Speaker 1: Have you ever, you know, picked up your phone or maybe opened your laptop and it just, well, it just knew. It recommended that exact song you were thinking about or maybe showed you an ad for something really specific you just mentioned. Happens all the time, right? Or uh it navigates you through rush hour somehow predicting every single slowdown perfectly. It feels a bit like magic, doesn't it?
Speaker 2: It really does. But behind these like everyday miracles, there's something huge, something powerful, and it's always is growing.
Speaker 1: Mhm. Massive. Welcome to the deep dive. Today we're diving into a term that well, it gets thrown around a lot. A real buzz word.
Speaker 2: Definitely a buzz word.
Speaker 1: But what does it really mean for you? You, the person actually experiencing all these digital kind of spooky wonders. We're talking about big data, right? Let's get into it. So, our mission today is basically to unpack this this colossal concept. We're trying to define what big data actually is. Uh understand why it's become so incredibly important,
Speaker 2: crucial really,
Speaker 1: differentiate it from those, you know, traditional databases you might know about. Explore some of the challenges because it's not all smooth sailing.
Speaker 2: Oh, definitely not.
Speaker 1: And then and we'll take a peek into its future. We want to give you the insights to feel properly informed. We've uh waited through a stack of really interesting material on this laying out the whole landscape and yeah, we're ready to dig in.
Speaker 2: Yeah, sound good. So, okay, to start us off, when we say big data, what are we actually talking talking about like fundamentally?
Speaker 1: Yeah, that's the perfect place to start because you know it's way more than just like a really big Excel sheet, right? At its core, big data isn't just lots of information. It refers to collections of data that are so incredibly huge. Um so vast that the old ways, the traditional methods, they just can't handle it.
Speaker 2: Ah okay, we're talking about data sets so massive they and this is a good quote can't be handled by traditional means let alone used in any reasonable way. That distinction is really key.
Speaker 1: Gotcha. So, it's not just a lot. It's literally too much for the old tools. That makes sense. And it's not just one kind of information either, is it? What sort of data are we actually dealing with here?
Speaker 2: Exactly. It covers a really really wide spectrum. This data can be uh structured like neat tables or unstructured like raw text, images, videos, you name it.
Speaker 1: Or even semistructured somewhere in between. Okay.
Speaker 2: And what's fascinating I think is it's not just the size, but what that size means for how we actually interact with information. And it's not static data either. It's usually not only extremely large, but also growing very quickly,
Speaker 1: constantly growing,
Speaker 2: constantly. And its purpose, well, it's the fuel behind really powerful stuff like predictive modeling, AI, machine learning, and other popular topics that are, you know, genuinely shaping our world right now.
Speaker 1: Okay, that immediately grabs my attention because, yeah, AI, machine learning, it feels futuristic, but it's clearly happening right now. So, why is something this this huge and frankly unwieldy. Why is it so important?
Speaker 2: Well, it really boils down to the power information gives you to make better decisions. Simple as that almost, right? With big data, you genuinely can make better choices because you have more data to work with. Basically, better patterns can be found with more data points.
Speaker 1: So, more data, clearer picture.
Speaker 2: Exactly. A bigger, richer data set lets you spot trends and connections you just completely miss with smaller amounts of info. It's like seeing the whole forest, not just, you know, a couple of trees.
Speaker 1: And speed is a big part of it, too, isn't it? It's not just about finding insights eventually, but finding them like now real time.
Speaker 2: Precisely. That real- time data collection and analytics aspect is huge. It means businesses and even us as individuals through apps can move and make choices faster.
Speaker 1: Like the traffic app,
Speaker 2: perfect example, traffic apps updating constantly or banks catching fraud as it happens, not days later.
Speaker 1: Okay.
Speaker 2: And beyond just speed, more data and Those real-time insights, they lead to way more efficiency. Businesses can automate more of your business to reduce costs because they understand the patterns better.
Speaker 1: Makes sense.
Speaker 2: And maybe most importantly for you, the listener, it means companies get the closer you can tailor your business to your customers, better personalization.
Speaker 1: Okay, here's where it gets really interesting for me and probably for everyone listening. Think about how that tailoring impacts your life every day, right? Those personalized ads, better streaming recommendations, even those traffic predictions we mentioned, it's all driven by this. But, uh, to really get our heads around how it manages all this, is there like a simple framework, some core ideas that define what big data actually is?
