Speaker 1: You've shared some incredibly relatable notes with us lately about
Speaker 1: the absolute headache that is raw webscraping, Like you know,
Speaker 1: the exact feeling we were talking about here.
Speaker 2: Oh yeah, completely. It's a universal pain point.
Speaker 3: Right.
Speaker 1: You write a script, You ask your code to go
Speaker 1: out and fetch data maybe I don't know prices, book titles,
Speaker 1: contact information, and instead of giving you a neat spreadsheet,
Speaker 1: your script basically kicks open your front door and dumps
Speaker 1: a massive, unorganized, completely unstructured pile of raw HTML tags
Speaker 1: right in the middle of.
Speaker 2: Your floor, which is exactly the painful reality of data
Speaker 2: extraction when you first start out as a developer. I mean,
Speaker 2: you successfully get the data, which is great, but it
Speaker 2: is just buried under weird formatting, Unicode errors and broken
Speaker 2: relative lengths.
Speaker 3: It's a total mess, exactly.
Speaker 2: It's practically unusable in that raw state.
Speaker 1: Precisely, and that is the mission for our deep dive today.
Speaker 1: We are going fundamentally beyond just fetching pages. We are
Speaker 1: architecting a clean, highly efficient, basically enterprise grade data extraction machine.
Speaker 2: I love the sound of that.
Speaker 1: We are going to walk through this step by step
Speaker 1: logic of taking that raw chaotic web data and transforming
Speaker 1: it into polished, validated information using built in framework tools.
Speaker 3: Because let's be real.
Speaker 1: Nobody wants a pile of digital garbage crashing their database.
Speaker 2: What's fascinating here is the necessary shift in a developer's
Speaker 2: mindset because scraping isn't just about grabbing data. Frankly, anyone
Speaker 2: can write a script to grab data, right.
Speaker 1: The grabbing part is easy, exactly.
Speaker 2: Professional level scraping is entirely about how you structure that
Speaker 2: data efficiently while it is in transit. So today we
Speaker 2: are really analyzing the journey that data takes. Okay, we
Speaker 2: will explore the mechanisms to collect it cleanly at the
Speaker 2: source and then crucially, how to route it through an
Speaker 2: automated sequential assembly line for processing and validation.
Speaker 1: Let's dive right into that source collection part, because before
Speaker 1: we can process any information at all, we need a reliable,
Speaker 1: scalable way to pick the actual items out of that
Speaker 1: missy pile of markup and the materials you've shared with us.
Speaker 1: Introduce a well a highly specific tool for this called
Speaker 1: an item loader.
Speaker 2: Yes, item loaders. In software engineering, we love encapsulation, right,
Speaker 2: and item loaders are these delightfully convenient mechanisms designed specifically
Speaker 2: to populate data objects.
Speaker 1: Okay, can you give me an example of how that looks.
Speaker 2: Sure, Let's imagine you are scraping a bookstore. You have
Speaker 2: a digital blueprint defined in your code for what a
Speaker 2: book should look like. That's your book item class, right,
Speaker 2: the template exactly. The item loader is the specialized tool
Speaker 2: you use to safely inject the actual extracted data into
Speaker 2: that book object.
Speaker 1: Tracking with you so far, But how does that logic
Speaker 1: actually execute in the code like step by step?
Speaker 2: Well, it begins with initialization at the top of your script.
Speaker 2: You import the item loader class. Then, when your spider
Speaker 2: hits a web page, you instantiate an item loader object okay,
Speaker 2: and you pass it to critical arguments. First the specific
Speaker 2: class you want to build, which is your book item. Second,
Speaker 2: you hand it the raw response data that your spider.
Speaker 1: Just downloaded, which is that messy pile of code on
Speaker 1: our floor.
Speaker 2: Precisely, that response.
Speaker 1: Is the pile. Okay, let's unpack this. So the loader
Speaker 1: is currently holding an empty box the book object, and
Speaker 1: it is looking at the pile of raw web data.
Speaker 1: But it needs a mechanism to actually move the data
Speaker 1: into the box.
Speaker 2: Right, Yes, exactly, And for every single field you want
Speaker 2: to populate, say the title, the author, or the price,
Speaker 2: you use specific extraction methods built right into the loader.
