Speaker 1: Welcome to this deep dive. Today we're taking a look
Speaker 1: at well a pretty massive digital superpower, and more importantly,
Speaker 1: how to actually use it without you know, totally blowing
Speaker 1: up your.
Speaker 2: Own systems, right, which is surprisingly easy to do if
Speaker 2: you aren't careful.
Speaker 1: Oh absolutely so. Imagine you have this digital scout, a
Speaker 1: really tireless, lightning fast web scraper that you can just
Speaker 1: send out onto the chaotic landscape of the Internet to
Speaker 1: gather massive amounts of information for you.
Speaker 2: It sounds incredible, right, You just pointed at a website
Speaker 2: and say, go get me the data.
Speaker 1: Yeah, but that's where almost everyone makes their first like
Speaker 1: catastrophic mistake.
Speaker 2: Oh yeah, you think your main problem is just finding
Speaker 2: the data, but within about five minutes of launching that scout,
Speaker 2: your actual problem becomes drowning in an absolute ocean of
Speaker 2: garbage exactly.
Speaker 1: And that is the core of our mission today. We
Speaker 1: are mastering the mechanics of how to really control that
Speaker 1: digital scout.
Speaker 2: Because if data is the new oil, mean crude oil
Speaker 2: straight out of the ground, will just completely destroy a
Speaker 2: modern engine.
Speaker 1: Right If a multi million dollar e commerce algorithm scrapes
Speaker 1: like a messy string of web code instead of a
Speaker 1: clean numerical price.
Speaker 2: The whole system just crashes, it really does. So today
Speaker 2: we're going to look at how you build an automated
Speaker 2: refinery something to turn that raw, messy web code into polished,
Speaker 2: organized business intelligence.
Speaker 1: We'll be unpacking the underlying architecture of web crawling, and
Speaker 1: the beauty of this architecture is that it all relies
Speaker 1: on logical flow.
Speaker 2: Yeah, if you can understand the flow of the information,
Speaker 2: the actual mechanics of how you gather it become well
Speaker 2: incredibly intuitive.
Speaker 1: So our roadmap for this deep dive, we're going to
Speaker 1: break this down into three main phases. First, how to
Speaker 1: steer the crawler using allow and deny.
Speaker 2: Rules right so it doesn't get completely lost exactly.
Speaker 1: Then we'll explore this invisible assembly line of input and
Speaker 1: output processors that clean up the data.
Speaker 2: And finally we'll open up the toolkit and look at
Speaker 2: how to handle escalating data problems using specific functions.
Speaker 1: And for those of you listening, even though this is
Speaker 1: technical programming architecture, we're breaking it down step by step
Speaker 1: you'll be able to completely visualize the logic in your head.
Speaker 2: So let's start at the very beginning of that flow.
Speaker 2: The scout itself, which in the web scraping world is
Speaker 2: almost universally called a spider. A spider, right, Yeah, and
Speaker 2: a spider is essentially just an automated script, a bot really,
Speaker 2: that navigates from link to link across the web, fetching
Speaker 2: the contents of whatever pages it visits.
Speaker 1: But if you just release a spider onto a major
Speaker 1: retail website without giving it any instructions, I mean, it
Speaker 1: has no concept of what's useful then at all.
Speaker 2: It'll just click every single link it finds.
Speaker 1: Like, it'll download the about us page, the terms of service,
Speaker 1: the user forums.
Speaker 2: The shopping card, password reset pages, all of it, which
Speaker 2: burns massive amounts of your processing power and bandwidth to
Speaker 2: retrieve data that you're just going to throw right into
Speaker 2: the trash.
Speaker 1: Yeah, if your goal is to build like a competitive
Speaker 1: database of their product catalog, a download of their privacy
Speaker 1: policy is totally.
Speaker 2: Useless, exactly. This is where we have to establish our
Speaker 2: guard rails. Before we can even think about refining the data.
Speaker 2: We have to make sure our crawler is fetching the
Speaker 2: correct pages to begin.
Speaker 1: With, and we do that by setting up allow and
Speaker 1: deny rules.
Speaker 2: Right, you got it. The spider really needs to know
Speaker 2: what it's looking for.