Speaker 2: Absolutely. Yeah. There's a really well-known framework often called the three Vs of big data. Yeah. It was actually coined by Gartner, the big tech analysis firm, way back in 2001.
Speaker 1: Oh, wow. That long ago?
Speaker 2: Yeah. It's been around a while conceptually, and it's still a great way to get the main characteristics. So, the first V is volume. volume. Okay, makes sense. Size.
Speaker 1: Exactly. It just refers to the high volume of data. Simply much data. We're talking scales that are hard to even imagine. Petabytes, exabytes, zetabytes.
Speaker 2: Exetabytes. What even is a zetabyte?
Speaker 1: It's uh a trillion gigabytes. Just staggering amounts of information.
Speaker 2: Whoa. Okay. Not just gigabytes then. So volume. What's the second V?
Speaker 1: That's velocity. This is all about the speed of data being generated. Often it tends to be real time or just incredibly so fast. What? Well, think about social media feeds constantly updating or sensor data pouring in from say internet of things devices. It's just this relentless flow like a digital river that never ever stops.
Speaker 2: Okay. A non-stop river of data. Got it. Volume, velocity, and the third V must be variety. But it covering all those different types of data we talked about earlier. Structured, unstructured.
Speaker 1: You got it. Variety. It covers the many sources of data or just such variety. That includes everything from those neat database records to totally unstructured emails, video social media posts, audio files, you name it, right? And if you tie these three together, volume, velocity, variety, they fundamentally change how we even have to think about managing data. They push us way beyond the old ways because frankly, it's just too much coming too fast. And it's too diverse for those old systems to cope with.
Speaker 2: That really clarifies it. Too big, too fast, too messy for the old ways. So with volume, velocity, and variety sorted, let's maybe make it more concrete. What are some real world examples, things you listening might interact with daily, maybe without even realizing big data is doing the heavy lifting behind the scenes?
Speaker 1: Oh, absolutely. One of the most obvious examples for most people is probably advertisements for products and marketing campaigns.
Speaker 2: Ah, yes. The spooky targeted ads.
Speaker 1: Exactly. If you've ever looked at, say, a specific pair of shoes online and then suddenly see ads for those exact shoes everywhere you go online.
Speaker 2: Yep. All the time.
Speaker 1: That's big data working behind the scenes analyzing your browsing. your clicks, your inferred preferences.
Speaker 2: It's kind of amazing and a little creepy sometimes how specific they get. It really can feel like they're reading your mind.
Speaker 1: It can feel that way. Another one, something you mentioned relying on is navigation.
Speaker 2: Oh, yeah. My commute saver, right? Route software such as Google Maps or Ways using GPS data, plus real-time traffic reports from other users, all crunched together to plan your routes dynamically. That is big data in action, constantly adjusting as conditions change. Saved me from countless scams too.
Speaker 1: Couldn't live without it some days.
Speaker 2: Totally. Then there's a really critical application often invisible to us which is fraud detection for banks and credit cards.
Speaker 1: Ah important stuff.
Speaker 2: Hugely important. Their sheer volume and crucially the velocity of transactions lets these systems spot weird patterns anomalies in real time. That's how they can block a fraudulent transaction almost instantly.
Speaker 1: Like if my card suddenly gets used halfway across the world.
Speaker 2: Precisely. Your bank system sees that huge geographic jump. flags it based on patterns learned from massive data sets and boom, transaction blocked, maybe you get a text alert. It's protecting you.
Speaker 1: Okay. And it goes beyond just, you know, shopping and driving too, right? There are bigger picture uses.
Speaker 2: Oh, absolutely. We're talking about major societal and environmental impacts. Think about predicting weather patterns, natural disasters, climate change, and early warning systems. Analyzing vast amounts of atmospheric data, seismic data, historical patterns. That's big data being used for global good potentially saving lives.
Speaker 1: That's incredible. Okay, so with all this data, zetabytes of it, flying around at insane speeds in all these different formats, it's crystal clear this isn't your standard database setup.
Speaker 2: Not even close.