Speaker 2: Like what the most common ones you will use are
Speaker 2: ad XPath, add serial tip, or add value.
Speaker 3: Oh okay.
Speaker 2: Think of XPath and CSS selectors as like highly specific
Speaker 2: GPS coordinates on a web page's document object model or down.
Speaker 3: I like the GPS coordinates.
Speaker 2: Yeah, and these loader methods act as targeted grabbers. They
Speaker 2: reach into the raw HTML, pull out the exact string
Speaker 2: value at those coordinates, and stage it for the book object.
Speaker 1: I think I have a good announology for this that
Speaker 1: builds on the problem of the messy pile. Is an
Speaker 1: item loader essentially like sending a very literal, highly trained
Speaker 1: personal shopper into a massive grocery store.
Speaker 2: A game listening.
Speaker 1: You have them a specific list that is your item
Speaker 1: class the book object, and they know exactly which aisles
Speaker 1: and shelves to visit. Those are the XPath or CSS
Speaker 1: selectors to grab the exact raw goods you asked for,
Speaker 1: completely ignoring all the other noise in the store.
Speaker 2: That is a brilliant way to visualize it. Actually, the
Speaker 2: personal shopper doesn't get distracted by the promotional displays or
Speaker 2: the layout of the store. They just navigate directly to
Speaker 2: the precise coordinates you provided, grab the target item, and
Speaker 2: place it in their cart.
Speaker 1: But you know, as a developer, I have to push
Speaker 1: back a little here. Sure, why do we need to
Speaker 1: instantiate this specific loader object to act as the personal shopper?
Speaker 1: I mean, if I already know the xpass coordinates, why
Speaker 1: can't I just write a basic Python dictionary, grab the
Speaker 1: data manually in my code, and assign.
Speaker 2: It to a variable like a standard key value pair.
Speaker 1: Exactly, something like item title equals response dot x path
Speaker 1: and so on. It just feels like we are adding
Speaker 1: an extra layer of abstraction for absolutely no reason.
Speaker 2: It is a totally fair question, and honestly, it is
Speaker 2: a trap a lot of self taught developers fall into.
Speaker 2: Oh really, yeah, The reason you use an item loader
Speaker 2: is separation of concerns. When you grab data manually and
Speaker 2: stuff it into a dictionary, your core spider code gets
Speaker 2: extremely messy very.
Speaker 1: Quickly, because it's doing too much at once.
Speaker 2: Exactly, you are mixing the physical act of extracting data
Speaker 2: from the dom with the logic of parsing and structuring it.
Speaker 2: If a website changes its layout, your extraction logic and
Speaker 2: your structuring logic are completely tangled together.
Speaker 1: Ah, I see the problem. If I have fifty fields
Speaker 1: to scrape, a manual dictionary approach turns my spider into
Speaker 1: a massive, unreadable.
Speaker 3: Wall of code.
Speaker 2: Precisely, and it's a nightmare to maintain. The loader neatly
Speaker 2: encapsulates the extraction process. It holds the raw data secure
Speaker 2: surely so that it can be cleanly handed off to
Speaker 2: the next stage.
Speaker 3: That makes a lot of sense.
Speaker 2: And here is the mechanical kicker. The whole loading process
Speaker 2: officially concludes when you call a built in method called
Speaker 2: load item.
Speaker 1: Okay, so, if the loader is the shopper holding the
Speaker 1: box and we've mapped out the coordinates and grab the goods,
Speaker 1: there has to be a trigger that actually seals the
Speaker 1: box and says, hey, this item is finished. Because I'm
Speaker 1: assuming we can't just leave the data floating in the
Speaker 1: loader's memory forever. Load item must be that trigger.
Speaker 2: You hit the nail on the head. Load itom packages
Speaker 2: the populated object and hands it back to the framework's
Speaker 2: core engine. It essentially takes the finished box out of
Speaker 2: the shopper's hands and places it onto a conveyor belt.
Speaker 3: A conveyor belt, okay.
Speaker 2: Yes, and it also serves as the catalyst that triggers
Speaker 2: any pre processing or post processing logic you might have configured.
