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2024-02-04T05:13:47.801Z
nova-2
2-general-nova
2024-01-09.29447
30089e05-99d1-4376-b32e-c263170674af
14f57084-692b-4584-8a5b-de56eae24eda
7ca63639988407da8c33c3ce67668161f5037e2ca52b75329b4bb08f687099dc
67875a7f-c9c4-48a0-aa55-5bdb8a91c34a
deprecated
Neo4j, an ACID compliant graph database that can handle the extremely complex relational data of the modern world.
It was created in 2007 by normalized tables together is not how real humans think about relationships.
Naturally, we think about connections between entities.
Like, Bob asked a question on Stack Overflow, then Alice and Chad downvoted it, so Bob gave up on his dream of programming.
A graph can model this data the same way you might visualize it on a white m.
Nodes represent entities, edges represent relationships between entities, and properties are key value pairs that store data on these objects.
MX.
Under the hood, Neo4j is a true native graph database that applies this model all the way down to the storage level.
Queries are written in a declarative language called Cypher that resembles MQL, except nodes are surrounded by parentheses, then connect to other nodes with arrows via relationships and brackets.
Today, Neo 4J is used in applications mix like recommendation engines, social media platforms, and knowledge graphs for artificial intelligence, just to name a few.
To get started, it can be self hosted Amit Docker, but the easiest approach is to sign up for Aura, which provides a free, fully managed database in the cloud.
From there, we can build the next Twitter by making queries with Cypher.
Use create followed by parenthesis to create a new entity or node.
This node has a label of user that can be used to group similar nodes a mis2gether.
Inside the braces, we can add key value pairs to represent the data on that node.
Now a user might want to follow another user, which which can be represented with a relationship inside of brackets.
Then notice how the arrow points to another node with a label of user, and that's all it takes to create relational data.
No foreign keys or join tables required.
Although, we can add constraints to the schema such as the one here that will ensure every username aMimismunique.
In addition, we can define local variables in this query, then return them from the statement to get the data back as the result.
And what's especially awesome is that we can visualize it as an interactive graph or even as a table.
Now, to continue building Twitter, we can connect multiple tweet nodes to a user.
We also need a query that will return all of the tweets from all the users that are being followed.
This can be easily represented with nodes and relationships, but we can also filter this query with a WHERE clause to return tweets that were posted recently.
In addition, we can match on string patterns and regular expressions, or filter on complex patterns based on the graph itself, like only return tweets from users that have not been muted.
So not only is it intuitive for developers, but it also allows patterns in your data to emerge naturally, which is a huge advantage when when it comes to data analysis and machine learning.
This has been Neo4j in 100 seconds.
Hit the like button for more short videos like this.
Thanks for watching, and I will see you in the next one.

Speaker 0: Neo4j, an ACID compliant graph database that can handle the extremely complex relational data of the modern world. It was created in 2007 by normalized tables together is not how real humans think about relationships. Naturally, we think about connections between entities. Like, Bob asked a question on Stack Overflow, then Alice and Chad downvoted it, so Bob gave up on his dream of programming. A graph can model this data the same way you might visualize it on a white m.

Nodes represent entities, edges represent relationships between entities, and properties are key value pairs that store data on these objects. MX. Under the hood, Neo4j is a true native graph database that applies this model all the way down to the storage level. Queries are written in a declarative language called Cypher that resembles MQL, except nodes are surrounded by parentheses, then connect to other nodes with arrows via relationships and brackets. Today, Neo 4J is used in applications mix like recommendation engines, social media platforms, and knowledge graphs for artificial intelligence, just to name a few.

To get started, it can be self hosted Amit Docker, but the easiest approach is to sign up for Aura, which provides a free, fully managed database in the cloud. From there, we can build the next Twitter by making queries with Cypher. Use create followed by parenthesis to create a new entity or node. This node has a label of user that can be used to group similar nodes a mis2gether. Inside the braces, we can add key value pairs to represent the data on that node.