Speaker 1: If the traditional systems just choke on this stuff, what are the tools? What are the concepts that have been built for this this big data era?
Speaker 2: Yeah, that's the crucial next question. Why the need for totally different tools and languages? Well, it comes down to a few things. First, obviously the ability to handle large volumes of data. Second, the demand for realtime visualizations that can also be interactive. People want to see and play with the data, not just get static report,
Speaker 1: right? Make it usable.
Speaker 2: Exactly. And third, just the sheer amount of data storage needed that brings its own challenges like massive backup systems, specialized security personnel is a whole new infrastructure paradigm.
Speaker 1: Okay, so it demands a whole new toolkit. What are some of the big names in that toolkit, the kind of technologies powering all this?
Speaker 2: Well, one you hear about a lot. A really popular open source solution is Apache Hadoop.
Speaker 1: Hadoop. Heard of it,
Speaker 2: right? It's basically designed to store and process huge data sets. And the key is it uses distributed computing. Instead of one massive computer, it spreads the data and the work across potentially thousands of cheaper standard machines. That's how it scales up.
Speaker 1: Okay, so distribute the load. Makes sense. What else?
Speaker 2: Then there's Apache Spark. Also very popular, open- source. It's often called a unified analytics engine. It also uses cluster computing. similar to Hadoop, but it's often faster, especially for certain types of analysis because it does more in memory. Plus, it has built-in libraries for things like machine learning. Yeah. ML, SQL queries, and APIs for common languages like Python, R, Java. Makes it super versatile.
Speaker 1: This is super versatile. So, Spark is like Hadoop's faster, more versatile cousin for analysis?
Speaker 2: kind of. Yeah, they often work together actually. Hadoop might handle the storage, Spark handles the processing, then for actually seeing the data visual izing it,
Speaker 1: right? The dashboards and charts.
Speaker 2: You might use something like Splunk. It's a powerful data analytics tool built for big data. It can ingest all sorts of data, let you search it, create dashboards, visualizations, and it even incorporates AI now to help find insights automatically.
Speaker 1: Okay. Splunk for digging in and visualizing. Any others?
Speaker 2: And definitely Tableau, if you've seen those really slick interactive dashboards in like business presentations.
Speaker 1: Yeah, the ones you can click on and filter. High chance it was made with Tableau. It's a major data visualization tool, super popular in companies. Its big selling point is the drag and drop properties, which means people who aren't necessarily coders can build quite sophisticated charts and dashboards relatively easily.
Speaker 2: Got it. Hadoop and Spark for the backend processing. Splunk and Tableau more for the front-end analysis and visualization. That helps paint the picture. But let's really nail down this difference. Big data storage versus traditional databases. because you said it's not just a bigger version,
Speaker 1: right? It's fundamentally different in many ways. You're hitting on a really critical point. Traditional relational databases, the kind that use structure tables like SQL, they're fantastic for organized consistent data, but they really, really struggle with the sheer volume and variety of big data. They just weren't designed for it.
Speaker 2: Exactly. This is where NoSQL databases come into play. The name is a bit misleading. It often means not only SQL, they are specifically designed to handle massive amounts of of data, often unstructured or semistructured data.
Speaker 1: How do they do that differently?
Speaker 2: Well, unlike relational databases that need a strict predefined schema, like fixed columns in a table,
Speaker 1: Yeah.
Speaker 2: No SQL databases are much more flexible. They can handle data that doesn't fit neatly into rows and columns. More importantly, they're designed to scale horizontally.
Speaker 1: Horizontally,
Speaker 2: meaning meaning you can just add more machines to your cluster to handle more data or more traffic. Relational databases typically scale vertically. You have to buy a bigger, more power full single machine which gets incredibly expensive and has limits. NoSQL's horizontal scaling is way better suited for big data's unpredictable growth.
Speaker 1: Ah okay. So NoSQL trades some of that rigid structure for massive flexibility and scalability. That makes total sense for the chaos of big data. And within big data storage there are different concepts too, right? I've heard terms like data lake and data warehouse. What's the difference there?
Speaker 2: Yeah, absolutely. That distinction is really important for understanding how companies manage this data flow. Think of a data lake first. It's like this huge repository, vast pool where you store all your data, raw data in its native unprocessed format.