Speaker 2: If you were building this manually with basic Python dictionaries,
Speaker 2: you'd have to write all that handoff and processing logic
Speaker 2: from scratch every single time.
Speaker 1: Which brings us perfectly to the next major problem. Our
Speaker 1: personal shopper sealed the box and put it on the
Speaker 1: cavera belt, But if we look inside that box, the
Speaker 1: raw materials they gathered are probably still flawed. Oh all,
Speaker 1: Like a price might have a pound symbol or dollar
Speaker 1: sign stuck to it, or a string of text might
Speaker 1: have a bunch of weird whitespace characters from the HTML formatting.
Speaker 1: If I'm building a database for bargain hunters, saving a
Speaker 1: string like pound fifty five point zero zero is completely useless.
Speaker 1: Because a computer program cannot execute math on a currency symbol.
Speaker 1: We need a way to intercept and mutate that data
Speaker 1: in transit exactly.
Speaker 2: Structure does not equal cleanliness. The raw materials have been gathered,
Speaker 2: but they are absolutely not a finished product ready for
Speaker 2: a database. They need to be refined, checked, and validated
Speaker 2: in a very specific automated sequence.
Speaker 3: So how do we do that?
Speaker 2: Enter the item pipeline.
Speaker 1: Item pipelines. That implies a literal factory assembly line.
Speaker 2: And that is exactly how they function mechanically. Item pipelines
Speaker 2: are Python classes that the framework uses to chain together
Speaker 2: various transformations, operations, and checks on your raw scraped data.
Speaker 3: So it's an automated sequence.
Speaker 2: Exactly. Once LOADEDUM yields the data, the framework's engine automatically
Speaker 2: routes that item through these pipeline classes, executing them one
Speaker 2: after the other.
Speaker 1: Let's dig into the mechanics of that. What are we
Speaker 1: actually doing to the data inside these pipelines? Give me
Speaker 1: some real world use cases.
Speaker 2: They are essential for almost every professional scraping scenario. First
Speaker 2: is simply cleaning data. You might need a pipeline to
Speaker 2: format results, strip out unneeded HTML characters, or remove that
Speaker 2: currency symbol you mentioned, so you just have a pure
Speaker 2: integer or float to do mathon.
Speaker 1: That makes total sense. We need clean data types.
Speaker 3: What else?
Speaker 2: Validating data is massive. You need a pipeline step to
Speaker 2: ensure that the prices you've scraped are actually numerical values
Speaker 2: and not just a string of text that says out
Speaker 2: of stock.
Speaker 3: Well, I hate when that happens, right.
Speaker 2: You also use pipelines to check that URLs are structurally valid,
Speaker 2: or to verify with a regular expression that something claiming
Speaker 2: to be an email address actually contains an at symbol
Speaker 2: and a domain.
Speaker 1: So it's strict quality control it is.
Speaker 2: You also use pipelines to ensure that crucial items actually
Speaker 2: contain values and didn't just come back as empty null values.
Speaker 2: You can write logic to cross reference data and remove
Speaker 2: duplicate entries.
Speaker 3: Okay.
Speaker 2: Well, and finally, storing the final polished items into external files,
Speaker 2: sql databases or cloud storage is the classic final step
Speaker 2: of a pipeline.
Speaker 1: Let's ground this with a highly specific example. Let's say
Speaker 1: we are scraping products. We have our item loader, and
Speaker 1: we need to deal with pricing anomalies.
Speaker 2: Okay, Let's imagine we build a class called price pipeline.
Speaker 2: Every pipeline class requires a specific method called process item.
Speaker 2: As the framework routes your data, it passes the item
Speaker 2: through this method.
Speaker 3: Okay, process item. Got it.
Speaker 2: Let's say the website we are scraping lists prices before
Speaker 2: a mandatory sales tax inside process item, you can write
Speaker 2: a simple rule look at the items price field and
Speaker 2: if it doesn't include the sales tax, multiply the float
Speaker 2: value by the tax rate to calculate the true total,
Speaker 2: and then return the updated item to the conveyor belt.
