Cloud Computing Training Courses

Cloud Computing Training

Cloud Computing training

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Cloud Computing Course Outlines

Code Name Duration Overview
azurearchitectures Architecting Microsoft Azure Solutions 14 hours This training permits delegates to improve their Microsoft Azure solution design skills. After this training the delegate will understand the features and capabilities of Azure services, to be able to identify trade-offs, and make decisions for designing public and hybrid cloud solutions. During training the appropriate infrastructure and platform solutions to meet the required functional, operational, and deployment requirements through the solution life-cycle will be defined.Module 1: Design Principles for Cloud Infrastructure and Development Module 2: Designing App Service Web Apps Module 3: Designing Application Storage & Data Access Module 4: Securing Resources Module 5: Design Microsoft Azure Infrastructure and Networking Module 6: Designing an Advanced Application Module 7: Designing a Management, Monitoring Strategy Module 8: Designing a Business Continuity Strategy
rancher Rancher: Manage your Docker containers 14 hours Rancher is an open source software platform that enables organizations to run containers in production. With Rancher, organizations no longer have to cobble together distinct open-source technologies to build a container services platform. It includes a fully supported Kubernetes distribution as well as the option to choose from Docker Swarm and Apache Mesos. Rancher includes modular infrastructure services such as resource management, networking between containers, service discovery, container load balancing, container health monitoring, and backup and recovery, all under one roof. This course provides participants with an overview of Rancher and demonstrates through hands-on practice how to deploy and manage a Rancher container management system. Audience     Software engineers     System administrators     DevOps engineers Format of the course     Part lecture, part discussion, heavy hands-on practice Introduction     Rancher vs DC/OS Installing and configuring Rancher Starting the Rancher server Adding hosts Launching infrastructure services Creating a container using the UI Creating a container through Docker command line Creating a multi-container application Creating a multi-container application using Rancher Compose Launching Kubernetes Launching Mesos Launching Swarm Working with Catalogs Working with the Rancher API Closing remarks
rancheros RancherOS: Just enough OS to run Docker 7 hours RancherOS is an open-source Linux distribution that runs the entire operating system (including system services such as udev and rsyslog) as Docker containers. RancherOS includes only the bare minimum software needed to run Docker. It runs Docker as PID1 and dynamically pulls everything else it needs through Docker. At about 22MB, RancherOS is easy to distribute, orchestrate and spin up in your data center. This training introduces RancherOS's architecture, tools, and components and walks participants step-by-step through the setup of RancherOS in a live lab environment. By the end of this training, participants will have the understanding and practice to use RancherOS to run containers at scale in development, test and production. Audience     DevOps engineers     System administrators     Software engineers Format of the course     Part lecture, part discussion, heavy hands-on practice Introduction The RancherOS architecture Installing and configuring RancherOS Spinning up containers on RancherOS System level services (containers) vs user level services (containers) Running Linux system services through System Docker Running applications through User Docker Isolating user containers for different applications and user groups Controlling access to containers Networking and storage in RancherOS RancherOS security Upgrading and downgrading RancherOS Running RancherOS in the cloud Some sample deployments     Using Docker Compose to define application deployment     Building an Apache Mesos cluster on RancherOS     Running Nagios as a System Service on RancherOS Other container OSs and the future of the datacenter Closing remarks
awspaas AWS: A hands-on introduction to cloud computing 7 hours This training provides an overview of AWS products, services and solutions. It is aimed at individuals and teams who are: evaluating/preparing for an initial deployment of their IT infrastructure on AWS evaluating/preparing to migrate their existing IT infrastructure to the AWS public cloud By the end of this training, participants will have a clear understanding of the principal components that make up Amazon's cloud offering. The hands-on exercises, discussions and in-class deployments will provide participants with the practice and feedback they need to get comfortable and ready to carry out their own live deployments to AWS. Audience     Engineers, system administrators, DBAs, DevOps Format of the course     The training will be interactive and includes hands-on lab activities and live implementation. Introduction AWS Foundational Services     EC2, VPC, S3, EBS AWS compute and networking options AWS Security Identity and Access Management Managing structured and unstructured data in AWS Working with DynamoDB and Amazon Relational Database Service (RDS) AWS tools for management and deployment     Auto Scaling     CloudWatch     Elastic Load Balancing and Monitoring     Trusted Advisor Instance management Closing remarks
cloudcomp Cloud Computing Overview 7 hours This course has been created for people who want to understand how to benefit from cloud computing. It uses Amazon EC2 example, but can be tailored to other providers. Short history of computing Virtualization Private Cloud vs Public Could Where to use or not to use cloud computing Could computing in action Workshops, where delegates will be entitled to start an instance (included in the course price) Starting and stopping instances Monitoring Changing hardware requirements EBS vs instance storage Auto-scaling Load balancing Spot instances Overview of cloud providers Eucalyptus Digital Ocean Azure Others
springcloud Spring Cloud: Building microservices with Spring Cloud 14 hours Spring Cloud builds on Spring Boot to enable the development of distributed systems and microservices. In this training we start with a discussion of microservice architecture. Participant knowledge is put to the test through exercises and the step-by-step development of sample microservices. By the end of this training participants will have a solid understanding of how to use Spring Cloud and related Spring technologies to rapidly develop their own cloud-scale, cloud-ready microservices. Audience     Java developers wishing to rapidly build and deploy microservices Format of the course       Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development. Introduction     Microservice architecture, PaaS, and cloud-native design Overview of Spring Cloud sub-projects     Config Server & Bus, Eureka, Ribbon, Feign, and Hystrix Overview of Spring Boot Setting up your development environment Creating a Spring Boot application Centralized, versioned configuration management with Spring Cloud Config Dynamic configuration updates with Spring Cloud Bus Service discovery with Eureka Load balancing with Ribbon Applying circuit breakers with Hystrix Declarative REST clients with Feign Working with API Gateway Securing your microservices Tracing microservices to uncover latencies Troubleshooting Closing remarks
mdlmrah Model MapReduce and Apache Hadoop 14 hours The course is intended for IT specialist that works with the distributed processing of large data sets across clusters of computers. Data Mining and Business Intelligence Introduction Area of application Capabilities Basics of data exploration Big data What does Big data stand for? Big data and Data mining MapReduce Model basics Example application Stats Cluster model Hadoop What is Hadoop Installation Configuration Cluster settings Architecture and configuration of Hadoop Distributed File System Console tools DistCp tool MapReduce and Hadoop Streaming Administration and configuration of Hadoop On Demand Alternatives
gcpfaws Google Cloud Platform Fundamentals for AWS Professionals 6 hours This six-hour course with labs introduces AWS professionals to the core capabilities of Google Cloud Platform (GCP) in the four technology pillars: networking, compute, storage, and database. It is designed for AWS Solution Architects and SysOps Administrators familiar with AWS features and setup and want to gain experience configuring GCP products immediately. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly. This course teaches participants the following skills: Identify GCP counterparts for Amazon VPC, subnets, routes, NACLs, IGW, Amazon EC2, Amazon EBS, auto-scaling, Elastic Load Balancing, Amazon S3, Amazon Glacier, Amazon RDS, Amazon Redshift, AWS IAM, and more. Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more. Manage and monitor applications. Explain feature and pricing model differences. Locate documentation and training. This class is intended for the following: AWS Solution Architects just getting started with Google Cloud Platform. AWS SysOps Administrators used to building IaaS solutions. Architects and Engineers operating in multi-cloud environments. The course includes presentations, demonstrations, and hands-on labs. Module 1: Introducing Google Cloud Platform Google Cloud infrastructure. AWS regions, availability zones, and CloudFront. GCP regions, zones, edge caching, and Cloud CDN. GCP services. Module 2: Setting up Accounts and Billing AWS accounts, billing, and IAM roles. GCP accounts, billing accounts, projects, and admin setup. Account, billing, project, and admin setup. Lab: Set up projects and billing accounts with a free-trial GCP account. Module 3: Networking Amazon VPC, subnets, routes, NACLs, and security groups. GCP networks, subnets, routes, and firewall rules. VMs in networks. Lab: Add VMs, explore the default network, and test connectivity. Module 4: Working with VM Instances Amazon EC2 instance types, AMIs, Amazon EBS, ephemeral drives, spot instances. Google Compute Engine machine types, instances, persistent disks, local SSDs, preemptible VMs. VM and web app deployment. Lab: Deploy VMs with an app by console and command line. Module 5: Scaling and Load Balancing Apps Amazon EC2 launch configurations, auto-scaling groups, load balancing. Google Compute Engine instance templates, managed instance groups, load balancing. Autoscaling and load balancing setup. Lab: Scale and load balance instances, and test under load. Module 6: Isolating Instances and Apps A 3-tier web app in GCP. A custom network with custom subnets and firewall rules. Lab: Build a 3-tier web app with public front-end and private backend. Module 7: Using Storage as a Service and Database as a Service Amazon S3, Amazon Glacier, Amazon RDS, Amazon DynamoDB, Amazon Redshift, Amazon Athena. Google Cloud Storage, Google Cloud SQL, Cloud Spanner, Google Cloud Datastore, Google Cloud Bigtable, Google BigQuery. Lab: Use gsutil command-line tool to perform operations on buckets and objects in Cloud Storage. Lab: Load and analyze data in BigQuery. Module 8: Deployment and Monitoring AWS CloudFormation, Amazon CloudWatch. Google Cloud Deployment Manager, Google StackDriver. Lab: Deploy your infrastructure using Deployment Manager.