Speaker 1: So how does the allow rule actually communicate with the spider.
Speaker 2: Well, the allow option basically acts as a strict filter
Speaker 2: for the URLs the spiders permitted to follow. It relies
Speaker 2: on regular expressions, which you'll usually hear abbreviated as redx.
Speaker 1: Right. And for anyone newer to programming, a regular expression
Speaker 1: isn't just looking for an exact word match. It's more
Speaker 1: like a sequence of characters that specifies a search pattern.
Speaker 2: Yeah, it's like a super powered find function that looks
Speaker 2: at the structural DNA of the web address.
Speaker 1: So for URL to be extracted, it has to match
Speaker 1: that redx pattern exactly.
Speaker 2: And if you leave the allow rule completely empty, the
Speaker 2: default behavior is that the spider matches all links. It
Speaker 2: just goes everywhere, It just runs wild. Yeah. But let's
Speaker 2: say we're targeting a specific online bookstore and we only
Speaker 2: want the actual book listings.
Speaker 1: Okay, so we'd set an allow rule to look for
Speaker 1: a specific pattern in the URL path, like we might
Speaker 1: configure it to only follow links that contain the string
Speaker 1: catalog slash category, slash books.
Speaker 2: Right. So, if the spider is crawling the main page
Speaker 2: and sees a link pointing to catalog slash category slash
Speaker 2: books slash science fiction. It analyzes that URL, sees the
Speaker 2: matching pattern and proceeds to crawl it.
Speaker 1: And if it sees a link to customer support slash contact,
Speaker 1: the pattern fails.
Speaker 2: Yep, the spider ignores the link completely. It never even
Speaker 2: loads the page, which is really the key to scaling
Speaker 2: your data gathering efficiently.
Speaker 1: I definitely follow the logic there, but you know, it
Speaker 1: brings up an interesting question. If the allow rule is
Speaker 1: already acting as the strict bouncer, restricting the spider to
Speaker 1: just the book categories, why do we even need a
Speaker 1: deny a rule.
Speaker 2: Oh, because website structures are notoriously chaotic. I mean, they
Speaker 2: are rarely built with perfect clean.
Speaker 1: Category ah right, things get messy, It's very messy.
Speaker 2: Categories overlap, legacy pages get left behind. Or sometimes you
Speaker 2: just want to cast a slightly wider net with your
Speaker 2: allow rule so you don't miss anything, but.
Speaker 1: You still have those highly specific areas you absolutely know
Speaker 1: will ruin your data set exactly. It kind of reminds
Speaker 1: me of sending an eager, maybe a slightly hyperactive intern
Speaker 1: into an enormous physical library to do research.
Speaker 2: I like that analogy.
Speaker 1: Yeah, the allow rule is you telling the intern, hey,
Speaker 1: only bring me books from the nonfiction wing. Do not
Speaker 1: go to fiction, do not go to the cafeteria.
Speaker 2: But that intern is still going to bring you everything
Speaker 2: in nonfiction, including things you really don't.
Speaker 1: Want, right, which is where the deny rule acts as
Speaker 1: that vital safety net. You tell them you are allowed
Speaker 1: in the nonfiction wing, but whatever you do, stay out
Speaker 1: of the true crime section. I do not want to
Speaker 1: see those books.
Speaker 2: Translating that back to our webscraping scenario, you might have
Speaker 2: an allow rule capturing all books, but you could configure
Speaker 2: deny rules to block specific subcategory paths, like say erotica
Speaker 2: fifty or crime fifty one.
Speaker 1: So even if those paths technically sit inside the broader
Speaker 1: book catalog that's allowed by the first rule, the spider
Speaker 1: sees those specific strings in the denialist and just stops instantly.
Speaker 2: It acts as an absolute override.
Speaker 1: Okay, so we have the guardrails up. The spider goes out,
Speaker 1: navigates using the allow patterns, avoids the traps with the
Speaker 1: denial patterns, and brings back the right pages.
Speaker 2: The initial gathering phase.
Speaker 1: Is complete, but the data it brings back is well,
Speaker 1: it's far from ready for analysis, isn't it.