Speaker 1: But wait, what if the personal shop or grabbed a
Speaker 1: product and because the target website had a temporary rendering glitch,
Speaker 1: the price field is just well empty. It happens a lot, right,
Speaker 1: And if you try to multiply a null value or
Speaker 1: an empty string by a tax rate, Python is going
Speaker 1: to throw a type AAR and your entire SCRAPEG program
Speaker 1: is going to crash.
Speaker 2: Right, It absolutely would crash, which is why the pipeline
Speaker 2: architecture has a built in error handling mechanism called the
Speaker 2: drop item exception.
Speaker 1: Drop item. Yes, inside your price pipeline, you write a
Speaker 1: defensive condition. If an item arrives completely missing a mandatory
Speaker 1: price field, you use the command raised drop item.
Speaker 2: What does raising that exception actually do mechanically? Does the
Speaker 2: program just stop entirely?
Speaker 1: No, And that is the beauty of it. The framework
Speaker 1: catches that specific exception, it completely halts that single item's
Speaker 1: progress down the pipeline.
Speaker 3: Oh, just the one item exactly.
Speaker 1: It pulls that defective item right off the conveyor belt,
Speaker 1: logs it, and discards it so it doesn't crash the
Speaker 1: next worker down the line. Meanwhile, all the other healthy
Speaker 1: items keep slowing without interruption.
Speaker 2: Here's where it gets really interesting. If we extend our metaphor,
Speaker 2: the pipeline is the factory floor. Yes, the shopper dumps
Speaker 2: the raw materials at the start of the belt as
Speaker 2: the item moves down the line. Each worker, meaning each
Speaker 2: process item method, has one hyper specific job.
Speaker 1: One worker cleans the text, the next worker verifies it's
Speaker 1: a real number. The next worker applies the sales tax calculation.
Speaker 1: And if any worker detects a critically defective item, they
Speaker 1: hit a button that raises the drop item exception, basically
Speaker 1: dropping it through a trapdoor before it can ruin the
Speaker 1: final shipment.
Speaker 2: That is the exact architectural flow you should be visualizing.
Speaker 2: It is a sequence of highly specialized, isolated workers.
Speaker 1: To truly understand how this machine is constructed, we need
Speaker 1: to see it run end to end. Let's trace a
Speaker 1: practical example, step by step. Let's talk about scraping a
Speaker 1: sandbox site.
Speaker 2: A great exercise. Let's use books dot to scrape dot com. Okay,
Speaker 2: it's a classic sandbox environment structured into different catalog categories.
Speaker 2: It is essentially a mock online bookstore, which makes it
Speaker 2: the perfect legal playground to test these extraction tools.
Speaker 3: Let's look at the before picture.
Speaker 1: Before developer learns about loaders and pipelines, how do they
Speaker 1: traditionally try to scrape this bookstore. Let's talk about the
Speaker 1: pain of the manual approach.
Speaker 2: Oh, the manual approach, Well, you write your basic spider logic,
Speaker 2: you set it up to crawl through the catalog pages.
Speaker 2: You write your expath selectors to target the book's title,
Speaker 2: the price, the cover image URL, and the actual product
Speaker 2: page URL.
Speaker 1: That sounds like standard targeting, but the pain starts where
Speaker 1: you try to.
Speaker 2: Save it exactly. To save that data manually, developers typically
Speaker 2: set up a four loop to iterate over the extracted elements,
Speaker 2: and inside this loop, if they aren't using a framework's
Speaker 2: built in export tools, they will often import a Python
Speaker 2: library called codex codex.
Speaker 1: If you're doing this mannually, you're probably wrestling with Codex
Speaker 1: to handle file writing.
Speaker 2: Yes, Codex is used for encoding and decoding data, specifically
Speaker 2: to handle things like UTF eight character encoding when writing
Speaker 2: text files safely. Right, you would manually use codex dot
Speaker 2: open to create a file called books dot txt or
Speaker 2: books dot csv. Then, line by line inside your loop,
Speaker 2: you command your script to write the extracted title, price,
Speaker 2: and URLs directly into that file, usually combining them into
Speaker 2: a comma separated string.
Speaker 1: And the second a book title actually contains a comma
Speaker 1: in its text, your entire CSV structure breaks because you
Speaker 1: just added an accidental column.
Speaker 2: Yes, so frustrating.
