Big Data vs Fast Data
Key Points
- Understanding the difference between big data (large‑scale, stored for deep, historical insights) and fast data (low‑latency, real‑time streams) is essential before designing an AI or automation strategy.
- Big‑data architectures prioritize massive storage and batch processing—typically using data warehouses—to support model training, historic pattern analysis, and compliance‑driven governance.
- Fast‑data systems are built for real‑time responsiveness, focusing on rapid ingestion and immediate value extraction rather than sheer volume.
- The two approaches represent a trade‑off: optimizing for big data can limit the flexibility needed for fast data, and vice versa, so teams must deliberately choose—or carefully combine—architectures that match their primary business need.
- Selecting the appropriate technology stack for the chosen data paradigm directly influences scalability, value generation, and overall success of AI initiatives.
Sections
- Choosing Between Big and Fast Data - The speaker explains how distinguishing big data from fast data guides AI and automation architecture choices, emphasizing the trade‑off between large‑scale insight generation and real‑time responsiveness.
- Big Data vs Fast Data - The speaker contrasts big data’s depth‑focused technologies like Spark, AI platforms, and visualization for predictive modeling with fast data’s speed‑oriented applications such as real‑time fraud detection, personalization, and IoT automation.
- Transient Edge Storage for Real-Time Decisions - The passage describes using short‑lived edge or cache storage to temporarily aggregate recent event data for immediate decision‑making, then outlines a crawl‑walk‑run maturity model for progressing with big‑data architectures.
- AI‑Driven Dynamic Data Governance - The speaker outlines how AI‑powered, dynamically scaling storage with automated governance can unify fragmented data warehouses into a fast‑data fabric, reducing maintenance and enabling quicker insight generation across maturity stages.
- Combining AI, Automation, and Fast Data - The speaker explains how organizations can layer AI and automation onto fast‑data streams to enable real‑time alerts, personalization, label refactoring, and dynamic pricing, while emphasizing that AI model creation and big‑data infrastructure are separate but complementary investments.
Full Transcript
# Big Data vs Fast Data **Source:** [https://www.youtube.com/watch?v=vWVOMV_vxxs](https://www.youtube.com/watch?v=vWVOMV_vxxs) **Duration:** 00:15:13 ## Summary - Understanding the difference between big data (large‑scale, stored for deep, historical insights) and fast data (low‑latency, real‑time streams) is essential before designing an AI or automation strategy. - Big‑data architectures prioritize massive storage and batch processing—typically using data warehouses—to support model training, historic pattern analysis, and compliance‑driven governance. - Fast‑data systems are built for real‑time responsiveness, focusing on rapid ingestion and immediate value extraction rather than sheer volume. - The two approaches represent a trade‑off: optimizing for big data can limit the flexibility needed for fast data, and vice versa, so teams must deliberately choose—or carefully combine—architectures that match their primary business need. - Selecting the appropriate technology stack for the chosen data paradigm directly influences scalability, value generation, and overall success of AI initiatives. ## Sections - [00:00:00](https://www.youtube.com/watch?v=vWVOMV_vxxs&t=0s) **Choosing Between Big and Fast Data** - The speaker explains how distinguishing big data from fast data guides AI and automation architecture choices, emphasizing the trade‑off between large‑scale insight generation and real‑time responsiveness. - [00:03:05](https://www.youtube.com/watch?v=vWVOMV_vxxs&t=185s) **Big Data vs Fast Data** - The speaker contrasts big data’s depth‑focused technologies like Spark, AI platforms, and visualization for predictive modeling with fast data’s speed‑oriented applications such as real‑time fraud detection, personalization, and IoT automation. - [00:06:21](https://www.youtube.com/watch?v=vWVOMV_vxxs&t=381s) **Transient Edge Storage for Real-Time Decisions** - The passage describes using short‑lived edge or cache storage to temporarily aggregate recent event data for immediate decision‑making, then outlines a crawl‑walk‑run maturity model for progressing with big‑data architectures. - [00:09:29](https://www.youtube.com/watch?v=vWVOMV_vxxs&t=569s) **AI‑Driven Dynamic Data Governance** - The speaker outlines how AI‑powered, dynamically scaling storage with automated governance can unify fragmented data warehouses into a fast‑data fabric, reducing maintenance and enabling quicker insight generation across maturity stages. - [00:12:43](https://www.youtube.com/watch?v=vWVOMV_vxxs&t=763s) **Combining AI, Automation, and Fast Data** - The speaker explains how organizations can layer AI and automation onto fast‑data streams to enable real‑time alerts, personalization, label refactoring, and dynamic pricing, while emphasizing that AI model creation and big‑data infrastructure are separate but complementary investments. ## Full Transcript
Data is the foundation of AI and automation,
but not all data is the same.
And if you don't understand the difference between big data and fast data,
you might be building your AI strategy on the wrong foundation.