Speaker 2: Oh, it's terrible. By default, the data the spider hands
Speaker 2: you is just raw, messy HTML markup directly from the
Speaker 2: source code.
Speaker 1: Right, It's not just handing you a clean, nice spreadsheet,
Speaker 1: Like if we're trying to scrape the title of a book.
Speaker 1: It doesn't just hand over the words the Great Gatsby.
Speaker 2: No, it brings back the invisible infrastructure that tells the
Speaker 2: browser how to actually display those words.
Speaker 1: So you get like an opening heading tag, maybe a
Speaker 1: class attribute defining the font size, then some invisible white
Speaker 1: space line breaks the actual text the Great Gatsby, and
Speaker 1: then a closing heading tag.
Speaker 2: It's a lot of junk, which means we need to
Speaker 2: build the refinery. We need a system to strip all
Speaker 2: that away and leave just the clean data.
Speaker 1: And this brings us to processors.
Speaker 2: Yes, processors handle the pre processing and post processing of
Speaker 2: all this raw scrape data.
Speaker 1: They basically take that messy HTML string, format it, clean it,
Speaker 1: and turn it into a pristine data point.
Speaker 2: And this entire architecture relies on an invisible assembly line
Speaker 2: made of two really distinct phases, input processors and output processors.
Speaker 1: So an input processor acts immediately on the extracted data,
Speaker 1: like the very second the spider receives it right.
Speaker 2: As soon as that raw piece of HTML is plucked
Speaker 2: from the page, the input processor grabs it and does
Speaker 2: the initial cleaning.
Speaker 1: Then we have the output processors. So after all the
Speaker 1: raw data fragments for a specific item are collected and
Speaker 1: push through those input processors, the output processor takes over exactly.
Speaker 2: It looks at the pre clean data, applies the final formatting,
Speaker 2: and locks it in as the permanent value for that item.
Speaker 1: I kind of have to challenge this two step architecture
Speaker 1: though it feels like we're adding unnecessary complexity. Well, why
Speaker 1: not just build a massive bucket, Let the spider dump
Speaker 1: all the raw, messy HTML into the bucket for an hour,
Speaker 1: and then we just run one massive cleaning script at
Speaker 1: the very end in a single batch.
Speaker 2: It really comes down to system memory and the sheer
Speaker 2: scale of web data. Think about the efficiency of a
Speaker 2: physical assembly line. Okay, if you're scraping one hundred thousand
Speaker 2: pages and you wait until the very end to clean everything,
Speaker 2: you are forcing your system to hold massive amounts of heavy,
Speaker 2: redundant HTML tags in its active memory.
Speaker 1: Oh, so it just consumes a tremendous amount of ram huge.
Speaker 2: Amounts all right. By filtering individual pieces the moment they arrive,
Speaker 2: which is the input processor's job, you're constantly stripping away
Speaker 2: the excess weight before it even enters your database.
Speaker 1: Oh I see, So the input processor is essentially knocking
Speaker 1: the heavy mud off the boots on the porch so
Speaker 1: you don't have to spend hours cleaning the carpets inside
Speaker 1: the house later.
Speaker 2: That is a perfect way to look at it. The
Speaker 2: other crucial factor is that web data frequently arrives completely
Speaker 2: shattered into lists, shattered like an HTML page might have
Speaker 2: the title of a book broken up into multiple different
Speaker 2: text nodes scattered across the code. The input processor cleans
Speaker 2: each of those tiny shards individually as they arrive.
Speaker 1: Okay, and then the output processor acts as the final
Speaker 1: packaging step. It takes those clean little shards, assembles them
Speaker 1: into one beautiful string, and just seals the box exactly.
Speaker 2: But to run an assembly line that's sophisticated, we obviously
Speaker 2: need specific tools to solve specific problems, right.
Speaker 1: And as we look at the data arriving, the framework
Speaker 1: provides built in processors to handle escalating levels of messiness.
Speaker 2: Let's actually start with a scenario where the data isn't
Speaker 2: messy at.
Speaker 1: All, okay, the ideal scenario.
Speaker 2: In that case, we use the most basic tool available,
Speaker 2: the identity processor. It's incredibly simple because it's only function
Speaker 2: is to do absolutely nothing.