Now a user might want to follow another user, which which can be represented with a relationship inside of brackets. Then notice how the arrow points to another node with a label of user, and that's all it takes to create relational data. No foreign keys or join tables required. Although, we can add constraints to the schema such as the one here that will ensure every username aMimismunique. In addition, we can define local variables in this query, then return them from the statement to get the data back as the result.

And what's especially awesome is that we can visualize it as an interactive graph or even as a table. Now, to continue building Twitter, we can connect multiple tweet nodes to a user. We also need a query that will return all of the tweets from all the users that are being followed. This can be easily represented with nodes and relationships, but we can also filter this query with a WHERE clause to return tweets that were posted recently. In addition, we can match on string patterns and regular expressions, or filter on complex patterns based on the graph itself, like only return tweets from users that have not been muted.

So not only is it intuitive for developers, but it also allows patterns in your data to emerge naturally, which is a huge advantage when when it comes to data analysis and machine learning. This has been Neo4j in 100 seconds. Hit the like button for more short videos like this. Thanks for watching, and I will see you in the next one.
Neo4j, an ACID compliant graph database that can handle the extremely complex relational data of the modern world. It was created in 2007 by normalized tables together is not how real humans think about relationships. Naturally, we think about connections between entities. Like, Bob asked a question on Stack Overflow, then Alice and Chad downvoted it, so Bob gave up on his dream of programming. A graph can model this data the same way you might visualize it on a white m. Nodes represent entities, edges represent relationships between entities, and properties are key value pairs that store data on these objects. MX. Under the hood, Neo4j is a true native graph database that applies this model all the way down to the storage level. Queries are written in a declarative language called Cypher that resembles MQL, except nodes are surrounded by parentheses, then connect to other nodes with arrows via relationships and brackets. Today, Neo 4J is used in applications mix like recommendation engines, social media platforms, and knowledge graphs for artificial intelligence, just to name a few. To get started, it can be self hosted Amit Docker, but the easiest approach is to sign up for Aura, which provides a free, fully managed database in the cloud.
software
software development
data
databases
data analysis
ideologies
data management
artificial intelligence
neural networks
visualization
From there, we can build the next Twitter by making queries with Cypher. Use create followed by parenthesis to create a new entity or node. This node has a label of user that can be used to group similar nodes a mis2gether. Inside the braces, we can add key value pairs to represent the data on that node.
twitter
Now a user might want to follow another user, which which can be represented with a relationship inside of brackets. Then notice how the arrow points to another node with a label of user, and that's all it takes to create relational data.
No foreign keys or join tables required. Although, we can add constraints to the schema such as the one here that will ensure every username aMimismunique. In addition, we can define local variables in this query, then return them from the statement to get the data back as the result. And what's especially awesome is that we can visualize it as an interactive graph or even as a table. Now, to continue building Twitter, we can connect multiple tweet nodes to a user. We also need a query that will return all of the tweets from all the users that are being followed. This can be easily represented with nodes and relationships, but we can also filter this query with a WHERE clause to return tweets that were posted recently. In addition, we can match on string patterns and regular expressions, or filter on complex patterns based on the graph itself, like only return tweets from users that have not been muted.
twitter
databases
So not only is it intuitive for developers, but it also allows patterns in your data to emerge naturally, which is a huge advantage when when it comes to data analysis and machine learning. This has been Neo4j in 100 seconds. Hit the like button for more short videos like this.
machine learning
artificial intelligence
software development
deep learning
neural networks
Thanks for watching, and I will see you in the next one.
