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Datadog + Cloud Computing Feature Explanation > Datadog Features > Flashcards

Datadog Features Flashcards

1
Q

Containers + Kubernetes

A

Real time view of health of containerized environments

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2
Q

IoT

A

agent that monitors physical device connected to the internet

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3
Q

Distributed tracing

A

Execution path of a request

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4
Q

Continuous profiler

A

observe application process and measure code performance

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5
Q

Deployment tracking

A

breakdown requests tied to the continuous profiler

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6
Q

Serverless monitoring

A

metrics, traces, logs monitoring of a cloud model in which the cloud provider takes care of all the server-related infrastructure and maintenance

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7
Q

Agent

A

Brings data from server into DD
Installed at the OS
Collects system-level metrics (RAM, storage, CPU)

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8
Q

APM focuses on

A 
Monitor performance metrics of applications
Metrics:
- Requests (load)
- Error
- Duration/Latency
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9
Q

ETE Tracing

A

End to end

connect traces to infrastructure metrics

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10
Q

Tracing w/o limits

A 
ingest, search, analyze 100% of traces
Complete traces: 
- priority to errors
- high latency
- low volume end points
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11
Q

Traces

A

the WHY
record of request on a application
show how long each step took to execute

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12
Q

Logs

A

the WHAT

record/trail of something that happened in the stack

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13
Q

SIEM

A

point out vulnerabilities in your code

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14
Q

Synthetic monitoring

A

Proactive
simulate different types of traffic
focuses on availability, application load/response times

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15
Q

CI/CD pipeline

A

catch issues before code makes it to staging and production

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16
Q

NPM

A

live network traffic between cloud & containerized environments
identify root cause of application if the network is at fault

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17
Q

NDM

A

live hardware network traffic between devices like routers, firewalls, switches

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18
Q

Auto-detection of cloud endpoints

A

Assesses which client-side applications are affected by they degradation of storage the depend on in the cloud

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19
Q

App-layer insight

A

health traffic of two endpoints

network metrics: TCP, latency, connection churn

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20
Q

DNS

A

domain name server

over view of server performance

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21
Q

RUM

A 
REACTIVE
focuses on end-user performance including:
- appearance
- load-times
- error
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22
Q

Mobile RUM

A

measure end user experience on web applications

* Runs on EDGE

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23
Q

Events

A

text record for bigger, less frequent moments

24
Q

Log parsing

A

refining and cleaning raw data into a standardized format

25
Q

Log indexing

A

storing clean logs

26
Q

Tags

A

each host & container has the same tag

27
Q

Attributes & facets

A

provide additional log related details IE: timestamp

28
Q

Roll

A

downgrading older data to a lower granularity

29
Q

Monitor alert

A

condition based alerts

30
Q

Metric alert

A

when a threshold is met OR when a metric value changes over a specified period of time

31
Q

Anomaly alert

A

when there is a irregularity compared to historical patterns

32
Q

Outlier alert

A

when a user is behaving differently that that of the group

33
Q

Forecast alert

A

when a metric is projected to cross a threshold in the future

34
Q

Composite alert

A

grouping multiple monitored alerts into 1 main alert

35
Q

Watchdog

A

automated detection monitoring

36
Q

Postmortem notebooks

A

summary of MTTD & MTTR data

37
Q

SLO

A

service level object
focused on end-user
reliability and performance goals of a service over a specific period of time

38
Q

SLI

A

service level indicator
focused on end-user
metrics about the service

39
Q

Error budgets

A

measure how much unreliability is allowed before an SLO or SLA is violated

40
Q

Burn rate

A

measure how fast an error budget is being consumed

41
Q

Infrastructure list

A

overview of all hosts monitored by DD

42
Q

Host map

A

visualize hosts

43
Q

Infrastructure monitoring focuses

A 
health and performance of back-end
Metrics:
- CPU
- Memory
- Storage
44
Q

Incident monitoring focuses

A

Get ahead of situation before it happens
Quickly identify issue, what it’s impacting
Quickly understand how to resolve and prevent in the future
(all can be automated)

45
Q

Metrics

A

numerical values

time-series data

46
Q

Distributed metrics

A

percentile aggregations across all of customer’s hosts

47
Q

DogStatsD

A

DD tool to easily collect custom metrics

48
Q

Security monitoring

A

security of infrastructure

49
Q

Line graph

A

time-series data

50
Q

Stacked areas graph

A

summarizes multiple data points into 1 data point

51
Q

Bar graph

A

count of a certain metric

52
Q

Heat map

A

evolution of a metric overtime

53
Q

Infrastructure Monitoring includes

A
  • hosts/clouds/vm
  • containers
  • processes
  • serverless
  • IoT
54
Q

What are the market expectations of an APM product?

A
  • distributed tracing
  • exception tracking
  • auto-instrumentation
  • code profiling
  • RUM
  • synthetics
55
Q

Which pillar?

If something goes wrong, WHAT exactly happened?

A

Logs

56
Q

Which pillar?

WHY is the code doing that? Is that what the code is supposed to?

A

Traces

57
Q

Which pillar?

Are the infrastructure and applications doing what they’re supposed to?

A

Metrics

Datadog + Cloud Computing Feature Explanation (2 decks)

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