Migrating a large number of applications to the public cloud is a complicated process.
There are many strategic questions organizations need to answer, steps to consider and
areas where migrators commonly struggle. In an earlier Doppler article, we discussed
some of these pitfalls and outlined six key enablers that teams should put in place to
ensure a migration process succeeds.
The first two enablers – collecting and analyzing your data – can on their own seem like
overwhelming tasks. Data is stored in a variety of formats. It comes from different
sources. Some of it is up to date, some is not. Data describes different sets of assets, and
the relationships among various data elements and asset types can be difficult to sort
out. How do you make sense of all this information so you can use it to plan the
migration?
One way to approach the data collection and assessment process is the way chefs pre-
pare a meal. Chefs start with “inputs”: raw or pre-cooked ingredients. They use “tools”:
pots, pans, appliances and instruments. They move on to conduct “processes”: a series
of steps that transform raw ingredients into consumable products, which we will call
“outputs.” Before the food is served, a chef may need to put it into temporary “storage”
to preserve freshness until its eventual consumption.
A migration project team uses the resources it has at hand to prepare vast amounts of
data that can be collected, transformed, sampled and repackaged for use and reuse.
Let’s step into our “kitchen” and see how teams can bring data to the table. We'll look at
how they aggregate and correlate it, and understand relationships between assets and
recognize patterns using data analytics tools and techniques, to generate outputs and
prepare for migrations.
Gather Up Your Ingredients
Just like a quality beef stew recipe includes meat, vegetables and a creative array of
spices, a cloud migration project starts with its own distinct set of ingredients. The list is
usually long. It includes morsels of data gathered up from application and database
inventories, server and device inventories, network scanners, configuration manage-
ment database (CMDB) exports, reports from asset discovery tools, server process data,
server communications data, spreadsheets with departmental asset information and
possibly other sources.
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