Speaker 1: Wait, it returns the original values completely unchanged.
Speaker 2: Yep.
Speaker 1: If it does nothing, why not just leave it blank?
Speaker 1: Why even declare a processor at all?
Speaker 2: It maintains the structural integrity of your pipeline. Sometimes a
Speaker 2: field genuinely doesn't need cleaning.
Speaker 1: Like an internal product ID number that's already perfectly formatted exactly.
Speaker 2: But the framework still expects a processor to be declared
Speaker 2: for consistency. So calling the identity processor tells the framework, Hey,
Speaker 2: I acknowledge this data is here, and I am deliberately
Speaker 2: passing it straight through to the next phase without modifications.
Speaker 1: Got it the deliberate pass through? But let's escalate the problem.
Speaker 1: Because the web is chaotic. What happens if the spider
Speaker 1: accidentally scrapes empty data structures alongside our actual target.
Speaker 2: Then we use the take first processor. It analyzes the
Speaker 2: list of values it receives and simply returns the first
Speaker 2: non null or non empty value and discards everything else.
Speaker 1: So if the input comes in as a list that
Speaker 1: reads empty space, empty space at the Great Gatsby space,
Speaker 1: it just grabs the Great Gatsby and throws the rest
Speaker 1: into the incinerator.
Speaker 2: Right. And because web scraping constantly pulls an accidental empty data,
Speaker 2: take first is typically deployed as an output processor for
Speaker 2: single valued fields. When you only want one definitive answer,
Speaker 2: you just tell it to take first.
Speaker 1: Let's escalate again. We mentioned earlier that sometimes a single
Speaker 1: piece of data is shattered across different nodes in the HTML.
Speaker 2: Yes, the shattered chards.
Speaker 1: Right, so the title gets scraped as three separate fragments,
Speaker 1: the Great and Gatsby. If we use take first on that,
Speaker 1: we just get the which is obviously wrong, Which.
Speaker 2: Is when we turn to the join processor. Just as
Speaker 2: the name implies, it takes multiple fragmented values and stitches
Speaker 2: them back together into a single strength.
Speaker 1: And you define a separator, right, so you can tell
Speaker 1: it to insert a space or comma between each fragment
Speaker 1: as it joins them.
Speaker 2: Exactly, so we have basic pass throughs. We can grab
Speaker 2: the first clean item, and we can stytch fragments together.
Speaker 1: But what if the data requires a multi step decontamination process.
Speaker 2: Then we move into the advanced tools, starting with a
Speaker 2: composed processor. This allows you to chain mathematical or logical
Speaker 2: functions together, basically creating a mini pipeline within your main pipeline.
Speaker 1: How does that work in practice?
Speaker 2: Well, the composed processor takes entire chunk of input data
Speaker 2: and passes it wholly into the first function you define.
Speaker 2: That function does its job and outputs.
Speaker 1: A result, and then that result is passed directly into
Speaker 1: the second function. In summer, value A goes into the machine,
Speaker 1: gets transformed into value B, which drops into the next
Speaker 1: machine and becomes value C.
Speaker 2: Which works beautifully for single, solid blocks of data. But
Speaker 2: as we've said, web data almost always arise as a
Speaker 2: list of multiple items. This brings us to the most common,
Speaker 2: probably the most powerful processor in the entire framework, the
Speaker 2: map composed processor.
Speaker 1: Okay, so how does map compose handle those lists differently
Speaker 1: than regular.
Speaker 2: Composed The fundamental difference is iteration. Instead of passing the
Speaker 2: whole massive chunk of data into a function at one uns,
Speaker 2: map compose isolates the elements in the list.
Speaker 1: It separates them.
Speaker 2: Yes, it takes the first function and applies it to
Speaker 2: each individual element separately. It processes element one, then element two,
Speaker 2: then element three.
Speaker 1: And the results of those individual cleanings are concatenated to
Speaker 1: construct a new list exactly.
Speaker 2: Then that entire new list is subjected to the second function,
Speaker 2: again iterating element by element.