Speaker 1: Plus you are manually hard coding filepaths, opening file loocks,
Speaker 1: and just hoping the script doesn't crash half way through
Speaker 1: and corrupt the whole file.
Speaker 2: It is incredibly brittle. I mean it works for a
Speaker 2: ten item test, sure, but it does not scale to
Speaker 2: ten thousand items. If you want to change how the
Speaker 2: data is saved or add a step to clean the data,
Speaker 2: you have to rewrite your core spider code.
Speaker 1: Okay, so let's look at the after picture. How do
Speaker 1: we upgrade this manual, brittle clunker into a high performance
Speaker 1: machine using the framework tools.
Speaker 2: First, we completely delete the manual text file writing. We
Speaker 2: throw out the codex logic and the four loops entirely.
Speaker 3: Goodbye codex.
Speaker 2: Instead, we bring in our personal shopper. We instantiate the
Speaker 2: item loader and pass it the selector for the current
Speaker 2: book element.
Speaker 3: Okay, so the shopper's ready.
Speaker 2: Now we introduce a powerful new concept input processors, specifically
Speaker 2: a built in processor called map compose.
Speaker 1: The map compose what exactly is map composed doing under
Speaker 1: the hood.
Speaker 2: So, map compose is a processor that allows you to
Speaker 2: apply a sequence of functions to your extracted data immediately,
Speaker 2: like before it even fully settles into the item object.
Speaker 3: Wait immediately, Yes.
Speaker 2: Let's look at the URLs we scraped from the sandbox
Speaker 2: site on books dot to scrape dot com. The image
Speaker 2: URLs and the book URLs are incomplete. They are relative links. Okay,
Speaker 2: they look like catalog slash book one dot HTML instead
Speaker 2: of a full clickable web address.
Speaker 3: Right, they're just fragments.
Speaker 1: If you try to open them in a browser or
Speaker 1: save them to a database, they lead.
Speaker 2: Nowhere, precisely. So outside of our spider, we create a
Speaker 2: simple custom Python function called adlink. All this function does
Speaker 2: is take a string and concatenate or join the base
Speaker 2: URL of the website with the fragmented url. We just
Speaker 2: passed it returning a fully qualified functioning weblink.
Speaker 3: And we plug this custom function into map compost.
Speaker 2: Yes. When we configure our item loader, we tell it
Speaker 2: that whenever it grabs a raw URL fragment via XPath,
Speaker 2: it must immediately pass that fragment through the map composed processor,
Speaker 2: and we pass ad link as the argument. Oh wow,
Speaker 2: the processor applies the function, meaning the URL is fixed
Speaker 2: and properly formatted before it is ever officially assigned to
Speaker 2: our book item. You can even chain them. You could
Speaker 2: have map composed, strip white space, then apply ad link
Speaker 2: all in one line of code.
Speaker 1: That is incredibly slick. But wait, I have a mechanical
Speaker 1: question about that.
Speaker 2: Go for it.
Speaker 1: Why is map composed designed this way? If I target
Speaker 1: one single price or one URL on a page, shouldn't
Speaker 1: it just return a single string? Why do we need
Speaker 1: a special processor to map functions over it?
Speaker 2: That is a fantastic catch. It has to do with
Speaker 2: how extraction languages work. XPath and CSS selectors are designed
Speaker 2: to query the dom and find all matches that fit.
Speaker 1: Your coordinates, right, all of them.
Speaker 2: Even if you know there is only one price on
Speaker 2: the page. The underlying framework engine doesn't know that so
Speaker 2: to be safe, it always returns extracted data as a
Speaker 2: Python list an iterable array. It returns a list containing
Speaker 2: your one item.
Speaker 1: Ah, so the data is trapped inside a list format
Speaker 1: like bracket quote fifty pounds end quote bracket instead of
Speaker 1: just the string fifty.
Speaker 2: Pounds exactly, and mac composed is specifically built to iterate
Speaker 2: over lists. It applies your function to every element inside
Speaker 2: that list. Okay, that makes sense, so you still have
Speaker 2: a list at the end of it. This is why
Speaker 2: we must also assign an output processor called take first
Speaker 2: to our loader.
