Cloud Computing Training Courses

Cloud Computing Training

Cloud Computing training

Client Testimonials

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

This was the first time I did remote training ever. It went well, better than I expected.

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

He's (the trainer) very flexible and work along with our questions.

Bokhara Bun- Employment and Social Development Canada.

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Apache Solr - Full-Text Search Server

The pace was just right.

Greg Noseworthy - Employment and Social Development Canada

Subcategories

Cloud Computing Course Outlines

ID Name Duration Overview
1881 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 could providers Eucalyptus Digital Ocean Azure Others
2458 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. OpenStack project OpenStack Releases community Swift architecture Authentication resources Glance architecture Support for installation images installation Nova schedule architecture employee calculation employee volumes employee network queue database Getting Nova Available versions and distributions Nova packages Nova Planning the implementation of Nova virtualization Technology Authentication API schedule Service installation images database Volumes Installing Nova Installing a Script Installing the package Using Nova Creating users and projects Uploading installation images Running instances Configuring Network Connectivity Joining volumes Destroying instance Nova Realty configuration files configuration Tools
2571 It’s 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)
2625 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
2624 Apache Solr - Full-Text Search Server 14 hours The course is intended for IT specialist that want to implement a solution that allows for elastic and efficient searching of big data sources. Introduction Apache Lucene What is Solr Installation Schema and textanalysis Schema modeling schema.xml Configuration Text analysis Working with index Importing data from other resources Indexing documents Querying Solr API Searching Basics of querying Sorting and Filtering Using scoring Functions Request handling Formatting Solr response Faceting Advanced topics Configuring and deploying Solr Integrating Solr with other libraries/technologies Search components Solr and scaling issues
85064 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)
85066 IoT (Internet of Things) for Entrepreneurs, Managers and Investors 21 hours 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 objectives 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 IoT-Ayla, iO Bridge, Libellium, Axeda, Cisco frog cloud Studies of business and technology of some of the common IoT devices like Home automation, Smoke alarm, vehicles, military, home health etc Target Audience Investors and IoT entrepreneurs Managers and Engineers whose company is venturing into IoT space Business Analysts & Investors Pre-requisites Should have basic knowledge of business operation, devices, electronics systems and data systems Must have basic understanding of software and systems Basic understanding of Statistics ( in Excel levels) 1. Day 1, 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 2. Day 1, 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. 3. Day 1, 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 Hands on training with sensor network PICO NET- BLE Base network Zigbee network-master/slave communication Data Hubs : MC and single computer ( like Beaglebone ) based datahub 4. Day 1, 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? RedBack, Diamond Back 5. Day 2, Session 1 — 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 6. Day 2, Session 2 — 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 Linkafy Mobile platform for IoT Axeda Xively 7. Day 2, Session 3 — 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 8. Day 2, Session 4 — 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 9. Day 3, Session 1 — 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 10. Day 3, Session 2 — 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 Axeda Xively Omega NovoTech Ayla Libellium CISCO M2M platform AT &T M2M platform Google M2M platform 11. Day 3, Session 3 — 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 12. Day 3, Session 4 — 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 Parallel computing basics for IoT
287801 Docker for Developers and System Administrators 14 hours Docker is a platform for developers and sysadmins to maintain distributed applications. It consists of a runtime to run containers and a service for sharing containers. With docker the same app can run unchanged on laptops, dedicated servers and virtual servers. This course teaches the basic usage of Docker, useful both for developers and system administrators. The course includes a lot of hands on exercises and the participants will practice in their own Docker environment and build their own Docker images during the 2 days. Course and outline author: Gergely Risko. What is Docker? Use cases Major components of Docker Docker architecture fundamentals Docker architecture Docker images Docker registry Docker containers The underlying technology Namespaces Control groups Union FS Container format Installation of Docker Installation on Ubuntu via apt-get installation of newer version of Docker Dockerizing applications The hello world example Interactive container Daemonizing programs Container usage Running a webapp in a container Investigating a container Port mapping Viewing the logs Looking at processes Stopping and restarting Removing a container Managing images Listing images Downloading images Finding images Networking of containers Port mapping details Container linking and naming Linking and environment variables Data in containers Data volumes Host directories as data volume Host file as data volume Data volume containers Backup, restore of data volumes Contributing to the ecosystem What is Docker Hub? Registering on Docker Hub Command line login Uploading to Docker Hub Private repositories Automated builds
287819 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.
287851 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: Linux Cluster Introduction Data Storage and Cluster Configuration Considerations iSCSI Configuration Device Mapper and Multipath Linux Cluster Configuration with Conga Linux Cluster Configuration with CCS Fencing and Failover Domain Quorum and Quorum Disk Cluster Logical Volume Management Global File System 2 Samba Cluster Apache Cluster with Conga and CCS Database Cluster with MySQL Linux Cluster using Pacemaker Apache Cluster using Pacemaker Linux Cluster using PCSD Web UI Database Cluster with MariaDB
287892 Cloud, SaaS, IaaS - pratical 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
Evening Cloud Computing courses, Cloud Computing classes, Cloud Computing on-site, Cloud Computing instructor, Weekend Cloud Computing training,Weekend Cloud Computing courses, Cloud Computing trainer, Cloud Computing one on one training, Cloud Computing training courses, Evening Cloud Computing training, Cloud Computing coaching, Cloud Computing private courses, Cloud Computing boot camp

Some of our clients