Agents And Their Types Xmpro Data Stream Designer
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Last updated
Learn about the different agents and their types in the XMPRO Data Stream Designer.
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welcome to another training video from
Mexico in this video we will be
discussing agents and their types agents
are basically a piece of code or logic
which gets encapsulated in Excel Pro
agent framework to become these blocks
which are configurable and reusable
among different use cases we provide a
library of agents which is extensible
and our users have access to the
framework that they can use to create
their own agents and add them to the
library here as you can see agents are
categorized into different categories
there are some fundamental categories
for agents and then there are some which
are derived from them
so I'll go through all of them one by
one so usually a stream starts with a
listener a listener is an agent which
doesn't have any input endpoint it would
always only have an output and that is
primarily responsible to talk to third
party systems or outside and the system
here and fetch new data as it up as it
becomes available
edan can be agents can be of two types
one is a polling agent which basically
has a polling interval on which can be
let's say five seconds ten seconds the
user is allowed to configure that and
that polling interval is what if he uses
to ping its data source or the third
party system to ask for new data so for
example a sequel listener and would ask
the database for new records that may
have become available in last 10 seconds
and then it would push them down on the
string to the next agent the other type
of listeners is the push base where they
don't have any polling mechanism but
they just subscribe to an endpoint and
as soon as something is published on
that endpoint they receive it and they
pass it down the line over here an
example would be an MQTT listener for
example which subscribes to an MQTT
broker
soon as it receives a data point it just
sends it down to the next agent next
category which is very similar to the
listeners is of context providers
context providers also do not have any
input endpoint hence they're usually
also format the start of your stream how
they are different to listeners is that
context providers work with slow
changing for context or reference data
rather than live data which listeners
are listening for context providers just
look at a reference data source cache
them in memory and whenever live data
which is coming at a speed comes through
they are able to provide that context
wire join or some other way to those
records example would be lets say you
have a device which is sending
temperature or a pressure value it
wouldn't be sending its make model when
was last serviced its location etc
though the things which are not changing
so it would only be sending live data
which are the things which mostly
changed as you would receive that
deference better from your context
providers you will do a join on let's
say a device ID and that way you will be
able to add context to your data next we
have transformations transformation is
basically are always found in the middle
of your stream they are required to
change or transform the data the key
difference here is they would rarely or
most probably never be talking to an
outside system there would always be
internal in memory agents which would
receive data change its shape or form
based on how you have configured them
and then output them alone for example
there is a Dalek and we region
transformation what it would do is it
would allow you to change a data type of
a certain attributes for example you may
want to change a string to a number or
vice-versa then I'll jump on to action
action agents are usually found towards
the end of your data stream they are
your call to actions there again agents
which talk to third party systems
outside the this environment and that is
where you would mostly krigger a work
order or or send a notification etc
action agents usually have an input and
an output endpoint where they can
perform the action they were supposed to
and then they would simply pass the same
data point out on their output so that
if you want to create a pipeline or line
up multiple action agents you can do
that and take multiple call to actions
in one for the same data point then if
we come back and we have a category for
AI n machine learning where you will
find agents which are related to machine
learning algorithms like binary
classification anomaly detection etc so
you can use those if you want to call a
model or get a prediction for your data
set next we also have functions which
are basically agents which call some
mathematical function like Fourier
transform or any signal filtering that
you may have to do lastly we have
recommendations these are action agents
which classify and the recommendation
category basically these are the ones
which you use to integrate to app
designer where you can trigger some
recommendations or resolve them as you
may like so that was a brief explanation
of agents and there are different types
thank you so much for watching