opstcloud Create OpenStack cloud infrastructure 14 hours The course helps to understand and implement cloud infrastructure based on OpenStack. The participant learns the architecture and capabilities of OpenStack and a variety of installation scenarios. Introduction OpenStack Architecture Core services Additional services Environment Planning implementation Preparing the environment Identity service Installing Keystone Architecture Authentication Image service Installing Glance Architecture Adding images Compute service Installing Nova Architecture Adding compute nodes Network service Installing Neutron Architecture Creating a simple network Block storage service Installing Cinder Architecture Administration Creating users and projects Creating and destroying instances Creating and attaching volumes Configuration tools
archgcp Architecting with Google Cloud Platform: Infrastructure 24 hours This three-day instructor-led class introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems and applications services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring. This course teaches participants the following skills: Consider the entire range of Google Cloud Platform technologies in their plans. Learn methods to develop, implement, and deploy solutions. Distinguish between features of similar or related products and technologies. Recognize a wide variety of solution domains, use cases, and applications. Develop essential skills for managing and administering solutions. Develop knowledge of solution patterns -- methods, technologies, and designs that are used to implement security, scalability, high availability, and other desired qualities. This class is intended for the following participants: Cloud Solutions Architects, DevOps Engineers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform. The course includes presentations, demonstrations, and hands-on labs. Essential Cloud Infrastructure: Foundation  Module 1: Introduction to Google Cloud Platform Role of the Cloud Architect. Learn about Solution Domains as an approach to design. Lab: Console and Cloud Shell. Module 2: Virtual Networks Cloud Virtual Networks (CVN), Projects, Networks, Subnetworks, IP addresses, Routes, Firewall rules. Subnetworks for resource management instead of physical network topology. Lab: Virtual Networking. Module 3: Virtual Machines GCE, tags, VM options, vCPUs, disk options, images, and special features of persistent disks for VMs. Essential Cloud Infrastructure: Core Services  Module 4: Cloud IAM Members, roles, organizations, account administration, service accounts. Lab: Cloud IAM. Module 5: Resource Management Billing, Quotas, Labels, Names, Cloud Resource Manager. Lab: Lab Billing. Module 6: Data Services Cloud Storage, Datastore, Bigtable, Cloud SQL. Lab: Cloud Storage. Lab: Cloud SQL. Module 7: Interconnecting Networks VPNs, Cloud Router, Cloud Interconnect, Direct Peering, Cloud DNS. Lab: VPN and Cloud Router. Elastic Cloud Infrastructure: Scaling and Automation  Module 8: Infrastructure Automation Infrastructure automation, custom images, startup and shutdown scripts and metadata, Deployment Manager, Cloud Launcher. Lab: Hadoop Cluster Maker. Lab: Virtual Machine. Module 9: Autoscaling Load Balancing, Instance Groups, Autoscaler. Lab: Autoscaling. Module 10: Resource Monitoring Stackdriver, Monitoring, Logging, Error Reporting, Tracing, Debugging. Lab: Resource Monitoring (Stackdriver). Module 11: Containers Containers, Google Container Engine (GKE), and Container Registry. Module 12: Platform Security Learn about Google's layered security strategy that uses a multi-faceted approach to provide platform security services and benefits. Module 13: Managed Services Dataproc, Dataflow, BigQuery, Datalab. Lab: BigQuery and Datalab. Elastic Cloud Infrastructure: Containers and Services  Module 14: Application Development Infrastructure App Engine, Cloud SDK, Dev Tools, Cloud Source Repos, Cloud Pub/Sub, Cloud Endpoints and Apigee, Cloud Functions. Module 15: Application Development Services Google App Engine (GAE), Dev Tools, Cloud Source Repos. Lab: App Engine Development. Module 16: Containers Containers, Google Container Engine (GKE), and Container Registry. Lab: Kubernetes Load Balancing.
