Hpc lunch and learn

Lyft is on the mission to improve people’s lives with the world’s best transportation. Starting 2019, Lyft has been running both Batch ETL and ML spark workloads primarily on Kubernetes with the Apache Spark on k8s operator. However, with the increasing scale of workloads in frequency and resource requirements, we started hitting numerous reliability issues related to IP allocation, container images, IAM role assignment, and Kubernetes Control Plane. To continue supporting growing Spark usage with Lyft, the team came up with a hybrid architecture optimized for containerized and non-containerized workload based on Kubernetes and YARN. In this talk, we will also cover a dynamic runtime controller that helps with per environment config overrides and easy switchover between resource managers.

This document discusses Cloudera Search, which integrates Apache Solr with Cloudera's distribution of Apache Hadoop (CDH) to provide interactive search capabilities. It describes the architecture of Cloudera Search, including components like Solr, SolrCloud, and Morphlines for extraction and transformation. Methods for indexing data in real-time using Flume or batch using MapReduce are presented. The document also covers querying, security features like Kerberos authentication and collection-level authorization using Sentry, and concludes by describing how to obtain Cloudera Search.

Presented by Michael Noll, Product Manager, Confluent. Why are there so many stream processing frameworks that each define their own terminology? Are the components of each comparable? Why do you need to know about spouts or DStreams just to process a simple sequence of records? Depending on your application’s requirements, you may not need a full framework at all. Processing and understanding your data to create business value is the ultimate goal of a stream data platform. In this talk we will survey the stream processing landscape, the dimensions along which to evaluate stream processing technologies, and how they integrate with Apache Kafka. Particularly, we will learn how Kafka Streams, the built-in stream processing engine of Apache Kafka, compares to other stream processing systems that require a separate processing infrastructure.

This document discusses a presentation titled "Reactive Fast Data & the Data Lake with Akka, Kafka, Spark" given by Todd Fritz at DevNexus in February 2017. The presentation agenda covers reactive systems and patterns, fast data, data lakes, the intersection of these topics, and architecture considerations for building systems that can scale to millions of users and billions of messages. Key technologies discussed include Akka, Kafka, and Spark.

In this presentation I describe the architecture of two of our Flink projects. Both developed for our customers from telco industry.

This document discusses Apache Tez, a framework for accelerating Hadoop query processing. Tez is designed to express query computations as dataflow graphs and execute them efficiently on YARN. It addresses limitations of MapReduce by allowing for custom dataflows and optimizations. Tez provides APIs for defining DAGs of tasks and customizing inputs/outputs/processors. This allows applications to focus on business logic while Tez handles distributed execution, fault tolerance, and resource management for Hadoop clusters.

Providing true interactive and scalable BI on Hadoop is proven to be one of the biggest challenges that is preventing completion of legacy EDW OLAP system transit to Hadoop. While we have all seen many benchmarks running consecutive queries claiming success, having thousands of concurrent business users sending complicated generated queries from their dashboards over billions of records while delivering interactive speed is yet to be seen. In this session we will discuss how an architecture that replaces full-scan brute-force approach with adaptive indexing and auto-generated cubes can dramatically reduce the resources and effort per query, resulting in interactive performance for high concurrency workloads and explain how this is achieved with minimum data engineering efforts. We will also discuss how this architecture can be seamlessly integrated with Hive to provide a complete OLAP-on-Hadoop solution. Session will include live demo of complex business dashboards connected to Hive and accessing billions of rows at interactive speed. Speaker Boaz Raufman, CTO and Co-Founder, JethroData

This presentation gives an overview of the steps in the workshop labs for Oracle Management Cloud APM and Log Analytics. The labs themselves and all sources are found at GitHub: https://github.com/lucasjellema/APM-Demo-App-WorldView .

One key area of Oracle OpenWorld 2016 was data in various shapes. Big Data, streaming data and traditional transactional data. The power of SQL to access and unleash all data - even data in NoSQL databases. The advent of the citizen data scientist. Streaming data analysis in real time on vast and fast and vast data, data discovery. And the new Oracle Database 12cR2 release. Forms, APEX, SQL and PL/SQL.

This document discusses predictive maintenance of robots in the automotive industry using big data analytics. It describes Cisco's Zero Downtime solution which analyzes telemetry data from robots to detect potential failures, saving customers over $40 million by preventing unplanned downtimes. The presentation outlines Cisco's cloud platform and a case study of how robot and plant data is collected and analyzed using streaming and batch processing to predict failures and schedule maintenance. It proposes a next generation predictive platform using machine learning to more accurately detect issues before downtime occurs.

This document provides an introduction to Cloudant, which is a fully managed NoSQL database as a service (DBaaS) that provides a scalable and flexible data layer for web and mobile applications. The presentation discusses NoSQL databases and why they are useful, describes Cloudant's features such as document storage, querying, indexing and its global data presence. It also provides examples of how companies like FitnessKeeper and Fidelity Investments use Cloudant to solve data scaling and management challenges. The document concludes by outlining next steps for signing up and exploring Cloudant.

