English
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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mic English
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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mic English
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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mic English
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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place Online Session
card_travel Training
mic English
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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mic English
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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mic English
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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mic English
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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mic English
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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