Speaker 1: Let me guess takes first, looks at the list, grabs
Speaker 1: the very first element out of it, and discards the
Speaker 1: list's structure entirely.
Speaker 2: You've got it. It extracts the clean string value right
Speaker 2: out of the array.
Speaker 1: So to summarize the immense benefit for you listening, by
Speaker 1: deleting our manual codex loop and plugging into map compose
Speaker 1: and take first via the item loader, we are leveraging
Speaker 1: the frameworks Optimize internal engine exactly. We aren't reinventing the wheel.
Speaker 1: It makes your code modular, highly scalable, and the resulting
Speaker 1: items are so well structured that you can just tell
Speaker 1: the framework to output them directly to ADJSON or CSV
Speaker 1: file with a single command line flag, without writing a
Speaker 1: single line of file saving code.
Speaker 2: It lets the framework handle all the heavy lifting of
Speaker 2: state management and file locking, freeing you up to focus
Speaker 2: entirely on the data logic.
Speaker 1: Which means we can finally move to the advanced logic.
Speaker 1: Our URLs are fixed, our strings are pulled out of
Speaker 1: their lists, our data is structured. Let's use the pipelines
Speaker 1: to execute some actual business rules. Let's return to our
Speaker 1: scenario where we're building a tool for bargain hunters. We
Speaker 1: only want to find and process affordable books.
Speaker 2: A perfect real world application, Let's say we want to
Speaker 2: analyze the prices and anything that costs over fifty we
Speaker 2: want to flag it as expensive, so our downstream systems
Speaker 2: know exactly how to categorize it.
Speaker 3: How do we architect that sequence?
Speaker 2: First, we rely on the cleaning task we mentioned earlier.
Speaker 2: The price we scraped definitely has a pound symbol attached
Speaker 2: to it. A computer cannot evaluate if a symbol is
Speaker 2: greater than fifty, so we already have an input processor
Speaker 2: stripping out the parent symbol and converting the string to
Speaker 2: a float. So we have a pure number.
Speaker 3: Okay, so the data is prepped.
Speaker 2: Next, we set up two entirely separate pipeline classes in
Speaker 2: our project to handle our business logics sequentially, and this
Speaker 2: sequence is strictly managed in a central configuration file called
Speaker 2: settings dot pi.
Speaker 1: Two pipelines. I see where this is going. But how
Speaker 1: does settings dot pi actually know which one to run first?
Speaker 2: Inside Settings dot pi there is a configuration dictionary specifically
Speaker 2: for item pipelines. The keys are the names of your
Speaker 2: pipeline classes, and the values are priority numbers, typically ranging
Speaker 2: from zero to a thousand. Oh I see the framework's
Speaker 2: engine reads this dictionary. The lower the number, the earlier
Speaker 2: that pipeline runs on the assembly line.
Speaker 1: So it acts as the exact schedule for the factory floor.
Speaker 1: Walk me through the data flow here.
Speaker 2: Our first pipeline class is called books price. In settings
Speaker 2: dot pi, we map it to a priority value of
Speaker 2: one hundred. Okay, because one hundred is the lowest number
Speaker 2: in our setup. The engine routes the data there first.
Speaker 2: Its processidea method has a simple job. It evaluates the
Speaker 2: pure number price. If the float value is strictly greater
Speaker 2: than fifty, it overwrites the item's price field entirely with
Speaker 2: the text string expensive got it if it's fifty or below.
Speaker 2: It leaves the price field exactly as it is.
Speaker 1: So books price at priority one hundred is essentially the
Speaker 1: labeling worker. It slaps an expensive sticker on the pricey
Speaker 1: books and passes them along exactly.
Speaker 2: Then the framework takes the item and routes it down
Speaker 2: the belt to our second pipeline class, which we will
Speaker 2: call checks is viable In our settings dictionary. We map
Speaker 2: this to a priority value of two hundred. It fundamentals
Speaker 2: executes after pipeline one because.
Speaker 1: The engine sees that two hundred is numerically higher than
Speaker 1: one hundred.
Speaker 2: Right inside checks viable, the processcytem method looks out the
Speaker 2: price field. It checks to see if the field literally
Speaker 2: contains the string expensive. Okay, If it does not contain it,
Speaker 2: meaning it's an affordable book that passed through the first
Speaker 2: worker unchanged, it executes a command to print the book's
Speaker 2: details to the console for us to verify.
