|
Availability %
|
Downtime per year
|
Downtime per month
|
Downtime per week
|
Downtime per day
|
|
90% ("one
nine")
|
36.5 days
|
72 hours
|
16.8 hours
|
2.4 hours
|
|
95% ("one and a
half nines")
|
18.25 days
|
36 hours
|
8.4 hours
|
1.2 hours
|
|
97%
|
10.96 days
|
21.6 hours
|
5.04 hours
|
43.2 minutes
|
|
98%
|
7.30 days
|
14.4 hours
|
3.36 hours
|
28.8 minutes
|
|
99% ("two
nines")
|
3.65 days
|
7.20 hours
|
1.68 hours
|
14.4 minutes
|
|
99.5% ("two and
a half nines")
|
1.83 days
|
3.60 hours
|
50.4 minutes
|
7.2 minutes
|
|
99.80%
|
17.52 hours
|
86.23 minutes
|
20.16 minutes
|
2.88 minutes
|
|
99.9% ("three
nines")
|
8.76 hours
|
43.8 minutes
|
10.1 minutes
|
1.44 minutes
|
|
99.95% ("three
and a half nines")
|
4.38 hours
|
21.56 minutes
|
5.04 minutes
|
43.2 seconds
|
|
99.99% ("four
nines")
|
52.56 minutes
|
4.38 minutes
|
1.01 minutes
|
8.64 seconds
|
|
99.995% ("four
and a half nines")
|
26.28 minutes
|
2.16 minutes
|
30.24 seconds
|
4.32 seconds
|
|
99.999% ("five
nines")
|
5.26 minutes
|
25.9 seconds
|
6.05 seconds
|
864.3 milliseconds
|
|
99.9999% ("six
nines")
|
31.5 seconds
|
2.59 seconds
|
604.8 milliseconds
|
86.4 milliseconds
|
|
99.99999%
("seven nines")
|
3.15 seconds
|
262.97 milliseconds
|
60.48 milliseconds
|
8.64 milliseconds
|
|
99.999999%
("eight nines")
|
315.569 milliseconds
|
26.297 milliseconds
|
6.048 milliseconds
|
0.864 milliseconds
|
|
99.9999999%
("nine nines")
|
31.5569 milliseconds
|
2.6297 milliseconds
|
0.6048 milliseconds
|
0.0864 milliseconds
|
Aspect Data Wrangling (Data Preprocessing) Exploratory Data Analysis (EDA) Objective Prepare raw data for modeling by cleaning, transforming, and formatting it appropriately. Explore and understand the data to gain insights, identify patterns, and make decisions on data handling and modeling. Order Typically performed as a preliminary step before EDA. Usually conducted after data wrangling to further investigate data characteristics. Data Handling Focuses on data cleaning, filling missing values, encoding categorical variables, and scaling features. Involves data visualization, statistical analysis, and summary statistics to uncover patterns, relationships, and anomalies. Techniques Techniques include imputation, outlier detection, feature scaling, and one-hot encoding. Techniques include histograms, scatter plots, box plots, correlation matrices, and descriptive statistics. Data Transformation Involves structural changes to the dataset, such as feature engineering, data normaliz...
Comments