iotemi IoT (Internet of Things) for Entrepreneurs, Managers and Investors 21 hours Unlike other technologies, IoT is far more complex encompassing almost every branch of core Engineering-Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics and Mobile. For each of its engineering layers, there are aspects of economics, standards, regulations and evolving state of the art. This is for the firs time, a modest course is offered to cover all of these critical aspects of IoT Engineering. Summary An advanced training program covering the current state of the art in Internet of Things Cuts across multiple technology domains to develop awareness of an IoT system and its components and how it can help businesses and organizations. Live demo of model IoT applications to showcase practical IoT deployments across different industry domains, such as Industrial IoT, Smart Cities, Retail, Travel & Transportation and use cases around connected devices & things Target Audience Managers responsible for business and operational processes within their respective organizations and want to know how to harness IoT to make their systems and processes more efficient. Entrepreneurs and Investors who are looking to build new ventures and want to develop a better understanding of the IoT technology landscape to see how they can leverage it in an effective manner. Duration 3 Days ( 8 hours / day) Estimates for Internet of Things or IoT market value are massive, since by definition the IoT is an integrated and diffused layer of devices, sensors, and computing power that overlays entire consumer, business-to-business, and government industries. The IoT will account for an increasingly huge number of connections: 1.9 billion devices today, and 9 billion by 2018. That year, it will be roughly equal to the number of smartphones, smart TVs, tablets, wearable computers, and PCs combined. In the consumer space, many products and services have already crossed over into the IoT, including kitchen and home appliances, parking, RFID, lighting and heating products, and a number of applications in Industrial Internet. However the underlying technologies of IoT are nothing new as M2M communication existed since the birth of Internet. However what changed in last couple of years is the emergence of number of inexpensive wireless technologies added by overwhelming adaptation of smart phones and Tablet in every home. Explosive growth of mobile devices led to present demand of IoT. Due to unbounded opportunities in IoT business, a large number of small and medium sized entrepreneurs jumped on a bandwagon of IoT gold rush. Also due to emergence of open source electronics and IoT platform, cost of development of IoT system and further managing its sizable production is increasingly affordable. Existing electronic product owners are experiencing pressure to integrate their device with Internet or Mobile app. This training is intended for a technology and business review of an emerging industry so that IoT enthusiasts/entrepreneurs can grasp the basics of IoT technology and business. Course Objective Main objective of the course is to introduce emerging technological options, platforms and case studies of IoT implementation in home & city automation (smart homes and cities), Industrial Internet, healthcare, Govt., Mobile Cellular and other areas. Basic introduction of all the elements of IoT-Mechanical, Electronics/sensor platform, Wireless and wireline protocols, Mobile to Electronics integration, Mobile to enterprise integration, Data-analytics and Total control plane M2M Wireless protocols for IoT- WiFi, Zigbee/Zwave, Bluetooth, ANT+ : When and where to use which one? Mobile/Desktop/Web app- for registration, data acquisition and control –Available M2M data acquisition platform for IoT-–Xively, Omega and NovoTech, etc. Security issues and security solutions for IoT Open source/commercial electronics platform for IoT-Raspberry Pi, Arduino , ArmMbedLPC etc Open source /commercial enterprise cloud platform for AWS-IoT apps, Azure -IOT, Watson-IOT cloud in addition to other minor IoT clouds Studies of business and technology of some of the common IoT devices like Home automation, Smoke alarm, vehicles, military, home health etc. Session 1 — Business Overview of Why IoT is so important Case Studies from Nest, CISCO and top industries IoT adaptation rate in North American & and how they are aligning their future business model and operation around IoT Broad Scale Application Area Smart House and Smart City Industrial Internet Smart Cars Wearables Home Healthcare Business Rule Generation for IoT 3 layered architecture of Big Data — Physical (Sensors), Communication, and Data Intelligence Session 2 — Introduction of IoT: All about Sensors – Electronics Basic function and architecture of a sensor — sensor body, sensor mechanism, sensor calibration, sensor maintenance, cost and pricing structure, legacy and modern sensor network — all the basics about the sensors Development of sensor electronics — IoT vs legacy, and open source vs traditional PCB design style Development of sensor communication protocols — history to modern days. Legacy protocols like Modbus, relay, HART to modern day Zigbee, Zwave, X10,Bluetooth, ANT, etc. Business driver for sensor deployment — FDA/EPA regulation, fraud/tempering detection, supervision, quality control and process management Different Kind of Calibration Techniques — manual, automation, infield, primary and secondary calibration — and their implication in IoT Powering options for sensors — battery, solar, Witricity, Mobile and PoE Hands on training with single silicon and other sensors like temperature, pressure, vibration, magnetic field, power factor etc. Demo : Logging data from a temperature sensor Session 3 — Fundamental of M2M communication — Sensor Network and Wireless protocol What is a sensor network? What is ad-hoc network? Wireless vs. Wireline network WiFi- 802.11 families: N to S — application of standards and common vendors. Zigbee and Zwave — advantage of low power mesh networking. Long distance Zigbee. Introduction to different Zigbee chips. Bluetooth/BLE: Low power vs high power, speed of detection, class of BLE. Introduction of Bluetooth vendors & their review. Creating network with Wireless protocols such as Piconet by BLE Protocol stacks and packet structure for BLE and Zigbee Other long distance RF communication link LOS vs NLOS links Capacity and throughput calculation Application issues in wireless protocols — power consumption, reliability, PER, QoS, LOS Sensor networks for WAN deployment using LPWAN. Comparison of various emerging protocols such as LoRaWAN, NB-IoT etc. Hands on training with sensor network Demo : Device control using BLE Session 4 — Review of Electronics Platform, production and cost projection PCB vs FPGA vs ASIC design-how to take decision Prototyping electronics vs Production electronics QA certificate for IoT- CE/CSA/UL/IEC/RoHS/IP65: What are those and when needed? Basic introduction of multi-layer PCB design and its workflow Electronics reliability-basic concept of FIT and early mortality rate Environmental and reliability testing-basic concepts Basic Open source platforms: Arduino, Raspberry Pi, Beaglebone, when needed? Session 5 — Conceiving a new IoT product- Product requirement document for IoT State of the present art and review of existing technology in the market place Suggestion for new features and technologies based on market analysis and patent issues Detailed technical specs for new products- System, software, hardware, mechanical, installation etc. Packaging and documentation requirements Servicing and customer support requirements High level design (HLD) for understanding of product concept Release plan for phase wise introduction of the new features Skill set for the development team and proposed project plan -cost & duration Target manufacturing price Session 6 — Introduction to Mobile app platform for IoT Protocol stack of Mobile app for IoT Mobile to server integration –what are the factors to look out What are the intelligent layer that can be introduced at Mobile app level ? iBeacon in IoS Window Azure Amazon AWS-IoT Web Interfaces for Mobile Apps ( REST/WebSockets) IoT Application layer protocols (MQTT/CoAP) Security for IoT middleware- Keys, Token and random password generation for authentication of the gateway devices. Demo : Mobile app for tracking IoT enabled trash cans Session 7 — Machine learning for intelligent IoT Introduction to Machine learning Learning classification techniques Bayesian Prediction-preparing training file Support Vector Machine Image and video analytic for IoT Fraud and alert analytic through IoT Bio –metric ID integration with IoT Real Time Analytic/Stream Analytic Scalability issues of IoT and machine