Database as a Service (DBaaS) is cloud database hosted and managed by the cloud service providers that can be accessed through public cloud or the hybrid cloud. The cloud provider takes care of provisioning, configuring, setup, maintenance, backups and patching the database. Customers are expected to export the database and start consuming the service through the pay-as-you-go model. In his session at 5th Big Data Expo, Janakiram MSV will analyze the current market landscape while exploring the available options, strengths and weaknesses of current DBaaS players. He will highlight the key factors that enterprises should consider before adopting a cloud database platform.

The promise of the cloud is substantial. Oracle's public cloud promise goes beyond the generic promise. This presentation describes the promise of the Oracle Public Cloud specifically for developers. It describes the current state of the PaaS Platform, the actual and coming services and what they could mean to a developer. From same platform, different location (DBaaS, JCS) to cloud native stack (ICS, MCS) and services for Citizen Developers, the presentation touches upon virtually all services relevant to developers. The presentation concludes with first the steps enterprises can start taking to move to the cloud and second the steps individual developers could and perhaps should take in order to conquer the clouds.

Cloudant is a fully-managed NoSQL distributed data layer service based on a JSON document store that provides high availability, scalability, simplicity and performance. It uses a flexible schema and scales massively while always being available. Cloudant is an operational data store and NoSQL document database with a simple HTTP API that is fully integrated with mobile devices, big data, cloud and delivery. It provides replication, sync, real-time analytics using MapReduce, full-text search and geospatial capabilities.

Hear Ryan Millay, IBM Cloudant software development manager, discuss what you need to consider when moving from world of relational databases to a NoSQL document store. You'll learn about the key differences between relational databases and JSON document stores like Cloudant, as well as how to dodge the pitfalls of migrating from a relational database to NoSQL.

The document discusses machine learning with SQL Server 2016 and R Services. It provides an overview of machine learning, R programming language, and the challenges of using R with SQL databases prior to SQL Server 2016. SQL Server 2016 introduces R Services, which allows running R code directly in the database for high performance, scalable machine learning. R Services integrates R with SQL Server through in-database deployment and parallel processing capabilities. This eliminates data movement and scaling issues while leveraging existing R and SQL skills.

In any modern web platform you end up with a need to store different views of your data in many different datastores. I will cover how we have coped with doing this in a reliable way at State.com across a range of different languages, tools and datastores.

This powerpoint is the summary of my 2017 Summer Undergraduate Research experience with Dr. Jeff Prevost.

Introducing an internal cloud brings new paradigms, tools and infrastructure management. When placed alongside traditional HPC the new opportunities are significant But getting to the new world with micro-services, autoscaling and autodialing is a journey that cannot be achieved in a single step.

By simply looking at structured and unstructured data, Data Lakes enable companies to understand correlations between existing and new external data - such as social media - in ways traditional Business Intelligence tools cannot. For this you need to find out the most efficient way to store and access structured or unstructured petabyte-sized data across your entire infrastructure. In this meetup we’ll give answers on the next questions: 1. Why would someone use a Data Lake? 2. Is it hard to build a Data Lake? 3. What are the main features that a Data Lake should bring in? 4. What’s the role of the microservices in the big data world?

FoundationDB is a next-generation database that aims to provide high performance transactions at massive scale through a distributed design. It addresses limitations of NoSQL databases by providing a transactional, fault-tolerant foundation using tools like the Flow programming language. FoundationDB has demonstrated high performance that exceeds other NoSQL databases, and provides ease of scaling, building abstractions, and operation through its transactional design and automated partitioning. The goal is to solve challenges of state management so developers can focus on building applications.

The requirement of running HPC/Congnitive Workload flow with container and manged by container platform

This document provides an overview of software architecture fundamentals and patterns, with a focus on architectures for scalable systems. It discusses key quality attributes for architecture like performance, reliability, and scalability. Common patterns for scalable systems are described, including load balancing, map-reduce, and caching. The document also provides a detailed look at architectures used at Facebook, including the architectures for Facebook's website, chat service, and handling of big data. Key aspects of each system are summarized, including the technologies and design principles used.

This document discusses proposed changes to a Systems Programming course (CS252) to incorporate cloud computing concepts. The course currently focuses on C/C++, operating systems, and networking. The proposal is to have students write mobile and web applications using HTML5, JavaScript frameworks, and cloud services on Bluemix. Students would work in groups on semester-long projects developing games, social apps, or other programs that run in browsers and mobile devices while calling APIs hosted on Bluemix. This aims to teach new generation web development skills and how applications can leverage cloud computing technologies.

While cloud computing offers virtually unlimited capacity, harnessing that capacity in an efficient, cost effective fashion can be cumbersome and difficult at the workload level. At the organizational level, it can quickly become chaos. You must make choices around cloud deployment, and these choices could have a long-lasting impact on your organization. It is important to understand your options and avoid incomplete, complicated, locked-in scenarios. Data management and placement challenges make having the ability to automate workflows and processes across multiple clouds a requirement. In this webinar, you will: • Learn how to leverage cloud services as part of an overall computation approach • Understand data management in a cloud-based world • Hear what options you have to orchestrate HPC in the cloud • Learn how cloud orchestration works to automate and align computing with specific goals and objectives • See an example of an orchestrated HPC workload using on-premises data From computational research to financial back testing, and research simulations to IoT processing frameworks, decisions made now will not only impact future manageability, but also your sanity.

