It’s important for businesses to handle information well, especially when using Artificial Intelligence (AI). To do this well, we need to understand the difference between “Big Data” and “Fast Data.”
They are both important, but they work in different ways. Knowing the difference helps us make better choices for your AI goals.
What is Big Data?#
Big Data is about looking at large amounts of information over time. It helps train AI models, find old patterns, and keep large records of information.
Think of it like studying a huge library of facts to learn deep lessons. It’s about how much data you have and how deep you can go with it.
To handle Big Data, it is common that we will need tools like:
Data Warehouses: These are like huge storage rooms for all your information. They help you find specific facts quickly from a lot of saved data.
Processing Tools (like Spark): These tools help sort and clean the large amounts of data. This makes it easier to find useful information.
BI and AI Platforms: These are like dashboards that help you see your data clearly. They also help you build smart models that can guess what might happen next.
How Big Data Grows?#
Businesses usually go through three steps as they get better at using Big Data:
Starting Out (Crawl): At first, information is often spread out in different places. Each part of a company might have its own data. This can make it hard to get a full picture.
Getting Better (Walk): Next, companies start bringing all their data together. They use new ways to connect different data sources. This helps create a more organized data system.
Expert Level (Run): At the highest level, AI helps manage the data itself. Data storage can grow or shrink as needed, and AI helps keep everything in order. This lets the company focus more on what the data means for their business.
What is Fast Data?#
Fast Data is about making quick decisions. It’s about using information right away when it happens.
This is useful for things like finding fraud, giving people custom online experiences, or controlling smart devices instantly. The value of Fast Data is in how quickly we can use it.
Fast Data needs tools that work very quickly. For example:
Streaming Tools (like Kafka): These are like fast rivers that carry data as soon as it’s created. They make sure information moves quickly to where it’s needed.
Quick Processing (like Function as a Service): These tools act on single pieces of data very fast. They can trigger actions right away based on new information.
Temporary Storage: Fast Data systems often use short-term storage. This helps them quickly use new data without having to save it for a long time.
How Fast Data Grows?#
Fast Data also has three levels of development:
Starting Out (Crawl): Early on, Fast Data might just create alerts when something happens. People then have to decide what to do.
Getting Better (Walk): In the next step, AI helps understand events better. It can label things like “fraud” or “high risk.” This gives people more information than just a simple alert.
Expert Level (Run): At the most advanced level, Fast Data systems can act on their own. They can not only identify things but also make changes or start actions automatically and instantly.
Understanding the critical differences between Big Data and Fast Data is not just technical jargon, but the strategic key to unlocking your AI’s full potential.
Working Together#
Big Data and Fast Data are different, but they work well together.
AI models often need a lot of Big Data to learn. But once they learn, they can use Fast Data to make smart decisions in real-time.
Choosing the right approach depends on what your business needs.
Do you need deep historical insights (Big Data) or quick, real-time actions (Fast Data)?
By understanding these differences and using the right tools, we can make our AI plans stronger and get better business results.
For more information, you can watch the video at https://www.youtube.com/watch?v=vWVOMV_vxxs.