Neo4j, an ACID compliant graph database that can handle the extremely complex relational data of the modern world. It was created in 2007 by normalized tables together is not how real humans think about relationships. Naturally, we think about connections between entities. Like, Bob asked a question on Stack Overflow, then Alice and Chad downvoted it, so Bob gave up on his dream of programming. A graph can model this data the same way you might visualize it on a white m. Nodes represent entities, edges represent relationships between entities, and properties are key value pairs that store data on these objects. MX. Under the hood, Neo4j is a true native graph database that applies this model all the way down to the storage level. Queries are written in a declarative language called Cypher that resembles MQL, except nodes are surrounded by parentheses, then connect to other nodes with arrows via relationships and brackets. Today, Neo 4J is used in applications mix like recommendation engines, social media platforms, and knowledge graphs for artificial intelligence, just to name a few. To get started, it can be self hosted Amit Docker, but the easiest approach is to sign up for Aura, which provides a free, fully managed database in the cloud. From there, we can build the next Twitter by making queries with Cypher. Use create followed by parenthesis to create a new entity or node. This node has a label of user that can be used to group similar nodes a mis2gether. Inside the braces, we can add key value pairs to represent the data on that node. Now a user might want to follow another user, which which can be represented with a relationship inside of brackets. Then notice how the arrow points to another node with a label of user, and that's all it takes to create relational data. No foreign keys or join tables required. Although, we can add constraints to the schema such as the one here that will ensure every username aMimismunique. In addition, we can define local variables in this query, then return them from the statement to get the data back as the result. And what's especially awesome is that we can visualize it as an interactive graph or even as a table. Now, to continue building Twitter, we can connect multiple tweet nodes to a user. We also need a query that will return all of the tweets from all the users that are being followed. This can be easily represented with nodes and relationships, but we can also filter this query with a WHERE clause to return tweets that were posted recently. In addition, we can match on string patterns and regular expressions, or filter on complex patterns based on the graph itself, like only return tweets from users that have not been muted. So not only is it intuitive for developers, but it also allows patterns in your data to emerge naturally, which is a huge advantage when when it comes to data analysis and machine learning. This has been Neo4j in 100 seconds. Hit the like button for more short videos like this. Thanks for watching, and I will see you in the next one.
Neo4j,
neo4j
an
an
ACID
acid
compliant
compliant
graph
graph
database
database
that
that
can
can
handle
handle
the
the
extremely
extremely
complex
complex
relational
relational
data
data
of
of
the
the
modern
modern
world.
world
It
it
was
was
created
created
in
in
2007
2007
by
by
normalized
normalized
tables
tables
together
together
is
is
not
not
how
how
real
real
humans
humans
think
think
about
about
relationships.
relationships
Naturally,
naturally
we
we
think
think
about
about
connections
connections
between
between
entities.
entities
Like,
like
Bob
bob
asked
asked
a
a
question
question
on
on
Stack
stack
Overflow,
overflow
then
then
Alice
alice
and
and
Chad
chad
downvoted
downvoted
it,
it
so
so
Bob
bob
gave
gave
up
up
on
on
his
his
dream
dream
of
of
programming.
programming
A
a
graph
graph
can
can
model
model
this
this
data
data
the
the
same
same
way
way
you
you
might
might
visualize
visualize
it
it
on
on
a
a
white
white
m.
m
Nodes
nodes
represent
represent
entities,
entities
edges
edges
represent
represent
relationships
relationships
between
between
entities,
entities
and
and
properties
properties
are
are
key
key
value
value
pairs
pairs
that
that
store
store
data
data
on
on
these
these
objects.
objects
MX.
mx
Under
under
the
the
hood,
hood
Neo4j
neo4j
is
is
a
a
true
true
native
native
graph
graph
database
database
that
that
applies
applies
this
this
model
model
all
all
the
the
way
way
down
down
to
to
the
the
storage
storage
level.
level
Queries
queries
are
are
written
written
in
in
a
a
declarative
declarative
language
language
called
called
Cypher
cypher
that
that
resembles
resembles
MQL,
mql
except
except
nodes
nodes
are
are
surrounded
surrounded
by
by
parentheses,
parentheses
then
then
connect
connect
to
to
other
other
nodes
nodes
with
with
arrows
arrows
via
via
relationships
relationships
and
and
brackets.