learning What are the architectural implementation of Machine learning for IoT Demo : Using KNN Algorithm for regression analysis Demo : SVM based classification for image and video analysis Session 8 — Analytic Engine for IoT Insight analytic Visualization analytic Structured predictive analytic Unstructured predictive analytic Recommendation Engine Pattern detection Rule/Scenario discovery — failure, fraud, optimization Root cause discovery Session 9 — Security in IoT implementation Why security is absolutely essential for IoT Mechanism of security breach in IOT layer Privacy enhancing technologies Fundamental of network security Encryption and cryptography implementation for IoT data Security standard for available platform European legislation for security in IoT platform Secure booting Device authentication Firewalling and IPS Updates and patches Session 10 — Database implementation for IoT : Cloud based IoT platforms SQL vs NoSQL-Which one is good for your IoT application Open sourced vs. Licensed Database Available M2M cloud platform Cassandra -Time Series Data Mongo-DB Omega Ayla Libellium CISCO M2M platform AT &T M2M platform Google M2M platform Session 11 — A few common IoT systems Home automation Energy optimization in Home Automotive-OBD IoT-Lock Smart Smoke alarm BAC ( Blood alcohol monitoring ) for drug abusers under probation Pet cam for Pet lovers Wearable IOT Mobile parking ticketing system Indoor location tracking in Retail store Home health care Smart Sports Watch Demo : Smart city application using IoT Demo : Retail, Transportation & Logistics Use case for IoT Session 12 — Big Data for IoT 4V- Volume, velocity, variety and veracity of Big Data Why Big Data is important in IoT Big Data vs legacy data in IoT Hadoop for IoT-when and why? Storage technique for image, Geospatial and video data Distributed database- Cassandra as example Parallel computing basics for IoT Micro services Architecture Demo : Apache Spark
glusterfs GlusterFS for System Administrators 21 hours GlusterFS is an open-source distributed file storage system that can scale up to petabytes of capacity. GlusterFS is designed to provide additional space depending on the user's storage requirements. A common application for GlusterFS is cloud computing storage systems. In this instructor-led training, participants will learn how to use normal, off-the-shelf hardware to create and deploy a storage system that is scalable and always available.  By the end of the course, participants will be able to: Install, configure, and maintain a full-scale GlusterFS system. Implement large-scale storage systems in different types of environments. Audience System administrators Storage administrators Format of the Course Part lecture, part discussion, exercises and heavy hands-on practice. Introduction to GlusterFS     Terminologies used Overview of GlusterFS architecture Installing of the GlusterFS Controlling and monitoring the installed GlusterFS Using the Gluster Console Manager Creating the Trusted Storage Pools Understanding of the volume types Creating the GlusterFS client Understanding geo-replication Managing the GLusterFS volume, client, geo-replication and directory quota GlusterFS workload monitoring Accessing the control lists Monitoring the unified file and object storage Monitoring the Hadoop compatible storage Discussing the snapshots GlusterFS troubleshooting Closing Remarks
bdbitcsp Big Data Business Intelligence for Telecom and Communication Service Providers 35 hours Overview Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience, but data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month. Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity. Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored. With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.) This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain. Course objectives Main objective of the course is to introduce new Big Data business intelligence techniques in 4 sectors of Telecom Business (Marketing/Sales, Network Operation, Financial operation and Customer Relation Management). Students will be introduced to following: Introduction to Big Data-what is 4Vs (volume, velocity, variety and veracity) in Big Data- Generation, extraction and management from Telco perspective How Big Data analytic differs from legacy data analytic In-house justification of Big Data -Telco perspective Introduction to Hadoop Ecosystem- familiarity with all Hadoop tools like Hive, Pig, SPARC –when and how they are used to solve Big Data problem How Big Data is extracted to analyze for analytics tool-how Business Analysis’s can reduce their pain points of collection and analysis of data through integrated Hadoop dashboard approach Basic introduction of Insight analytics, visualization analytics and predictive analytics for Telco Customer Churn analytic and Big Data-how Big Data analytic can reduce customer churn and customer dissatisfaction in Telco-case studies Network failure and service failure analytics from Network meta-data and IPDR Financial analysis-fraud, wastage and ROI estimation from sales and operational data Customer acquisition problem-Target marketing, customer segmentation and cross-sale from sales data Introduction and summary of all Big Data analytic products and where they fit into Telco analytic space Conclusion-how to take step-by-step approach to introduce Big Data Business Intelligence in your organization Target Audience Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office. Business Analysts in Telco CFO office managers/analysts Operational managers QA managers Breakdown of topics on daily basis: (Each session is 2 hours) Day-1: Session -1: Business Overview of Why Big Data Business Intelligence in Telco. Case Studies from T-Mobile, Verizon etc. Big Data adaptation rate in North American Telco & and how they are aligning their future business model and operation around Big Data BI Broad Scale Application Area Network and Service management Customer Churn Management Data Integration & Dashboard visualization Fraud management Business Rule generation Customer profiling Localized Ad pushing Day-1: Session-2 : Introduction of Big Data-1 Main characteristics of Big Data-volume, variety, velocity and veracity. MPP architecture for volume. Data Warehouses – static schema, slowly evolving dataset MPP Databases like Greenplum, Exadata, Teradata, Netezza, Vertica etc. Hadoop Based Solutions – no conditions on structure of dataset. Typical pattern : HDFS, MapReduce (crunch), retrieve from HDFS Batch- suited for analytical/non-interactive Volume : CEP streaming data Typical choices – CEP products (e.g. Infostreams, Apama, MarkLogic etc) Less production ready – Storm/S4 NoSQL Databases – (columnar and key-value): Best suited as analytical adjunct to data warehouse/database Day-1 : Session -3 : Introduction to Big Data-2 NoSQL solutions KV Store - Keyspace, Flare, SchemaFree, RAMCloud, Oracle NoSQL Database (OnDB) KV Store - Dynamo, Voldemort, Dynomite, SubRecord, Mo8onDb, DovetailDB KV Store (Hierarchical) - GT.m, Cache KV Store (Ordered) - TokyoTyrant, Lightcloud, NMDB, Luxio, MemcacheDB, Actord KV Cache - Memcached, Repcached, Coherence, Infinispan, EXtremeScale, JBossCache, Velocity, Terracoqua Tuple Store - Gigaspaces, Coord, Apache River Object Database - ZopeDB, DB40, Shoal Document Store - CouchDB, Cloudant, Couchbase, MongoDB, Jackrabbit, XML-Databases, ThruDB, CloudKit, Prsevere, Riak-Basho, Scalaris Wide Columnar Store - BigTable, HBase, Apache Cassandra, Hypertable, KAI, OpenNeptune, Qbase, KDI Varieties of Data: Introduction to Data Cleaning issue in Big Data RDBMS – static structure/schema, doesn’t promote agile, exploratory environment. NoSQL – semi structured, enough structure to store data without exact schema before storing data Data cleaning issues Day-1 : Session-4 : Big Data Introduction-3 : Hadoop When to select Hadoop? STRUCTURED - Enterprise data warehouses/databases can store massive data (at a cost) but impose structure (not good for active exploration) SEMI STRUCTURED data – tough to do with traditional solutions (DW/DB) Warehousing data = HUGE effort and static even after implementation For variety & volume of data, crunched on commodity hardware – HADOOP Commodity H/W needed to create a Hadoop Cluster Introduction to Map Reduce /HDFS MapReduce – distribute computing over multiple servers HDFS – make data available locally for the computing process (with redundancy) Data – can be unstructured/schema-less (unlike RDBMS) Developer responsibility to make sense of data Programming MapReduce = working with Java (pros/cons), manually loading data into HDFS Day-2: Session-1.1: Spark : In Memory distributed database What is “In memory” processing? Spark SQL Spark SDK Spark API RDD Spark Lib Hanna How to