Live Integrated Visualization Environment: An Experiment in Generalized Structured Frameworks for Visualization and Analysis

This document provides an overview of Google Cloud Platform (GCP) services. It begins by explaining why GCP is underpinned by Google's infrastructure and innovation. It then outlines GCP's compute, networking, storage, big data, and machine learning services. These include Compute Engine, Container Engine, App Engine, load balancing, Cloud DNS, Cloud Storage, Cloud Datastore, Cloud Bigtable, Cloud SQL, BigQuery, Dataflow, Pub/Sub, Dataproc, and Cloud Datalab. Machine learning services such as Translate API, Prediction API, Cloud Vision API, and Cloud Speech API are also introduced.

This chapter discusses software development security. It covers topics like programming concepts, compilers and interpreters, procedural vs object-oriented languages, application development methods like waterfall vs agile models, databases, object-oriented design, assessing software vulnerabilities, and artificial intelligence techniques. The key aspects are securing the entire software development lifecycle from initial planning through operation and disposal, using secure coding practices, testing for vulnerabilities, and continually improving processes.

The document summarizes lessons learned from building a real-time network traffic analyzer in C/C++. Key points include: - Libpcap was used for traffic capturing as it is cross-platform, supports PF_RING, and has a relatively easy API. - SQLite was used for data storage due to its small footprint, fast performance, embeddability, SQL support, and B-tree indexing. - A producer-consumer model with a blocking queue was implemented to handle packet processing in multiple threads. - Memory pooling helped address performance issues caused by excessive malloc calls during packet aggregation. - Custom spin locks based on atomic operations improved performance over mutexes on FreeBSD/

This document discusses how organizations will need to adapt their data infrastructure and software models as Moore's Law ends and data volumes continue growing exponentially. It outlines how traditional clustering, databases, and application servers will no longer scale to meet these new demands. New distributed, dynamically adaptive approaches like NoSQL data stores, functional programming, and eventual consistency models are needed. Hardware is also evolving to support exabyte storage, tens of thousands of CPU cores, and networked memory, requiring new software architectures.

This webinar discusses tools for making big data easy to work with. It covers MetaScale Expertise, which provides Hadoop expertise and case studies. Kognitio Analytics is discussed as a way to accelerate Hadoop for organizations. The webinar agenda includes an introduction, presentations on MetaScale and Kognitio, and a question and answer session. Rethinking data strategies with Hadoop and using in-memory analytics are presented as ways to gain insights from large, diverse datasets.

IBM Spectrum Conductor can manage H2O Driverless AI instances at scale across multiple nodes in an enterprise data center. Key benefits include the ability to run multiple Driverless AI instances on the same host using GPUs, failover capabilities if an instance fails, and role-based access control for users. The integration improves productivity by providing a shared file system, workload management, and allowing easy start/stop of Driverless AI instances.

The document discusses strategies for transitioning from monolithic architectures to microservice architectures. It outlines some of the challenges with maintaining large monolithic applications and reasons for modernizing, such as handling more data and needing faster changes. It then covers microservice design principles and best practices, including service decomposition, distributed systems strategies, and reactive design. Finally it introduces Lagom as a framework for building reactive microservices on the JVM and outlines its key components and development environment.

This is a small introduction to microservices. you can find the differences between microservices and monolithic applications. You will find the pros and cons of microservices. you will also find the challenges (Business/ technical) that you may face while implementing microservices.

IBM Connect 2017 Session on RESTful architectures and their uses in IBM Domino environments (Notes and XPages applications). February 22, 2017.

This document discusses Indix's evolution from its initial Data Platform 1.0 to a new Data Platform 2.0 based on the Lambda Architecture. The Lambda Architecture uses three layers - batch, serving, and speed layers - to process streaming and batch data. This provides robustness, fault tolerance, and the ability to query both real-time and batch processed views. The new system uses technologies like Spark, HBase, and Solr to implement the Lambda Architecture principles.

Cloud computing is no longer a fad that is going around. It is for real and is perhaps the most talked about subject. Various players in the cloud eco-system have provided a definition that is closely aligned to their sweet spot –let it be infrastructure, platforms or applications. This presentation will provide an exposure of a variety of cloud computing techniques, architecture, technology options to the participants and in general will familiarize cloud fundamentals in a holistic manner spanning all dimensions such as cost, operations, technology etc

Everything that I found interesting about machines behaving intelligently during June 2024

Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation

Java Servlet programs

These fighter aircraft have uses outside of traditional combat situations. They are essential in defending India's territorial integrity, averting dangers, and delivering aid to those in need during natural calamities. Additionally, the IAF improves its interoperability and fortifies international military alliances by working together and conducting joint exercises with other air forces.

Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)