brackets
Today,
today
Neo
neo
4J
4j
is
is
used
used
in
in
applications
applications
mix
mix
like
like
recommendation
recommendation
engines,
engines
social
social
media
media
platforms,
platforms
and
and
knowledge
knowledge
graphs
graphs
for
for
artificial
artificial
intelligence,
intelligence
just
just
to
to
name
name
a
a
few.
few
To
to
get
get
started,
started
it
it
can
can
be
be
self
self
hosted
hosted
Amit
amit
Docker,
docker
but
but
the
the
easiest
easiest
approach
approach
is
is
to
to
sign
sign
up
up
for
for
Aura,
aura
which
which
provides
provides
a
a
free,
free
fully
fully
managed
managed
database
database
in
in
the
the
cloud.
cloud
From
from
there,
there
we
we
can
can
build
build
the
the
next
next
Twitter
twitter
by
by
making
making
queries
queries
with
with
Cypher.
cypher
Use
use
create
create
followed
followed
by
by
parenthesis
parenthesis
to
to
create
create
a
a
new
new
entity
entity
or
or
node.
node
This
this
node
node
has
has
a
a
label
label
of
of
user
user
that
that
can
can
be
be
used
used
to
to
group
group
similar
similar
nodes
nodes
a
a
mis2gether.
mis2gether
Inside
inside
the
the
braces,
braces
we
we
can
can
add
add
key
key
value
value
pairs
pairs
to
to
represent
represent
the
the
data
data
on
on
that
that
node.
node
Now
now
a
a
user
user
might
might
want
want
to
to
follow
follow
another
another
user,
user
which
which
which
which
can
can
be
be
represented
represented
with
with
a
a
relationship
relationship
inside
inside
of
of
brackets.
brackets
Then
then
notice
notice
how
how
the
the
arrow
arrow
points
points
to
to
another
another
node
node
with
with
a
a
label
label
of
of
user,
user
and
and
that's
that's
all
all
it
it
takes
takes
to
to
create
create
relational
relational
data.
data
No
no
foreign
foreign
keys
keys
or
or
join
join
tables
tables
required.
required
Although,
although
we
we
can
can
add
add
constraints
constraints
to
to
the
the
schema
schema
such
such
as
as
the
the
one
one
here
here
that
that
will
will
ensure
ensure
every
every
username
username
aMimismunique.
amimismunique
In
in
addition,
addition
we
we
can
can
define
define
local
local
variables
variables
in
in
this
this
query,
query
then
then
return
return
them
them
from
from
the
the
statement
statement
to
to
get
get
the
the
data
data
back
back
as
as
the
the
result.
result
And
and
what's
what's
especially
especially
awesome
awesome
is
is
that
that
we
we
can
can
visualize
visualize
it
it
as
as
an
an
interactive
interactive
graph
graph
or
or
even
even
as
as
a
a
table.
table
Now,
now
to
to
continue
continue
building
building
Twitter,
twitter
we
we
can
can
connect
connect
multiple
multiple
tweet
tweet
nodes
nodes
to
to
a
a
user.
user
We
we
also
also
need
need
a
a
query
query
that
that
will
will
return
return
all
all
of
of
the
the
tweets
tweets
from
from
all
all
the
the
users
users
that
that
are
are
being
being
followed.
followed
This
this
can
can
be
be
easily
easily
represented
represented
with
with
nodes
nodes
and
and
relationships,
relationships
but
but
we
we
can
can
also
also
filter
filter
this
this
query
query
with
with
a
a
WHERE
where
clause
clause
to
to
return
return
tweets
tweets
that
that
were
were
posted
posted
recently.
recently
In
in
addition,
addition
we
we
can
can
match
match
on
on
string
string
patterns
patterns
and
and
regular
regular
expressions,
expressions
or
or
filter
filter
on
on
complex
complex
patterns
patterns
based
based
on
on
the
the
graph
graph
itself,
itself
like
like
only
only
return
return
tweets
tweets
from
from
users
users
that
that
have
have
not
not
been
been
muted.