Speaker 1: So everything just gets dumped in?
Speaker 2: pretty much. It could be web logs, social media feeds, sensor data collected real time from IoT, Internet of Things devices, structure data, images, everything. It's stored raw without necessarily having a specific purpose in mind yet. Like a literal lake, you just pour all the streams in. The idea is you store it now. now figure out how to use it later.
Speaker 1: Okay. A giant digital holding tank for raw stuff. So, what's a data warehouse then? Sounds more organized.
Speaker 2: Exactly. A data warehouse is different. It typically stores processed data. Data that's been cleaned, transformed, and structured specifically for analysis and reporting. It will need a schema design so the data can be easily worked with. Think of it less like a lake and more like a highly organized library or well, a warehouse where everything is neatly shelved and labeled ready for business intelligence queries.
Speaker 1: Got it. lake raw messy potential warehouse clean structured ready for reports and I think I saw data mart mentioned too is that related?
Speaker 2: yeah a data mart is essentially a subset of a data warehouse. you can think of it as a data warehouse for a specific purpose. so maybe the marketing department has its own data mart with just the customer and campaign data they need drawn from the main warehouse. it's like a specialized wing of the library focused on one subject.
Speaker 1: lake warehouse smart raw dump organized library specialized section that makes the flow much clearer. And just to reiterate, trying to use a traditional relational database for that initial data lake stage or even a massive warehouse, that's just asking for trouble.
Speaker 2: Oh, absolutely. The whole concept of database scalability, how gracefully your system handles growth, say from a 100 customers to 100,000 or a million is key. And the reality is traditional relational databases will basically fall over if you try and use them for big data at that scale.
Speaker 1: They just crumble under the pressure.
Speaker 2: They really do. They weren't arch detected for that kind of distributed low, that velocity, that variety. That's why technologies like NoSQL emerged. They represent a different set of trade-offs, prioritizing scalability and flexibility over the strict consistency sometimes found in relational systems.
Speaker 1: Okay, this is painting a picture of incredibly powerful systems. But it can't all be easy, right? If these tools are so capable, what are the catches? What are the potential downsides? Or, you know, the really hard parts about working with big data?
Speaker 2: That's such an important question. to ask because yeah, it's definitely not a magic bullet. There are some major challenges. One of the absolute biggest is the lack of talent skills.
Speaker 1: Finding the people who know how to use this stuff.
Speaker 2: Exactly. It is incredibly hard to find people that can work with big data tools. Well, folks with deep expertise in Hadoop, Spark, NoSQL, data engineering, data science. They are very expensive to hire and hard to find. There's a huge skills gap between the demand and the supply right now.
Speaker 1: Yeah, I bet you hear data scientists everywhere. But I guess actually being one who can handle this scale is rare. Beyond people, what about the tech itself? The infrastructure must be a monster, right?
Speaker 2: Oh, absolutely. If your underlying systems aren't ready, infrastructure weaknesses and tech debt will hit fast. Big data, remember, comes in fast. It needs fast processing. Not all systems and networks can handle it. You need really robust, scalable, often expensive infrastructure. Neglect that and things grind to a halt very quickly.
Speaker 1: So, you need the experts. and the high-end gear. What else?
Speaker 2: Then there's the data itself. Just because you can collect zetabytes doesn't mean it's all useful, right? Data quality is a massive issue.
Speaker 1: Ah, good point. Big doesn't automatically mean good.
Speaker 2: Precisely. Not all data is good data. Companies can collect data we shouldn't, maybe due to privacy concerns. They can collect data that just isn't helpful for their goals. And even good data can be hard to organize and clean, especially when it's coming from so many varied sources. Garbage in, garbage out still applies, even at massive scale.
Speaker 1: So you can drown in useless or bad data.
Speaker 2: You absolutely can. And finally, maybe the most critical challenge, especially these days, security and compliance,
Speaker 1: right? Protecting all that data.
Speaker 2: It's a huge responsibility. Think about it. The more data you have, the bigger the target for hackers and the more valuable you are to bad actors. A breach involving a massive big data repository can be catastrophic financially and reputationally. Plus, you have regulations like GDPR, CCPA, Compliance is complex and non-negotiable.