migrate an existing Hadoop system to Spark Day-2 Session -1.2: Storm -Real time processing in Big Data Streams Sprouts Bolts Topologies Day-2: Session-2: Big Data Management System Moving parts, compute nodes start/fail :ZooKeeper - For configuration/coordination/naming services Complex pipeline/workflow: Oozie – manage workflow, dependencies, daisy chain Deploy, configure, cluster management, upgrade etc (sys admin) :Ambari In Cloud : Whirr Evolving Big Data platform tools for tracking ETL layer application issues Day-2: Session-3: Predictive analytics in Business Intelligence -1: Fundamental Techniques & Machine learning based BI : Introduction to Machine learning Learning classification techniques Bayesian Prediction-preparing training file Markov random field Supervised and unsupervised learning Feature extraction Support Vector Machine Neural Network Reinforcement learning Big Data large variable problem -Random forest (RF) Representation learning Deep learning Big Data Automation problem – Multi-model ensemble RF Automation through Soft10-M LDA and topic modeling Agile learning Agent based learning- Example from Telco operation Distributed learning –Example from Telco operation Introduction to Open source Tools for predictive analytics : R, Rapidminer, Mahut More scalable Analytic-Apache Hama, Spark and CMU Graph lab Day-2: Session-4 Predictive analytics eco-system-2: Common predictive analytic problems in Telecom Insight analytic Visualization analytic Structured predictive analytic Unstructured predictive analytic Customer profiling Recommendation Engine Pattern detection Rule/Scenario discovery –failure, fraud, optimization Root cause discovery Sentiment analysis CRM analytic Network analytic Text Analytics Technology assisted review Fraud analytic Real Time Analytic Day-3 : Sesion-1 : Network Operation analytic- root cause analysis of network failures, service interruption from meta data, IPDR and CRM: CPU Usage Memory Usage QoS Queue Usage Device Temperature Interface Error IoS versions Routing Events Latency variations Syslog analytics Packet Loss Load simulation Topology inference Performance Threshold Device Traps IPDR ( IP detailed record) collection and processing Use of IPDR data for Subscriber Bandwidth consumption, Network interface utilization, modem status and diagnostic HFC information Day-3: Session-2: Tools for Network service failure analysis: Network Summary Dashboard: monitor overall network deployments and track your organization's key performance indicators Peak Period Analysis Dashboard: understand the application and subscriber trends driving peak utilization, with location-specific granularity Routing Efficiency Dashboard: control network costs and build business cases for capital projects with a complete understanding of interconnect and transit relationships Real-Time Entertainment Dashboard: access metrics that matter, including video views, duration, and video quality of experience (QoE) IPv6 Transition Dashboard: investigate the ongoing adoption of IPv6 on your network and gain insight into the applications and devices driving trends Case-Study-1: The Alcatel-Lucent Big Network Analytics (BNA) Data Miner Multi-dimensional mobile intelligence (m.IQ6) Day-3 : Session 3: Big Data BI for Marketing/Sales –Understanding sales/marketing from Sales data: ( All of them will be shown with a live predictive analytic demo ) To identify highest velocity clients To identify clients for a given products To identify right set of products for a client ( Recommendation Engine) Market segmentation technique Cross-Sale and upsale technique Client segmentation technique Sales revenue forecasting technique Day-3: Session 4: BI needed for Telco CFO office: Overview of Business Analytics works needed in a CFO office Risk analysis on new investment Revenue, profit forecasting New client acquisition forecasting Loss forecasting Fraud analytic on finances ( details next session ) Day-4 : Session-1: Fraud prevention BI from Big Data in Telco-Fraud analytic: Bandwidth leakage / Bandwidth fraud Vendor fraud/over charging for projects Customer refund/claims frauds Travel reimbursement frauds Day-4 : Session-2: From Churning Prediction to Churn Prevention: 3 Types of Churn : Active/Deliberate , Rotational/Incidental, Passive Involuntary 3 classification of churned customers: Total, Hidden, Partial Understanding CRM variables for churn Customer behavior data collection Customer perception data collection Customer demographics data collection Cleaning CRM Data Unstructured CRM data ( customer call, tickets, emails) and their conversion to structured data for Churn analysis Social Media CRM-new way to extract customer satisfaction index Case Study-1 : T-Mobile USA: Churn Reduction by 50% Day-4 : Session-3: How to use predictive analysis for root cause analysis of customer dis-satisfaction : Case Study -1 : Linking dissatisfaction to issues – Accounting, Engineering failures like service interruption, poor bandwidth service Case Study-2: Big Data QA dashboard to track customer satisfaction index from various parameters such as call escalations, criticality of issues, pending service interruption events etc. Day-4: Session-4: Big Data Dashboard for quick accessibility of diverse data and display : Integration of existing application platform with Big Data Dashboard Big Data management Case Study of Big Data Dashboard: Tableau and Pentaho Use Big Data app to push location based Advertisement Tracking system and management Day-5 : Session-1: How to justify Big Data BI implementation within an organization: Defining ROI for Big Data implementation Case studies for saving Analyst Time for collection and preparation of Data –increase in productivity gain Case studies of revenue gain from customer churn Revenue gain from location based and other targeted Ad An integrated spreadsheet approach to calculate approx. expense vs. Revenue gain/savings from Big Data implementation. Day-5 : Session-2: Step by Step procedure to replace legacy data system to Big Data System: Understanding practical Big Data Migration Roadmap What are the important information needed before architecting a Big Data implementation What are the different ways of calculating volume, velocity, variety and veracity of data How to estimate data growth Case studies in 2 Telco Day-5: Session 3 & 4: Review of Big Data Vendors and review of their products. Q/A session: AccentureAlcatel-Lucent Amazon –A9 APTEAN (Formerly CDC Software) Cisco Systems Cloudera Dell EMC GoodData Corporation Guavus Hitachi Data Systems Hortonworks Huawei HP IBM Informatica Intel Jaspersoft Microsoft MongoDB (Formerly 10Gen) MU Sigma Netapp Opera Solutions Oracle Pentaho Platfora Qliktech Quantum Rackspace Revolution Analytics Salesforce SAP SAS Institute Sisense Software AG/Terracotta Soft10 Automation Splunk Sqrrl Supermicro Tableau Software Teradata Think Big Analytics Tidemark Systems VMware (Part of EMC)
CloudInfrastructure Cloud Infrastructure and Services 35 hours Audience IT architects, IT consultants Format of the course Part lecture, part discussion, hands-on practice 1. Introduction to Cloud Computing How did we get here -  From application hosting to SaaS to public & private cloud Cloud definition Chose your flavor: IaaS, PaaS, SaaS Typical cloud usage scenarios SaaS vs. traditional enterprise computing The programmable Web: an API in the cloud 2. Cloud migration Better utilization through resource virtualization Cloud management for elasticity: automated, on-demand provisioning of resources Evolving the economy of scale through shared infrastructure and applications Cloud benefits and challenges 3. IaaS (Infrastructure as a Service) IaaS architecture and key features Who are the major IaaS providers? Microsoft Windows Azure Web Roles & Worker Roles Scalability, load balancing, fail over Amazon Web Services (AWS) Elastic Compute Cloud (EC2) & Amazon Machine Images (AMI) IaaS+: AWS Application Services and Marketplace Regions & Availability Zones Networking & security Monitoring, Auto Scaling, & Load Balancing Building scalable and fault-tolerant applications How to protect yourself from outages Management interfaces Private & hybrid cloud Drivers & challenges Defining the requirements A Methodology for building a private cloud How to manage the private cloud Who can help: vendor overview VMware Abiquo Amazon Virtual Private Cloud Hybrid clouds Use cases Product example: Eucalyptus How to select a private cloud model 4. PaaS (Platform as a Service) PaaS defined A complete PaaS stack Where to draw the line: IasS+ or pure-PaaS or custom-SaaS? What functionality do we need to build applications for the cloud? Multi-Tenancy What is a multi-tenant system? Evolving the economy of scale Customizing the application for a tenant Considerations for multi-tenant applications:     Stability, SLA, legal & regulatory, security, maintenance, 3rd-party components Who are the major PaaS providers? Microsoft Windows Azure Google App Engine Force.com Outlook: the future of PaaS 5. SaaS (Software as a Service) What is SaaS? Some examples. 