Why map your infrastructure before a cloud migration Before committing to any transition to AWS, a complete map of your current infrastructure is not a luxury: it is an operational prerequisite. Without it, you are moving through unknown territory. An infrastructure that has grown gradually accumulates layers of applications, services, databases and dependencies that are often poorly documented. Teams know what they do day to day, but no one has a grip on the overall picture. When the time comes to migrate, this strategic blindness is costly: you forget a critical dependency, the transition drags on, costs explode, or risks are not anticipated.
Mapping solves this problem by creating a faithful representation of what you actually own. It identifies redundant applications, end-of-life services that no one dares touch, data flows between systems, and above all the hidden dependencies that could paralyze a poorly prepared migration. For a scale-up or mid-sized company whose infrastructure has outgrown the artisanal stage, this visibility is indispensable for moving from technical debt to a costed and realistic action plan. It is also on this map that the cost audit relies (you can only optimize what you measure) and the risk analysis (you can only anticipate what you have documented).
The key elements to document in an infrastructure map An exhaustive map covers several dimensions that together form a faithful portrait of your existing setup. First, the inventory of applications, that is, every service, business application or middleware in production. For each one, note the owner (team, department), the technology used (framework, language, database), its role in the value chain, and above all its business criticality. An application that processes payment transactions does not carry the same weight as an internal administration interface.
Second, document the underlying infrastructure resources: servers (physical or virtual), environments (dev, test, staging, production), databases (relational, NoSQL, data lakes), file systems, networks and firewalls. For each resource, capture its current configuration, its usage (processor, memory, storage), and its growth history if possible. Third, map the data flows between applications: which data moves between which system and which other, at what frequency, what latency, what volume. These flows reveal dependencies. A database shared between two applications that seem independent creates a hidden coupling that complicates the migration.
Fourth, do not neglect external dependencies: third-party APIs, SaaS services, integrations with partners. They represent constraints that are often forgotten. An application that depends on a third-party API with a rate limit will have to go through a traffic management component during the migration. Finally, document the critical configurations: SSL certificates, secret keys, application parameters that differ between environments. These details often decide the success or failure of a transition. A complete map generates inventory documents, flow diagrams, and a dependency matrix that can be updated and consulted throughout the project.
The methods and tools for mapping effectively Mapping an infrastructure is not a simple list to fill in: it is a process that combines several approaches to ensure completeness and precision. The first step is gathering information from the teams. Organize workshops with architects, system administrators, developers and business owners. Each holds pieces of the puzzle: the infrastructure team knows the servers and networks, the developers know which applications talk to each other, the business explains criticality. Without these exchanges, you will inevitably miss hidden dependencies.
Next, leverage the available technical data. Your monitoring tools (Datadog, CloudWatch, Prometheus) already record the network flows between servers. Your configuration management systems (Terraform, CloudFormation, Ansible) partially document your infrastructure. Your identity tools (LDAP, Active Directory) list the access. Your existing IT inventories (CMDB, ServiceNow) contain partial but useful data. The idea is not to reinvent the wheel, but to consolidate what already exists and fill the gaps.
For visualization, use tools dedicated to architecture mapping. Solutions like Lucidchart, Draw.io, or Miro let you create collaborative diagrams that the team can annotate. Specialized infrastructure discovery solutions (such as AWS Application Discovery Service if you are already partially on AWS, or CloudMapper) automate the capture of resources and flows. They generate dependency graphs that would have taken weeks to draw by hand. For more mature organizations, an IT asset management tool coupled with an application dependency analysis tool offers a view that is both technical and business-oriented.
The process of an effective mapping usually follows this framework: phase 1, gathering requirements and defining the scope (do you map everything at once or in stages?), phase 2, collecting data through the four channels (interviews, technical tools, configuration as code, inventories), phase 3, analyzing and consolidating the raw data into a coherent model, phase 4, creating the diagrams and visual representations, phase 5, validating with the teams (each must recognize its domain), and phase 6, documenting and keeping it up to date. Do not make the mistake of believing that a map is set in stone: it must be revised regularly, especially during architecture changes.
Identifying critical dependencies and points of failure Once your infrastructure is mapped, the next step is to analyze the dependencies to identify which ones are critical and which ones could become points of failure during a migration. Dependencies take several forms. Data dependencies are the most obvious: an application that reads a database. If that database is shared with three other applications, you cannot migrate the first without coordinating the other three. Network call dependencies, where application A waits for a response from application B to continue, create critical delays. A code dependency, such as a shared library used by several services, means that an update must be tested and deployed everywhere.
To identify critical dependencies, ask these questions: first, what is the frequency of interaction? An application that exchanges data with another every hour has a less urgent dependency than one that does so 100 times per second. Second, what is the latency tolerance? A payment system does not tolerate an extra 500 milliseconds of latency the way a nightly consolidation batch does. Third, is the dependency one-way or two-way? If system B depends on A but not the reverse, you may be able to migrate B first. If it is reciprocal, you must migrate them together.
A point of failure is a dependency that, if it breaks, paralyzes one or more critical services. Imagine a centralized authentication API used by 20 applications. That service is a point of failure. During the migration, if the API switched to AWS is not compatible with the old applications left on-premises, everything stops. These points of failure must be handled carefully: either you migrate them first (so the dependents can follow), or you create a temporary compatibility shim (a component that translates the old calls into the new versions), or you leave them on-premises longer. Dependency mapping, coupled with your cost audit and your risk analysis, lets you define the optimal migration sequence. It also informs the decision of which type of migration (lift-and-shift, rearchitecture, replatforming) best suits each application or group of applications.
From mapping to the transition plan Mapping is only useful if it feeds the decisions that follow. For a scale-up or mid-sized company preparing a migration, mapping creates the foundations on which the next three steps rely: the cost audit, the risk analysis, and finally the migration plan itself.
The cost audit first requires knowing what you have to cost. Do you have a complete map? You can then estimate the cost of each application on-premises (infrastructure, software licenses, operational labor) and compare it to the equivalent cost on AWS. This analysis often reveals surprises: a critical application that weighs little in on-premises costs but a lot on AWS because of an inefficient architecture, or conversely, a system thought to be cheap that ultimately costs the price of a small team of manual operations.
The risk analysis is built on the dependency map. What are the points of failure? What are the risks of incompatibility during the switchover? Which applications require exhaustive testing before migration? The map provides the raw material to answer these questions. It also helps identify the forgotten dependencies that could cause silent failures: an application that depends on external authentication, a specific network integration, or a non-portable software license.
The migration plan itself flows directly from your map. Have you identified the dependencies? You know in what order to migrate. Have you measured the data flows? You size the bandwidth of the transition tunnel. Have you documented the critical configurations? You prepare the parameter mappings for each environment. A precise map turns a migration of several quarters of trial and error into a structured project that you master and cost in weeks.
That is why, in an infrastructure assessment approach, mapping is the first step toward regaining control. It answers the question: before migrating, I must know what I own and how it holds together. Once this answer is documented, the difficult decisions become possible.