Speaker 1: To make sure we're crystal clear on the mechanics here,
Speaker 1: let's use a physical analogy. Let's say our raw data
Speaker 1: is a large basket of freshly dug potatoes completely covered
Speaker 1: in thick mud.
Speaker 2: Okay, I'm following, and our goal.
Speaker 1: Is a basket of perfectly washed, peeled potatoes.
Speaker 2: A very standard data cleaning requirement, right.
Speaker 1: So using the regular composed processor is like taking the
Speaker 1: entire basket of potatoes and dumping it into a large
Speaker 1: tub of water and trying to wash the whole mass
Speaker 1: at once.
Speaker 2: Yeah, the potatoes on the outside might get clean, but
Speaker 2: the ones trapped in the middle are still going to
Speaker 2: have mud on them when you pull the basket out
Speaker 2: to try and peel them.
Speaker 1: Exactly. It acts on the entire mass simultaneously, which leaves
Speaker 1: huge room for error.
Speaker 2: Especially when dealing with complex nested HTML structures.
Speaker 1: But map composed is different. Because map compose iterates, it
Speaker 1: means You take one single potato out of the basket,
Speaker 1: you wash it, You place it in a new clean basket.
Speaker 1: You take the next potato, wash it, move it right,
Speaker 1: and once the entire basket is washed, you iterate again
Speaker 1: with the peeler. You take one clean potato, peel it
Speaker 1: and move it. You process each piece individually, which guarantees
Speaker 1: every single element is perfectly handled.
Speaker 2: The logic of that analogy maps perfectly to messy web data.
Speaker 2: We do not want to try and clean a massive
Speaker 2: block of HTML code at once. Too messy, way too messy.
Speaker 2: We use map composed to isolate every single HTML element,
Speaker 2: strip the code tags off each one individually, and collect
Speaker 2: the clean text brilliant.
Speaker 1: Now there's one more tool we need to cover for
Speaker 1: a very specific type of modern data.
Speaker 2: Ah the select chains processor. Yes, this is a specialized
Speaker 2: tool for querying values using a Jason path because modern
Speaker 2: web applications and APIs frequently send data back in JSON
Speaker 2: format instead of traditional htemail.
Speaker 1: Let's visualize Jason format for a second. It's essentially a
Speaker 1: series of nested folders, or like Russian nesting dolls.
Speaker 2: That's a great way to picture it.
Speaker 1: You open the biggest doll labeled store and inside is
Speaker 1: a doll labeled books. You open that to find science fiction,
Speaker 1: and inside that is the final piece of data the title.
Speaker 2: And the select James processor allows you to write a
Speaker 2: specific path that acts like a laser pointer. It pierces
Speaker 2: straight through those nested dolls to extract exactly the data
Speaker 2: point you need without having to manually open every single layer.
Speaker 1: Okay, so we have a full toolkit. Now we understand
Speaker 1: how to set guardrails. We understand the input and output
Speaker 1: assembly line, and we have tools to handle empty spaces,
Speaker 1: shattered data, complex iterative cleaning, and nested Jason structures.
Speaker 2: We are ready.
Speaker 1: Let's mentally walk through a practical scenario. Let's look at
Speaker 1: the data blueprint for our catalog of books and actually
Speaker 1: set up the assembly line to clean it.
Speaker 2: All right, let's establish our workbench. We import the specific
Speaker 2: processors we know we'll need from the framework Join, map,
Speaker 2: compose and take first GOTTAM. We also bring in a
Speaker 2: specialized function called removed tags, which is exactly what it
Speaker 2: sounds like. It targets HTML markup and deletes it, leaving
Speaker 2: only the text.
Speaker 1: And let's also say we wrote our own custom validation
Speaker 1: function called filter price. It analyzes text to ensure the
Speaker 1: value is actually a mathematical number.
Speaker 2: Right, Because the spider accidentally scrapes the word free or
Speaker 2: out of stock, we don't want those words corrupting a
Speaker 2: database column designed specifically for numerical pricing data.
Speaker 1: So our tools are ready. The raw data for the
Speaker 1: book title field comes in from the spider. The input
Speaker 1: processor takes over immediately, and.
Speaker 2: We instructed to use map composed to run our removed
Speaker 2: tags function.