6. Cloud Architecture Service Oriented Architecture (SOA) Service Layer Model & the concept of loose coupling SOA + Event Driven Architecture (EDA) = e-SOA What is REST and why is it important for the cloud? Synergy of SOA and Cloud - the industry view SOA / SaaS synergy SOA / PaaS synergy Approaches to meet demand Applying SOA principles to the cloud: loose coupling, encapsulation, asynchronous services Building multi-tenancy applications based on SOA Migrating legacy systems into the cloud SOA / IaaS synergy Service-Oriented Infrastructure (SOI) Service virtualization vs. server virtualization Automated, on-demand resource provisioning 6. Cloud Integration The need for cloud integration and its challenges How SOA can help: focus on integration From application integration to Service Oriented Integration (SOI) The need for (inter)mediation Mediation functionality Enterprise Service Bus (ESB) reference architectures What are the particular requirements for cloud integration? From ESB to “Internet Service Bus” Product Examples: Windows Azure AppFabric IBM Cast Iron Fiorano 7. Standards and Open Source Software Cloud standards Portability & interoperability: problem statement Distributed Management Task Force, Inc. (DMTF) Open Virtualization Format (OVF) Open Cloud Standards Incubator Apache Libcloud Open Source Software (OSS) OpenStack 8. Cloud Security The evolution to Cloud Security From traditional Web applications to SOA to Cloud Public cloud vs. on-premise datacenter Cloud security is a multi-dimensional problem Dimension 1: IaaS, PaaS, SaaS Dimension 2: Network, VM, application, data Dimension 3: CSP, tenant Identity, Entitlement & Access Management (IdEA) Authentication & Access Control SAML, XACML, and Policy Enforcement Point (PEP) Security across on-premise systems & multiple Clouds Cloud Security Alliance standards Cloud Controls Matrix, Consensus Assessments Initiative, Cloud Audit, Cloud Trust Protocol Security, Trust, and Assurance Registry 9. Governance for Cloud-based Services Business vs. IT vs. EA vs. SOA vs. Cloud Governance Why SOA governance can (should) be the basis for Cloud governance SOA governance frameworks, standards, technologies Open Group’s Service Integration Maturity Model (OSIMM) Open Group SOA Governance Reference Model (SGRM) SOA Governance Vitality Method (SGVM) Cloud governance Similarities and differences to SOA governance Delineating responsibilities: cloud provider vs. cloud customer Switching cloud providers – the worst case test for your governance A Cloud governance methodology Technologies for implementing governance 10.Outlook and Conclusions Outlook and usage for cloud computing Hadoop – gaining popularity in the Cloud Cloud Return on Investment (ROI) Total Cost of Ownership (TCO)
cloudcpt It is all about Cloud: Key Concepts, Players, and Technologies 21 hours Audience IT architects, mid-level IT managers, IT consultants Format of the course Currently 100% lectures. 1.Introduction to Cloud Computing How did we get here -  From application hosting to SaaS to public & private cloud Cloud definition Chose your flavor: IaaS, PaaS, SaaS A cloud reference architecture Typical cloud usage scenarios SaaS vs. traditional enterprise computing The programmable Web: an API in the cloud Moving into the cloud Better utilization through resource virtualization Cloud management for elasticity: automated, on-demand provisioning of resources Evolving the economy of scale through shared infrastructure and applications Cloud benefits and challenges 2.Infrastructure as a Service (IaaS) IaaS architecture and key features What to look for when selecting an IaaS provider? Overview of major IaaS providers IaaS examples Microsoft Windows Azure Web Roles & Worker Roles Scalability, load balancing, fail over Amazon Web Services (AWS) Elastic Compute Cloud (EC2) & Amazon Machine Images (AMI) IaaS+: AWS Application Services and Marketplace Regions & Availability Zones Networking & security Monitoring, Auto Scaling, & Load Balancing Building scalable and fault-tolerant applications The big AWS outage & how to protect yourself Management interfaces 3.Private & Hybrid Cloud Private cloud: drivers & challenges Defining the requirements A Methodology for building a private cloud How to manage the private cloud Who can help: vendor overview VMware Abiquo Amazon Virtual Private Cloud Hybrid clouds Use cases Product example: Eucalyptus How to select a private cloud model 4.PaaS: Key Concepts & Major Players PaaS defined A complete PaaS stack Where to draw the line: IasS+ or pure-PaaS or custom-SaaS? What functionality do we need to build applications for the cloud? Multi-Tenancy What is a multi-tenant system? Evolving the economy of scale Customizing the application for a tenant Considerations for multi-tenant applications: Stability, SLA, legal & regulatory, security, maintenance, 3rd-party components A detailed look at major PaaS providers: Microsoft Windows Azure Google App Engine Force.com Outlook: the future of PaaS 5.Synergy of SOA and Cloud Computing Services and SOA defined Service Layer Model & the concept of loose coupling SOA + Event Driven Architecture (EDA) = e-SOA What is REST and why is it important for the cloud? Synergy of SOA and Cloud - the industry view SOA / SaaS synergy SOA / PaaS synergy Approaches to meet demand Applying SOA principles to the cloud: loose coupling, encapsulation, asynchronous services Building multi-tenancy applications based on SOA Migrating legacy systems into the cloud SOA / IaaS synergy Service-Oriented Infrastructure (SOI) Service virtualization vs. server virtualization Automated, on-demand resource provisioning 6.Cloud Integration The need for cloud integration and its challenges How SOA can help: focus on integration From application integration to Service Oriented Integration (SOI) The need for (inter)mediation Mediation functionality Enterprise Service Bus (ESB) reference architectures What are the particular requirements for cloud integration? From ESB to “Internet Service Bus” Product Examples: Windows Azure AppFabric IBM Cast Iron Fiorano 7.Standards and Open Source Software Cloud standards Portability & interoperability: problem statement Distributed Management Task Force, Inc. (DMTF) Open Virtualization Format (OVF) Open Cloud Standards Incubator Apache Libcloud Open Source Software (OSS) OpenStack 8.Securing the Cloud The evolution to Cloud Security From traditional Web applications to SOA to Cloud Public cloud vs. on-premise datacenter Cloud security is a multi-dimensional problem Dimension 1: IaaS, PaaS, SaaS Dimension 2: Network, VM, application, data Dimension 3: CSP, tenant Identity, Entitlement & Access Management (IdEA) Authentication & Access Control SAML, XACML, and Policy Enforcement Point (PEP) Security across on-premise systems & multiple Clouds Cloud Security Alliance standards Cloud Controls Matrix, Consensus Assessments Initiative, Cloud Audit, Cloud Trust Protocol Security, Trust, and Assurance Registry 9.Governance for Cloud-based Services Business vs. IT vs. EA vs. SOA vs. Cloud Governance Why SOA governance can (should) be the basis for Cloud governance SOA governance frameworks, standards, technologies Open Group’s Service Integration Maturity Model (OSIMM) Open Group SOA Governance Reference Model (SGRM) SOA Governance Vitality Method (SGVM) Cloud governance Similarities and differences to SOA governance Delineating responsibilities: cloud provider vs. cloud customer Switching cloud providers – the worst case test for your governance A Cloud governance methodology Technologies for implementing governance 10.Outlook and Conclusions Outlook and usage for cloud computing Hadoop – gaining popularity in the Cloud Cloud Return on Investment (ROI) Total Cost of Ownership (TCO)
osv OSv: Operating System for the Cloud 7 hours OSv is an open source operating system for the cloud. In this instructor-led, live training, participants will learn how OSv compares to conventional virtualization technologies and approaches as they set up an instance of OSv in a lab environment. By the end of this training, participants will be able to: Run OSv on minimal memory and cpu overhead, compared to traditional OSs Install, configure and troubleshoot OSv Reduce the size and number of running OS instances Run large applications such as Cassandra on OSv Audience System administrators Format of the course Part lecture, part discussion, exercises and heavy hands-on practice To request a customized course outline for this training, please contact us.
