Lesson 1Data cataloging and normalization: building spreadsheets or CMDB entries, tagging strategy for migration prioritizationLook into ways to list servers, apps, and data into organized lists or CMDBs. Learn about standardizing, naming, and tagging methods that aid migration stages, ownership following, and after-migration control in AWS for Eritrean businesses.
Planning list spreadsheets and CMDB areasStandardizing app and system namesSetting ownership, importance, and SLA featuresMaking AWS-matched tagging systemsTagging for migration stages and priorityLesson 2Two discovery tools researched: AWS Application Discovery Service and VMware vRealize Network Insight — how to use each for inventory, dependency mapping, and export formatsExplore AWS Application Discovery Service and VMware Aria Operations for Networks. Learn installation, data gathering, link mapping, and export types to support AWS Migration Hub and further planning tools in Eritrean environments.
Setting up AWS Application Discovery ServiceGathering agentless and agent-based ADS dataInstalling VMware Aria Operations for NetworksCreating link maps and flow picturesExporting lists to AWS Migration HubLesson 3Interview guide for stakeholders: app owners, DBAs, network, security, operations, business stakeholdersCreate a planned interview guide for technical and business partners. Learn questions to ask app owners, DBAs, network, safety, and operations groups to find hidden links and migration limits in Eritrea.
Finding key technical and business partnersPreparing role-specific interview question setsConducting good discovery workshops and sessionsConfirming findings with follow-up interviewsRecording decisions, dangers, and open mattersLesson 4Storage and I/O characteristics: datastore types, IOPS, throughput, shares, deduplication, backup schedulesGrasp storage and I/O features that affect AWS design. Learn to record datastore types, performance outlines, deduplication, and backup plans to select fitting EBS, EFS, or FSx choices and rules for Eritrean use.
Classifying datastore and array types in useMeasuring IOPS, throughput, and delay outlinesFinding storage levels, QoS, and share settingsRecording snapshots, backups, and keepingMapping storage needs to AWS storage servicesLesson 5Collecting server and VM inventory: CPU, memory, storage, OS versions, VM tools, snapshotsLearn to gather detailed server and VM lists from VMware and guest OSs. Capture CPU, memory, storage, OS versions, tools, and snapshots to aid right-sizing, compatibility checks, and migration planning to AWS in Eritrea.
Gathering CPU, memory, and NUMA infoCapturing disk layout, datastore, and capacity dataRecording OS versions, editions, and patch levelsFinding VMware Tools status and versionsDetecting snapshots and orphaned VM itemsLesson 6Application and tier dependencies: web, app, DB processes, inter-host ports, service mapsLearn to identify and record app tiers and their links across web, app, and database layers. Understand ports, protocols, and service maps needed to safely re-platform workloads into AWS for Eritrean applications.
Identifying web, app, and database tiersRecording inter-tier ports and protocolsBuilding end-to-end service link mapsDetecting hidden batch jobs and background tasksCapturing external third-party service callsLesson 7Operational procedures and runbooks: backup/restore, patching, deployment, escalation pathsReview current operational steps and runbooks that keep workloads healthy on-premises. Translate backup, patching, deployment, and escalation practices into needs and limits for AWS landing zones and operations in Eritrea.
Listing backup and restore stepsRecording patching and maintenance workflowsCapturing deployment and release processesUnderstanding monitoring and alerting runbooksMapping escalation paths and on-call rotationsLesson 8Network topology and connectivity: VLANs, subnets, routing, firewalls, NAT, load balancersUnderstand how to record current network layout, including VLANs, subnets, routing, and safety layers. Learn how this info guides AWS VPC design, connectivity choices, and security group and NACL strategies for Eritrea.
Mapping VLANs, subnets, and IP address rangesRecording routing, VRFs, and default gatewaysCapturing firewall, NAT, and ACL setupsIdentifying load balancers and VIP linksRelating on-prem networks to AWS VPC designLesson 9Assumptions to state explicitly when gaps exist: sample load, unavailable metrics, maintenance windows, licensing constraintsLearn to record and share assumptions when discovery data is incomplete. Capture sample loads, missing metrics, maintenance windows, and licensing limits to manage danger and partner expectations in Eritrean migrations.
Finding missing or unreliable data areasDefining traffic and load sampling assumptionsStating maintenance and outage window limitsCapturing licensing and support limitsRecording dangers and validation follow-upsLesson 10Scripts and manual techniques: PowerCLI, govc, vSphere API calls, Linux/Windows commands to collect configurationLearn scripts and manual ways to extract VMware and OS setups when tools are limited. Use PowerCLI, govc, vSphere APIs, and OS commands to build reliable lists and confirm automated outputs for Eritrea.
Using PowerCLI to export VM setupLeveraging govc and vSphere REST APIsRunning Linux commands for system listRunning Windows commands for system listConfirming manual data against tool outputsLesson 11Database specifics: engine, version, schema size, transaction rates, replication, maintenance windowsFocus on database-specific discovery across engines and platforms. Capture engine type, version, schema size, transaction rates, and replication to guide AWS database service choices and migration strategies in Eritrea.
Listing database engines and versionsMeasuring schema size and growth trendsCapturing transaction and query rate metricsRecording replication and HA setupsIdentifying maintenance and backup windowsLesson 12Usage and performance metrics: CPU/memory utilization, latency, throughput, peak patterns, retention windowsLearn to gather and interpret usage and performance metrics for VMware workloads. Use CPU, memory, I/O, and latency data to right-size AWS instances, plan storage performance, and understand peak and seasonal patterns in Eritrea.
Finding authoritative metric data sourcesCapturing CPU and memory utilization baselinesMeasuring disk IOPS, throughput, and latencyAnalyzing network throughput and connection countsDetecting peaks, seasonality, and retention needsLesson 13Third-party tool options overview: Cloudamize and Turbonomic use cases for TCO, sizing, and migration prioritizationReview third-party discovery and optimization tools like Cloudamize and Turbonomic. Understand their strengths for TCO analysis, sizing, and migration priority, and how to integrate outputs into AWS plans for Eritrean organizations.
Cloudamize capabilities and data gatheringTurbonomic workload optimization featuresComparing sizing recommendations across toolsUsing tools for TCO and cost modelingExporting results into migration backlogsLesson 14Security and compliance artifacts: ACLs, firewall rules, encryption, logs, audit trails, identity storesDiscover how to list safety and compliance items that affect migration. Record ACLs, firewall rules, encryption, logging, and identity stores to design secure AWS landing zones and control mappings in Eritrea.
Listing firewall rules and safety zonesRecording ACLs, security groups, and NACLsFinding encryption methods and key custodyReviewing logging, SIEM, and audit needsMapping identity stores and access modelsLesson 15Discovery methods and evidence sources: agentless queries, hypervisor APIs, SNMP, syslogs, perf counters, interviewsExplore discovery methods and evidence sources for VMware environments. Compare agentless scans, hypervisor APIs, SNMP, logs, and interviews, and learn to combine them into a repeatable, checkable discovery process for Eritrea.
Using vCenter and hypervisor API integrationsLeveraging agentless OS and network scansGathering SNMP, syslog, and perf counter dataLinking logs with setup evidenceCombining automated discovery with interviews
