The second step in the data lifecycle is data
Once we’ve collected and recorded an observation
as data, we need to store it so that it can
be retrieved for future analysis.
As data are being recorded by sensors, these
data are first recorded temporarily in a type
of memory called volatile storage.
Volatile storage means that the data are lost
when the device loses power.
As a result, we need to transfer our data
somewhere more permanent, so that they will
be available anytime we need them.
In data science, we store our data in one
of several persistent-storage mediums.
Persistent storage, means that the device
retains the data after the power to the device
has been shut off.
For example, a computer’s hard drive retains
its data even if you turn the power off and
then turn it back on again.
By storing our data in a persistent-storage
medium, we can retrieve our data as needed.
Unless we overwrite the data, or the device
permanently fails, our data should always
Data can be stored in computers in several
First, we have file-based formats – which
store data in files on the file system of a computer.
For example, comma-separate values (or CSV)
files and Excel spreadsheets.
Next, we have web-based formats – which
store data in formats best suited for data
transfer over the internet.
For example, eXtensible markup language (or
Third, we have transactional databases – which
store data in a form best suited for
For example, normalized relational databases
and No-SQL databases.
Forth, we have analytical databases – which
store data in a format best suited for
For example, data warehouses, data marts,
and data cubes.
And finally, we have Big Data platforms – which
can store massive data sets by distributing
both data and processing across many computers
For example, Spark, Hive, and Hadoop.
There are many options to choose from, so
it’s important to know which option is right
for your specific data-storage scenario.