cloudarch Cloud Architect 35 hours Day 1 - provides end-to-end coverage of fundamental cloud computing topics as they relate to both technology and business. The module is divided into a series of sections, each of which is accompanied by a hands-on exercise. Day 2 - explores technology-related topics that relate to cloud computing platforms. It does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that address cloud service architecture, cloud security threats and technologies, virtualization and data processing. Day 3 - provides a technical insight into foundational cloud computing platforms. Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) environments are explored as compound patterns, comprised of unique and shared building blocks. This module is structured as a guided tour through these architectural layers, describing primary components, highlighting shared components and explaining how building blocks can be assembled and implemented via cloud computing mechanisms and practices Day 4 - builds upon Day 3 to provide a deep dive into elastic, resilient and multitenant technology architectures, as well as specialized solution architectures, such as cloud bursting and cloud balancing. Through the study of architectural mechanisms, industry technologies and design patterns, both core and extended components are described that combine to realize elasticity, resiliency and multitenancy as primary characteristics of cloud platforms. By leveraging these native and enhanced scalability and failover-related feature-sets, specialized solution architectures are described to enable bursting between clouds and on-premise and cloud environments, as well as the balancing of runtime loads across clouds for performance and failover purposes. Day 5 - presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered previously. Day 1 - Fundamental Cloud Computing Fundamental Cloud Computing Terminology and Concepts Basics of Virtualization Specific Characteristics that Define a Cloud Understanding Elasticity, Resiliency, On-Demand and Measured Usage Benefits, Challenges and Risks of Contemporary Cloud Computing Platforms and Cloud Services Cloud Resource Administrator and Cloud Service Owner Roles Cloud Service and Cloud Service Consumer Roles Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) Cloud Delivery Models Combining Cloud Delivery Models Public Cloud, Private Cloud, Hybrid Cloud and Community Cloud Deployment Models Business Cost Metrics and Formulas for Comparing and Calculating Cloud and On-Premise Solution Costs Service Level Agreements (SLAs) for Cloud-based IT Resources Formulas for Calculating and Rating SLA Quality of Service Characteristics Cloud Technology Concepts Cloud Computing Mechanisms that Establish Architectural Building Blocks Virtual Servers, Ready-Made Environments, Failover Systems, and Pay-for-Use Monitors Cloud Balancing and Cloud Bursting Architectures Common Risks, Threats and Vulnerabilities of Cloud-based Services and Cloud-hosted Solutions Cloud Security Mechanisms Used to Counter Threats and Attacks Understanding Cloud-Based Security Groups and Hardened Virtual Server Images Cloud Service Implementation Mediums (including Web Services and REST Services) Cloud Storage Benefits and Challenges Cloud Storage Services, Technologies and Approaches Non-Relational (NoSQL) Storage Compared to Relational Storage Cloud Service Testing Considerations and Testing Types Day 3 - Fundamental Cloud Architecture Technology Architectural Layers of Cloud Environments Public and Private Cloud Technology Architecture laaS, PaaS and SaaS Technology Architecture Cloud Computing Mechanisms as part of Platform and Solution Technology Architectures Bare-Metal and Elastic Disk Provisioning Multipath Resource Access, Broad Access and Intelligent Automation Engines Usage and Pay-as-You-Go Monitoring Platform Provisioning and Rapid Provisioning Resource Management and Realtime Resource Availability Shared Resources, Resource Pools and Resource Reservation Self-Service and Usage and Administration Portals Workload Distribution and Service State Management Other technology architecture topics pertaining to cloud platforms, cloud-based solutions and services may also be explored. Advanced Cloud Architecture Elastic Environment Resilient Environment Multitenant Environment Direct I/O Access and Direct LUN Access Dynamic Data Normalization Zero Downtime and Storage Maintenance Window Load Balanced Virtual Servers Burst In, Burst Out and Cloud Bursting Cloud Balancing Redundant Storage and Storage Workload Management Elastic Disk Provisioning, Elastic Resource Capacity and Elastic Network Capacity Intra-Storage and Cross-Storage Device Vertical Tiering Redundant Physical Connections for Virtual Servers and Persistent Virtual Network Configurations Load Balanced Virtual Switches and Service Load Balancing Hypervisor Cluster Dynamic Failure and Recovery Synchronized Operating State Resource Reservation Other technology architecture topics pertaining to cloud platforms, cloud-based solutions and services may also be explored. Day 5 - Cloud Architecture Lab As a hands-on lab, this module provides a set of detailed exercises, that require participants to solve a number of inter-related problems, with the ultimate goal of evaluating, designing and correcting technology architectures to fulfill specific sets of solution and business automation requirements.
lcsmcentos Linux Cluster and Storage Management on CentOS 6 & 7 35 hours Created Linux Administrators and developers who are interested with getting involved in Clustering or require knowledge of Clustering based on Linux system. Even beginners, who have the basic skill and knowledge on Linux, can catch up with this course just if you follow the instructor's lab and explanation in detail. This course is intended to practice enough clustering technology and to show it is very easy to understand the clustering technology on Linux system. This course will be delivered to audience with 40% lectures, 50% labs and 10% Q&A. This five-day course strongly emphasizes lab-based activities. You'll learn how to deploy and manage shared storage and server clusters that provide highly available network services to a mission-critical enterprise environment. It can be deliver on any distribution (CentOS and Ubuntu are commonly used) This course covers these kinds of topics: Chapter 01 Linux Cluster Introduction Chapter 02 Data Storage and Cluster Configuration Considerations Chapter 03 iSCSI Configuration Chapter 04 Device Mapper and Multipath Chapter 05 Linux Cluster Configuration with Conga Chapter 06 Linux Cluster Configuration with CCS Chapter 07 Fencing and Failover Domain Chapter 08 Quorum and Quorum Disk Chapter 09 Cluster Logical Volume Management Chapter 10 Global File System 2 Chapter 11 Samba Cluster Chapter 12 Apache Cluster with Conga and CCS Chapter 13 Database Cluster with MySQL Chapter 14 Linux Cluster using Pacemaker Chapter 15 Apache Cluster using Pacemaker Chapter 16 Linux Cluster using PCSD Web UI Chapter 17 Database Cluster with MariaDB
cloudsaasiaas Cloud, SaaS, IaaS - Practical Overview of Available Options 35 hours This course is created for people who face choices which solution to choose for a specific problem. IT Managers, Solution Architects, Test Managers, System Administrators and Developers can benefit from this course by understanding the benefits and costs of available Cloud/SaaS/Iaas solutions. Overview of Cloud Virtalization (e.g. VirtualBox, WMware, KVM...) Hardware support for virtalization (sharing networki interfaces, etc...) Share nothing storage (S3, Ceph, Glacier) Mixed model (Bare Metal + Cloud) Public Cloud Providers Amazon Azure Google Aliyun UnitedStack Private Cloud Solutions OpenStack Amazon EC2 Ohters Software as a Service Benefits over deployable software Constomer isoaltion Legal aspects influencing solution Redunancy Availability Managing upgrades, versionsing, etc... Deployment options (BeanStalk, etc...) Redundant Databases NoSQL (e.g. MongoDB) SQL/NewSQL (e.g. Galera Cluster) Automate redundancy management with RDS Pros vs Cons Dealing with transactioons and consistency Hadoop Redundant WebServers Loadbalacing DNS load balacing (roundrobin, geo-proximity, etc..., e.g. Route53) Session handling Virtual Image Management (Appliances) Image formats Transfering images between zones Image interoperability between clouds