muted
So
so
not
not
only
only
is
is
it
it
intuitive
intuitive
for
for
developers,
developers
but
but
it
it
also
also
allows
allows
patterns
patterns
in
in
your
your
data
data
to
to
emerge
emerge
naturally,
naturally
which
which
is
is
a
a
huge
huge
advantage
advantage
when
when
when
when
it
it
comes
comes
to
to
data
data
analysis
analysis
and
and
machine
machine
learning.
learning
This
this
has
has
been
been
Neo4j
neo4j
in
in
100
100
seconds.
seconds
Hit
hit
the
the
like
like
button
button
for
for
more
more
short
short
videos
videos
like
like
this.
this
Thanks
thanks
for
for
watching,
watching
and
and
I
i
will
will
see
see
you
you
in
in
the
the
next
next
one.
one
success
The speaker explains the basics of NeoGeneration, an ACID compliant graph database that can handle complex graph data. They also discuss the use of CyFinancial in creating new nodes and streams for queries, and the ability to filter queries with luck. The speaker emphasizes the benefits of Neo profitability for developers and explains how to visualize the graph as an interactive table.
Neo4j in 100 Seconds
Neo4j, an ACID compliant graph database that can handle the extremely complex relational data of the modern world. It was created in 2007 by normalized tables together is not how real humans think about relationships. Naturally, we think about connections between entities. Like, Bob asked a question on Stack Overflow, then Alice and Chad downvoted it, so Bob gave up on his dream of programming. A graph can model this data the same way you might visualize it on a white m. Nodes represent entities, edges represent relationships between entities, and properties are key value pairs that store data on these objects. MX. Under the hood, Neo4j is a true native graph database that applies this model all the way down to the storage level. Queries are written in a declarative language called Cypher that resembles MQL, except nodes are surrounded by parentheses, then connect to other nodes with arrows via relationships and brackets. Today, Neo 4J is used in applications mix like recommendation engines, social media platforms, and knowledge graphs for artificial intelligence, just to name a few. To get started, it can be self hosted Amit Docker, but the easiest approach is to sign up for Aura, which provides a free, fully managed database in the cloud. From there, we can build the next Twitter by making queries with Cypher. Use create followed by parenthesis to create a new entity or node. This node has a label of user that can be used to group similar nodes a mis2gether. Inside the braces, we can add key value pairs to represent the data on that node. Now a user might want to follow another user, which which can be represented with a relationship inside of brackets. Then notice how the arrow points to another node with a label of user, and that's all it takes to create relational data. No foreign keys or join tables required. Although, we can add constraints to the schema such as the one here that will ensure every username aMimismunique. In addition, we can define local variables in this query, then return them from the statement to get the data back as the result. And what's especially awesome is that we can visualize it as an interactive graph or even as a table. Now, to continue building Twitter, we can connect multiple tweet nodes to a user. We also need a query that will return all of the tweets from all the users that are being followed. This can be easily represented with nodes and relationships, but we can also filter this query with a WHERE clause to return tweets that were posted recently. In addition, we can match on string patterns and regular expressions, or filter on complex patterns based on the graph itself, like only return tweets from users that have not been muted. So not only is it intuitive for developers, but it also allows patterns in your data to emerge naturally, which is a huge advantage when when it comes to data analysis and machine learning. This has been Neo4j in 100 seconds. Hit the like button for more short videos like this. Thanks for watching, and I will see you in the next one.
The speaker explains the basics of NeoGeneration, an ACID compliant graph database that can handle complex graph data. They also discuss the use of CyFinancial in creating new nodes and streams for queries, and the ability to filter queries with luck. The speaker emphasizes the benefits of Neo profitability for developers and explains how to visualize the graph as an interactive table.