Speaker 1: So the raw data, which is just a messy list
Speaker 1: of heading tags and text, gets fed into map compose.
Speaker 1: It isolates every individual piece, strips out all the HTML,
Speaker 1: and creates a new list containing just the clean words
Speaker 1: of the title.
Speaker 2: The input phase is complete. Now the output processor kicks
Speaker 2: in to finalize the.
Speaker 1: Field for the title. We call the join function. It
Speaker 1: takes that list of clean text fragments and joins them
Speaker 1: together into a single cohesive string. The title is officially
Speaker 1: clean and stored perfect.
Speaker 2: Now we move to the price field. The raw price
Speaker 2: data comes in. The input processor again uses map composed
Speaker 2: running removed tags to strip the HTML.
Speaker 1: But remember map compose allows us to chain functions iteratively.
Speaker 2: Yes, so after it strips the tags from an element
Speaker 2: we instructed to immediately run that custom filter price function.
Speaker 1: It strips the HTML and then instantly verifies hey is
Speaker 1: the remaining text a valid number? It validates the data
Speaker 1: on the fly.
Speaker 2: And by the time the input processing is done, we
Speaker 2: have a list containing only clean, verified numerical prices.
Speaker 1: But for the output processor on the price, we face
Speaker 1: a different logical problem. We can't use join. No, definitely not,
Speaker 1: because if we use join and the list had two numbers,
Speaker 1: our final price would literally be nine time teaen point
Speaker 1: nine point ninety nine. We just want the definitive price.
Speaker 2: Which is why we invoke take first. It grabs the
Speaker 2: first clean valid number from our filtered list, assigns it
Speaker 2: as the final value for the books price, and just
Speaker 2: discards the rest.
Speaker 1: The elegance of this architecture is just remarkable. I mean,
Speaker 1: the raw, messy code comes in from the chaotic Internet,
Speaker 1: the input processor isolates it, strips it down, and verifies
Speaker 1: it piece by piece like a relentless assembly line, and.
Speaker 2: Then the output processor neatly packages it up into a
Speaker 2: single perfect data point. Because you know, if your input
Speaker 2: is garbage, your output is garbage. This system ensures your
Speaker 2: input is pristine.
Speaker 1: It really does well. Before we wrap up, let's pose
Speaker 1: a quick review exercise for you listening to test your
Speaker 1: grasp of this architecture.
Speaker 2: Good idea. Think back to our toolkit. If you have
Speaker 2: a list of raw HTML elements and you need to
Speaker 2: strip the tags from each individual piece separately before combining them,
Speaker 2: which built in processor is the correct tool for the job?
Speaker 2: If you select did map compose, you understand the mechanics
Speaker 2: of iterative cleaning.
Speaker 1: Awesome. So our assembly line has perfectly cleaned, validated, and
Speaker 1: boxed the data. But you know a box sitting on
Speaker 1: the floor of a warehouse is pretty useless. It has
Speaker 1: to be shipped right. The sources we explore today mentioned
Speaker 1: that after an item is fully scraped and processed, it's
Speaker 1: sent to an item pipeline. This pipeline is a set
Speaker 1: of components executed sequentially.
Speaker 2: Yeah, the processor is cleaned the data, but the pipeline
Speaker 2: is the mechanism that actually deploys it.
Speaker 1: And it raises a really fascinating question to leave you with,
Speaker 1: what kind of powerful things happen in the pipeline when
Speaker 1: that pristine data is finally exported.
Speaker 2: That's the big question.
Speaker 1: Does it feed directly into machine learning models to predict
Speaker 1: market trends. Does it trigger real time algorithmic trading alerts
Speaker 1: or does it build like massive interconnected business intelligence databases.
Speaker 2: That handoff point from a clean box of data to
Speaker 2: an actionable system is where the real magic happens.
Speaker 1: And that is the next frontier for you to explore.
Speaker 2: The possibilities are truly endless once you master the refinery.
Speaker 1: Thank you for joining us on this deep dive into
Speaker 1: the architecture of data gathering. Keep exploring, keep questioning the
Speaker 1: systems around you, and we'll catch you next time.