awsarch1 AWS Architect Certification 14 hours On demand AWS Architect Certification training course is designed to help professionals to become cloud-enabled using Amazon Web Services. This course is taught with real life examples, helps participants understand the practical application of concepts such as fundamentals of cloud computing, Amazon Web services (AWS), Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Private Clouds and Cloud programming. After this course participants will be able to have their own implementations on cloud using EC2 instances, S3 buckets etc. Introduction to Cloud Computing Amazon EC2 and Amazon EBS  Amazon Storage Services : S3, RRS, CloudWatch Scaling and Load Distribution in AWS AWS VPC & Route 53 Identity and Access Management Techniques (IAM) and Amazon Managed Relational Database (RDS) Multiple AWS Services and Managing the Resources' Lifecycle AWS Architecture and Design Migrating to Cloud & AWS Case Study
cp100a Google Cloud Platform Fundamentals: Core Infrastructure 8 hours This one-day instructor-led class provides an overview of Google Cloud Platform products and services. Through a combination of presentations, demos, and hands-on labs, participants learn the value of Google Cloud Platform and how to incorporate cloud-based solutions into business strategies. This course teaches participants the following skills: Identify the purpose and value of Google Cloud Platform products and services Interact with Google Cloud Platform services Describe ways in which customers have used Google Cloud Platform Choose among and use application deployment environments on Google Cloud Platform: Google App Engine, Google Container Engine, and Google Compute Engine Choose among and use Google Cloud Platform storage options: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore Make basic use of BigQuery, Google’s managed data warehouse for analytics This class is intended for the following: Individuals planning to deploy applications and create application environments on Google Cloud Platform. Developers, systems operations professionals, and solution architects getting started with Google Cloud Platform. Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs. The course includes presentations, demonstrations, and hands-on labs. Module 1: Introducing Google Cloud Platform Explain the advantages of Google Cloud Platform Define the components of Google's network infrastructure, including: Points of presence, regions, and zones Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) Module 2: Getting Started with Google Cloud Platform Identify the purpose of projects on Google Cloud Platform. Understand the purpose of and use cases for Identity and Access Management. List the methods of interacting with Google Cloud Platform. Lab: Getting Started with Google Cloud Platform. Module 3: Google App Engine and Google Cloud Datastore Understand the purpose of and use cases for Google App Engine and Google Cloud Datastore. Contrast the App Engine Standard environment with the App Engine Flexible environment. Understand the purpose of and use cases for Google Cloud Endpoints. Lab: Deploying Applications Using App Engine and Cloud Datastore. Module 4: Google Cloud Platform Storage Options Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, and Google Cloud Bigtable. Learn how to choose between the various storage options on Google Cloud Platform. Lab: Integrating Applications with Google Cloud Storage. Module 5: Google Container Engine Define the concept of a container and identify uses for containers. Identify the purpose of and use cases for Google Container Engine and Kubernetes. Deploying Applications Using Google Container Engine. Module 6: Google Compute Engine and Networking Identify the purpose of and use cases for Google Compute Engine. Understand the various Google Cloud Platform networking and operational tools and services. Lab: Deploying Applications Using Google Compute Engine. Module 7: Big Data and Machine Learning Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms. Lab: Getting Started with BigQuery.  
dclfd1 Developing for Cloud Foundry 35 hours Cloud Foundry is the industry standard open-source platform as a service (PaaS) that provides you with a choice of clouds, developer frameworks, and application services. Audience This course is directed at engineers and developers who want to develop applications to be deployed through Cloud Foundry. cf Command Line Interface Installing the cf Command Line Interface Getting Started with the cf CLI Using the cf CLI with an HTTP Proxy Server Using the cf CLI with a Self-Signed Certificate Using cf CLI Plugins Developing cf CLI Plugins About Starting Applications Develop and Manage Applications Considerations for Designing and Running an Application in the Cloud Deploy an Application Deploying a Large Application Deploying with Application Manifests Scaling an Application Using cf scale Routes and Domains Stacks Cloud Foundry Environment Variables Using Blue-Green Deployment to Reduce Downtime and Risk Application Logging in Cloud Foundry Troubleshooting Application Deployment and Health Application SSH Overview Accessing Apps with SSH Accessing Services with SSH Identifying your Cloud Foundry API Endpoint and Version Cloud Foundry Concepts Cloud Foundry Overview Cloud Foundry Components Diego Architecture Differences Between DEA and Diego Architectures How Diego Allocates Work Four Levels of High Availability How Applications are Staged Understanding Diego SSH Scaling Cloud Foundry Orgs, Spaces, Roles, and Permissions Cloud Foundry Security Using Docker in Cloud Foundry Integrating Service Instances with Applications Services Overview Delivering Service Credentials to an Application Managing Service Instances with the CLI Managing Service Keys User-Provided Service Instances Manage Application Requests with Route Services Configuring Play Framework Service Connections Migrating a Database in Cloud Foundry Using Third-Party Log Management Services Service-Specific Instructions for Streaming Application Logs Integrating Cloud Foundry with Splunk Custom Services Custom Services Overview Service Broker API Managing Service Brokers Access Control Catalog Metadata Dashboard Single Sign-On Example Service Brokers Binding Credentials Application Log Streaming Route Services Supporting Multiple Cloud Foundry Instances   Buildpacks Buildpacks Buildpack Detection Custom Buildpacks Packaging Dependencies for Offline Buildpacks Configuring a Production Server  
dpcloud Deploying Cloud Foundry 35 hours Cloud Foundry is the industry standard open-source platform as a service (PaaS) that provides you with a choice of clouds, developer frameworks, and application services. Audience This course is directed at engineers, architects and developers interested in deploying and maintaining Cloud Foundry as cloud infrastructure for their applications. Deploying Cloud Foundry Overview of Deploying Cloud Foundry Create a Deployment Manifest for Cloud Foundry Deploying Cloud Foundry using BOSH Scaling Cloud Foundry Log Drain Blacklist Configuration Security Configuration for Consul Deploying Community Services Deploying Cloud Foundry on AWS Setting up an AWS Environment for Cloud Foundry with BOSH AWS Bootstrap Deploying BOSH on AWS Customizing the Cloud Foundry Deployment Manifest Stub for AWS Cloud Foundry Concepts Cloud Foundry Subsystems Overview Cloud Foundry Components Diego Architecture Differences Between DEA and Diego Architectures How the Diego Auction Allocates Jobs Four Levels of High Availability How Applications Are Staged Understanding Application SSH Orgs, Spaces, Roles, and Permissions Cloud Foundry Security Stacks Using Docker in Cloud Foundry Cloud Foundry Glossary Run and Troubleshoot Cloud Foundry Overview Creating and Modifying Quota Plans Cloud Foundry Logging Configuring System Logging Usage Events and Billing Configuring SSH Access for Cloud Foundry Monitoring and Testing Diego Components Troubleshooting Cloud Foundry Troubleshooting Applications Troubleshooting Wardenized Services Troubleshooting Diego for Windows Administer Cloud Foundry Adding Buildpacks to Cloud Foundry Managing Domains and Routes Creating and Managing Users with the cf CLI Creating and Managing Users with the UAA CLI (UAAC) Getting Started with the Notifications Service Application Security Groups Feature Flags Enabling IPv6 for Hosted Applications Securing Traffic into Cloud Foundry​ UAA API BOSH

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