Speaker 1: So what does this all mean? I understand the priority
Speaker 1: numbers completely lower runs first, higher runs later, But I
Speaker 1: have to challenge the architecture here. Bring on, why split
Speaker 1: this into two different classes with numbers one hundred and
Speaker 1: two hundred. Why not just have one single pipeline worker
Speaker 1: at priority one hundred who checks if the float is
Speaker 1: over fifty, and if it's not, just prints it out
Speaker 1: right then and there. Yeah, writing two classes seems like
Speaker 1: over engineering.
Speaker 2: It might seem that way at first glance. But if
Speaker 2: we connect this to the bigger picture of software architecture,
Speaker 2: separating these concerns into modular pipelines is what saves you
Speaker 2: from technical debt. How so you have one pipeline strictly
Speaker 2: dedicated to data labeling and one entirely separate pipeline strictly
Speaker 2: dedicated to filtering. This makes your code infinitely reusable.
Speaker 1: How does that actually play out in the long run, though?
Speaker 2: Imagine if next month project requirements change, you decide you
Speaker 2: don't just want to hide the expensive books from the
Speaker 2: console output, you want to permanently delete them from your
Speaker 2: data set using that drop item exception we analyzed earlier. Ah,
Speaker 2: I see, Or maybe you want to take only the
Speaker 2: affordable data and write it to a post reschool database.
Speaker 2: Because your architecture is modular, you don't have to touch
Speaker 2: or risk breaking your core labeling logic.
Speaker 1: You just leave the one hundred pipeline alone exactly.
Speaker 2: You just write a new isolated database pipeline class, slot
Speaker 2: it into the settings dot pi dictionary with a priority
Speaker 2: of say three hundred, and you're done. Modularity gives you
Speaker 2: immense scaling flexibility.
Speaker 1: I completely see the vision. Now you're building a factory
Speaker 1: that is effortless to reconfigure. Let's do a quick recap
Speaker 1: of the architectural journey we just mapped out. We start
Speaker 1: with the absolute chaos of raw web.
Speaker 2: Markup the messy pile YEP.
Speaker 1: We brought in our personal shopper, the item loader, to
Speaker 1: extract exactly what we needed while separating our logic. We
Speaker 1: used input processors like map compose to fix broken relative
Speaker 1: URLs on the fly and take first to rescue our
Speaker 1: data from XPath.
Speaker 2: Lists beautifully summarized.
Speaker 1: And finally, we routed our clean items through a sequential
Speaker 1: pipeline assembly line managed by settings dot pi to evaluate numbers,
Speaker 1: label expensive items, and filter the final output based on
Speaker 1: strict priority execution.
Speaker 2: It is a phenomenal engineering transformation. You take a messy,
Speaker 2: unusable pile of HTML on the floor and automatically forge
Speaker 2: it into a pristine, perfectly validated data set, ready for
Speaker 2: any application and.
Speaker 1: To reinforce everything we unravel today, I want to leave
Speaker 1: you with a short mental exercise to test your understanding
Speaker 1: of the architecture. Think back to that dictionary in the
Speaker 1: settings dot PI file book sprice our labeling worker was
Speaker 1: assigned priority one hundred, checks viable our printing worker with
Speaker 1: priority two hundred. If you wanted to architect a brand
Speaker 1: new pipeline class that completely deletes the expense of books
Speaker 1: using the raise drop item exception, what exact priority number
Speaker 1: would you assign it in the dictionary to ensure it
Speaker 1: runs after the books are evaluated and labeled, but before
Speaker 1: the final viable books are printed out.
Speaker 2: That is a phenomenal puzzle. Understanding exactly where to place
Speaker 2: that new worker on the assembly line is where the
Speaker 2: true power of this framework unlocks.
Speaker 1: Think about the numerical sequence. We will leave you to
Speaker 1: map that out on your own. Thanks for joining us
Speaker 1: for this deep dive into architecting and elegant bulletproof data extraction.
Speaker 2: Machine, happy building, and keep your pipelines clean.
Speaker 3: Well. Gats you next time.