Speaker 1: Wow. So, yeah, it's definitely not just a gold mine. It's a potential minefield, too. Talent gaps, infrastructure costs, data quality nightmares, huge security risks. That's a really important balance to understand. But despite all these hurdles, big data isn't going away.
Speaker 2: Not a chance. It's only getting bigger.
Speaker 1: So, what's next? Where is this constantly evolving landscape heading in the say near future?
Speaker 2: Well, the trend suggests things are getting even faster and smarter. One major shift is towards more streaming data in real time. Instead of cloaking data in batches, say every hour or every day, increasingly the goal is to process data as a stream instead of in batches.
Speaker 1: So analyzing it literally as it flows in.
Speaker 2: Exactly. This delivers more real-time information and analytics, allowing for truly immediate insights and potentially immediate actions. It's a natural evolution of that velocity we talked about.
Speaker 1: Okay. Even faster insights. And given all the buzz, I'm guessing AI and machine learning are going to get even more entwined with big data.
Speaker 2: Oh, absolutely. We're looking at artificial intelligence, AI, and machine learning, ML for more automated decision and responses. Imagine systems that don't just present insights to a human, but actually use those insights to make decisions or trigger actions automatically based on the incoming data streams. That level of automation is a key future trend.
Speaker 1: Okay. Smarter, more automated systems. Now, this next point sounds really significant, especially for everyone listening. Democratization of data. What does that mean in practice?
Speaker 2: It's a really powerful concept. It means people having more access to their own data. Things like the ability to remove their data, download their data, and see what is known about them. Think about GDPR rights, for example. It's about shifting some of the power and transparency back to the individual, giving you more control over your digital self.
Speaker 1: That sounds like a positive shift. More power to the user.
Speaker 2: It has the potential to be definitely. And alongside that, to make actually working with big data easier for more people, we're seeing more no code and low code solutions emerge.
Speaker 1: Meaning?
Speaker 2: meaning tools that allow people without deep programming skills to still build applications, run analyses or create visualizations on top of big data platforms. Think drag and drop interfaces, visual builders, it lowers the barrier to entry.
Speaker 1: So making these powerful tools accessible to more than just the elite data scientists.
Speaker 2: Exactly. It really highlights this overall shift, I think, moving from just, you know, hoarding massive amounts of data towards making it genuinely useful across the board. making it more transparent and hopefully giving you the individual more agency in how it's used.
Speaker 1: What an absolutely fascinating journey we've taken today really digging into big data. We started off defining it not just lots of data but these extraordinarily large collections that the old ways just can't handle.
Speaker 2: Mhm. Too big, too fast, too varied.
Speaker 1: Exactly. We saw why it's so important driving better decisions, enabling that real-time speed and agility and leading to those personalized experiences you see every day. We got to grips with Gartner's three Vss, volume, velocity, and variety.
Speaker 2: The core characteristics.
Speaker 1: But then we saw it in action all around us. Your ads, your maps, fraud detection, even predicting weather patterns. We looked at the new generation of tools needed like Hadoop for storage, Spark for processing, Splunk, and Tableau for making sense of it all,
Speaker 2: the whole new ecosystem.
Speaker 1: And crucially, we clarified that difference between dumping raw stuff into a data lake versus organizing process info in a data warehouse or a specialized data mart. We also made sure to face the challenges head on,
Speaker 2: right? The talent gap, the infrastructure headaches, data quality issues, and those huge security and compliance worries.
Speaker 1: Definitely not trivial hurdles. And finally, we looked ahead towards real-time streaming, much more automation via AI and ML, and perhaps most importantly for everyone listening, that trend towards democratization, giving you more access and control over your own data, coupled with tools that make it easier for more people to use.
Speaker 2: The constantly evolving picture. really is. Hopefully, this deep dive has equipped you listening to look at data a bit differently now to maybe understand some of the unseen forces shaping everything from your phone recommendations to maybe even your smart home devices.
Speaker 1: It's everywhere.
Speaker 2: It truly is. And that leaves us with a final thought to ponder as we do move towards this future where data is supposedly becoming more democratized. What new responsibilities but also what new opportunities will arise for you as an individual? How will you manage your own digital footprint and what What kind of transparency should you demand from the systems using your data? Something to think about until our next deep dive.