By Gurubaran Baskaran, Digital Marketer, CloudFabrix
For a long time, IT Ops teams have been trying
to keep up with the advancements in data analytics and management. In certain
organizations, this problem is charged to DataOps teams. .These teams are
tasked with managing data growth and complexity as well as keeping pace with
new technologies like Artificial Intelligence for IT Operations (AIOps).
The task at hand continues to get harder
because modern complex systems are difficult to observe due to their dynamic
and distributed nature. With observability, everything around data gets tricky.
DataOps teams have to respond to multi-source data collection, uniform data
processing, and storage and generate real-time insights. It can get extremely
difficult to understand the interactions between various systems and the impact
they are having on cost, SLAs, outages, etc.
The DataOps problem is that there is no tool
for collecting disparate data all together in one place: from storage,
processing, analysis, or building insights on top of it.
What is the solution?
RDA-enabled DataOps and AIOps
AIOps solves this by combining AI & ML
algorithms into an intelligent way of mining operations data to build predictive
models about what will happen next - without any manual intervention.
This is where RDA-enabled DataOps and AIOps
come into the picture. And, that's because what if we want something more
intelligent than just canned responses or manual input? ML algorithms are used
now by enterprises primarily as automated decision-maker (AIDM) rather than
traditional human intelligence augmentation tools (HIAUT). They extract value
from unstructured datasets by analyzing patterns that humans might not see
without RDA.
For mundane and linear business processes,
enterprises use RPA. But, what can come to the rescue of DataOps and AIOps? Did
you think of ETL? We have something better to share. Robotic Data
Automation(RDA) automates DataOps and AIOps, making them as intelligent as you
want them to be.
The first issue is data collection. DataOps
teams collect IT Ops and related data in a variety of ways, from manual entry
to automated assets, logs, metrics, traces and alerts. But the process is not
perfect as errors are often introduced inadvertently during this phase. This
causes inaccuracies or incomplete datasets that need corrections manually later
on.
AIOps data pipelines typically consist of the
following dimensions:
1. Data collection - the ability to collect alerts,
metrics, logs and traces from broad set of ITOM tools
2. Context enrichment from additional sources
like CMDB, vendor systems, CI/CD tools and more.
3. Correlate, deduplicate to suppress noise
and surface only actionable situations with real-time causality analysis
4. Incident management and automation -the
ability to course-correct in flight.
5. Asset Intelligence - To understand service
dependencies and correlate alerts across the stack to resolve incidents.
6. Communication (visual or textual).
The data pipeline is a critical component of
AIOps. It is the glue that binds all other components together and ties the
whole ecosystem together. DataOps teams need to develop their own pipelines for
each use case, which often means using several different technologies to
collect various types of IT Ops data in one place so they can be processed
efficiently.
RDA in action
The Robotic Data automation process is similar
to how developers use Integrated Development Environment (IDEs) to write code,
iterate, compile and build images, which are then pushed onto a runtime
environment.
RDA provides a collaborative Jupyter style
notebook authoring tool and workflow visualization tool. Using this data
pipelines can be built or customized, and once finalized they can be pushed
onto the production AIOps platform. For workflow authoring, RDA is also
available as a standalone tool that can be deployed in a customer's own
environment or it can be used as a service within CloudFabrix's cloud-hosted
environment.
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ABOUT THE AUTHOR
Gurubaran Baskaran is a Digital Marketer at CloudFabrix.
Additional Resources:
Robotic Data Automation
AIOps Studio
Artificial Intelligence for IT Operations
Incident
Management