We've talked about big data a lot,
and there are plenty of systems that are optimized for big data.
However, they might not be best suited for fast data.
This is a real problem for technologists today.
For one, we're working with different kinds of data.
We need to make sure we're putting it on the right architecture.
So do you think you optimize for scale
and deep insights or more so for real time responsiveness?
Today we're going to break down how to make the right choices here.
And let's go through exactly what these two categories mean.
Why they actually represent a trade off, and how understanding
where you fit will directly impact your ability to scale.
So while we're going to go through two definitions here,
I want you to really think about how this not only are two different categories,
but essentially represent a trade off because as you start to optimize
for big data, you lose the flexibility to gain value out of fast data.
So you need to make sure that you're always comparing these two
to really figure out which category your work really falls into.
And it's all about where we get value from data.
Are we getting value from fast data, or from the fact
that there is a lot of data to gain insights from?
So there isn't really a silver bullet technology.
These are two completely different kinds of architectures, and technology suites.
So really have to make sure that you pick a side basically,
and you're going to optimize for that.
Maybe you'll use them in combination.
But let's start by going through the definition of what these really mean.
So first things first we'll talk about big data.
You probably work with big data all the time.
We've probably been talking about it for about over a decade now.
And this is basically where we're trying to analyze
massive amounts of data sets to extract insights over time.
If your goal is to train AI models, analyze historic patterns,
or manage massive
data archives, you're going to be dealing with some kind of big data,
and you're going to see a bigger focus on big data
when you have really, important compliance and governance requirements.
So when you're building a big data architecture,
you're going to see some common themes.
And really the biggest and most important thing about
big data is the data storage and management component.
So that really relies,
on your data warehouse, which is going to be some kind of
very large data repository where all this data is stored.
And that's where getting your value right.
The fact that you can put lots and lots of information
more than you ever could before in one place to build value from it.
And then what
really comes with this as another key technology would be something to help
process and manipulate the data in some way.
So you can then extract even more value out of your data.
So this could be some kind of automation
or processing technology like spark for example.
And that's essential when you're working with big data.
The other piece of this you're going to see would be, business insights.
And AI platforms.
So you're going to want to create dashboards from this data.
You're going to want to create different kinds of models.
You're going to want to get more insights from how we're actually using this data.
So in that you're going to see lots of technologies
around, data visualization, an AI platform.
So data scientists can actually work with the data.
So these are really the core technologies you're going to be seeing
when talking about big data and the kind of architecture
you're going to be driving towards, if you're working, on AI model training
or any kind of predictive analysis or even think about deep learning,
these are the kind of investments
that make sense because they're going to help you scale in depth.
Now let's talk about fast data.
And to no surprise, fast data is all about speed.
Whereas big data was all about depth with fast data.
It really is more about how we can make instant decision making.
And this could be for fraud detection, personalization,
or some kind of internet of Things automation, just as some examples.
This is not to say that fast data can't be large.
It really has to do with where does the value come from?
This is it.
The data valuable at that point in time,
or is it valuable in aggregation over a long period of time?
And then based on the answer to that question,
you can start to really see the differences here.
And so with big data, if you wanted to forecast your sales for next year
using past and historic sales as evidence, that would be a big data use case.
If you want to know what your sales were in the last five minutes,
that would be fast data and you might use that to make decisions going forward.
So both are incredibly important and powerful, but it really has to do
with the difference in where that data value lies.
Now you're going to see different kinds of investments as well.
With fast out, are
you really going to see more investments in data integration?
So you definitely for pretty much all data,
the cornerstone of this really is going to be some kind of,
streaming or something like Kafka.
That's going to take all these little data events
and is going to aggregate them
and send them off to another system
so that we can actually then,
bring them in and take some kind of action on them.
So this would lead to some kind of system
that would actually do some kind of event.
Right.
You want to take that data and then trigger something off of it.
So you have your stream here, which is an incredibly important piece of technology.
And then this is probably going to link to some kind of function as a service
or some kind of very low latency, lightweight
processing, structure where you can trigger and run this event.
This is basically going to allow us to make very quick decisions
that are really just siloed and isolated and completely independent from each other
and do not run as an aggregate system per se.
And then the last piece of this really is a little bit of storage.
Usually storage that's ephemeral.
Maybe lives on the edge or could be a cache so that you can actually take,
a couple of these events and a couple of these data points
that we all agree are very important and start bringing them all in,
to store them in the short term when they have value.
So if you want to know what happened in the last five minutes or what happened
last hour, you can kind of keep an inventory of this.
This isn't its final destination.
It's not the last place, that it's going to be stored
or its permanent data warehouse, but it's needed to facilitate that value.
When you need data in real time.
So these are really our core key differences here and architectures.
But let's go ahead and take a look now at what kind of maturity models
we really have around both of these kinds of data.
So now that we understand what big data is and the kind of architecture
it generally has, let's get an understanding of different levels
of maturity when working with big data.
So let's break this down into crawl walk, run or kind of like beginner intermediate
advanced models of what it really looks like to work with big data.
Most of us have started in
this stage where we have many different data silos,
in our organization and within those silos.
You know, we have our data repositories, which is great.
Maybe we're doing a bit of AI off of some, maybe we're building some dashboards
off of others, and, you know, we're generating business value.
We're finding new things from our data.
So there's really nothing wrong with this kind of architecture.
This is naturally where most people start out,
when working with some kind of big data architecture.
And then quickly, you'll start to realize that you find
more optimization by generally bringing everything together.
So moving all these sources to one larger data repository
where they can reside, either as like a data fabric or a data mesh,
or literally storing them all in the same kind of location.
And this is where you'll start to see the introduction of some kind of,
processing technology.
So you're starting to work with big data technologies.
You're starting to really
see the different kind of connections that can be formed.
You're finding those basically economies of scale of bringing new things together.
It's again, in the natural progression from, that stage.
Now, taking this even further,
because I think many people have data warehouses, you have data fabrics.
This is well established now.
It's really about adding actually AI and automation to this kind of architecture.
So in your data repository we would really expect
to have some kind of auto scaling, at the storage level.
So we could actually work with different levels of storage
that change dynamically based on business need.
As well as we would also want this just totally encompassed
in some kind of smart or, auto governance kind of structure.
So that you have this kind of locked down
and this can be driven by AI actually.
So you can actually be enhancing
your data architecture in general with AI.
And that's really what's
going to take you to a more advanced place where you can load more data.
And a lot of the,
basically the maintenance that comes with this can be automated
and you can move faster and focus on the actual business insights
and AI models that can be generated out of this data,
as opposed to focusing too much on maintenance and organizational silos.
So, for example, an organization might start from siloed data warehouses
to a unified data system or fabric, and then further enhance their big data
architecture with AI and automation to make it as optimized as possible.
All right.
Now let's talk about the different maturity levels with fast data. So
this is going to be again very different than big data.
But really we want to walk through this different levels of maturity here.
And we want to see what this really looks like when working with fast data.
And this is again going to be much more data integration focused.
So generally what people have to start with when working with fast data
is some kind of log analysis or real time alert or notification system.
So you have your event and that basically is going to trigger
some kind of alert.
And that's going to notify
that's going to share to people, let them know that that event happened
and then hopefully they can make better decisions moving forward.
Now how can we take that further?
Basically by adding AI.
So what I can do is it just doesn't take an alert
that sends to a human that says, do something about this.
It can actually then trigger an event and categorize,
or summarize or do some kind of advancement with that.
To actually tell someone, you know, this is fraud.
We're labeling this as fraud.
We're labeling this as high risk.
You're creating some kind of, you know, flag on it, maybe,
so that you can maybe,
again, make better decisions, that it's more than just a notification.
You're now actually sharing more information on it.
And then next would really be adding more autonomy to this.
So that not only is your fast data able to send a notification,
you're able to identify it, categorize it, and actually do something about it.
So this would be some kind of automation, some kind of action that can be taken.
So across all these different maturity models,
I think this path actually builds much more naturally on each other
and requires a lot less, technical advancement,
between each stage as compose as opposed to big data.
And what we can really do with this here is
we can take advantage of the different kind of AI and automation capabilities.
So think of this, not just as, you know, as an example, I would say,
so that someone might first just use this to share alerts on sales.
Then they can actually take fast data to work further and in real time,
do some kind of personalization, for the customer.
So, you know, label or refactor something and then to take it even to the next step
would be to dynamically change the price in real time
based on some other information from fast data and other kind of,
information that's coming into the system.
So that's how an organization could really take this from one stage to another.
Now, you've probably heard me talk about AI
when referring to these kinds of, models.
And you are correct in your thinking, don't you need big data to build those
AI models? You are correct.
But what's important to really notice here
is that you're not going to build your system,
or host your system here.
Really, for fast data on a big data system.
There are two different things.
So you might build an AI model that would help you classify
different types of events.
And it's applied to fast data.
So sometimes you need both.
But the important takeaway here is that they are two different things
that can be done in combination, but really are totally different
in terms of the types of technology and investment that you need in it.
Ultimately, here's why this matters.
AI and automation don't work without the right data foundation.
If you're investing in big data, you're setting yourself up
for deep insights and long term AI growth.
If you're investing in fast data, you're optimizing
for real time AI and optimizing.
Hopefully you should be walking away from this video,
understanding the key differences between fast data and big data,
and try to understand where your work
and how your data finds value ultimately falls on this scale.
She should be able to say where
where you really fall into between these two categories.
Are you optimizing for depth or for speed?
Either way, getting this right is essential
because the future of AI driven business insights
depends on whether data strategy aligns with AI goals.