Myah  Conn

Myah Conn

1593284940

Sneaker Data Pipeline - Grailed listings

Purpose

From a high level I want to create a scraper with Python and explore some of Google Cloud Platform offerings, specifically Cloud Storage, Cloud Functions, Cloud Scheduler and Pub/Sub. With the goal being to create an easy way to automate recurring jobs that extract and archive listing data from the popular fashion resale site www.Grailed.com. Like most everyone, I’ve had nothing but time at home over the last couple of weeks (quarantine), I decided to document the process of creating a web scraper and deploying a data pipeline.

Exploratory phase

I start all product/ecommerce related scraping projects browsing the website’s different product pages at different “levels”, meaning I intentionally look at pages with multiple products grouped by certain attributes (e.g. mens footwear or all Nike products), individual product pages, and products returned by using the sites search tool. Doing all of this while inspecting network traffic. As I click through to the different pages, I pay attention to requests being made where product related information is used, either directly in the request URL or in the parameters sent with a request. Firefox developer tools is my go to tool here for two main reasons. First, because I like being able to use the network panel interface to view, edit and resend requests. Second, because Firefox has a great JSON viewer tool that shows collapsable objects and arrays.

Following this routine, one of the first things I catch is that Grailed is using Algolia to retrieve product/listing data on demand. When looking at specific categories and product groupings, try to understand what type of infrastructure is supporting the sites product related data. For example, some sites rely on Shopify which adheres to a very specific schema. On the other end of things, sites will create their own API. Most commonly, sites will use some third party tools and craft their API within a framework. In this case it’s clear Grailed is relying on a well known search API named Algolia.

The first thing I catch is that Grailed is using Algolia to retrieve it’s data on demand.

Planning and Scraping

After doing a little bit of research on Algolia I found that it is a well documented search/query/API service. This has text based search, searchable attributes and facet filtering. I copy a few requests from the network panel and recreate the same request, using the python requests package. You can also do this directly in the “dev tools” network panel in Firefox, without having to setup anything (Edit and Resend button from image above). Once request are successfully replicated using the same headers and parameters, play with different values in the parameters and try removing unneeded headers. Below is a look at what params are passed with a request, searching for the brand Off-White in the category Sneakers.

With this pattern of parameters you can start to define a function that will accept a variation of parameters and make your request.

def get_grailed_data(designer, category):
	    headers = {
	        'Host': 'mnrwefss2q-dsn.algolia.net',
	        'Origin': 'https://www.grailed.com',
	        'accept': 'application/json',
	        'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36',
	        'content-type': 'application/x-www-form-urlencoded',
	        'X-Algolia-API-Key': f'{API_KEY}',
	        'X-Algolia-Application-Id': f'{APPLICATION_ID}',
	        'hitsPerPage': '100'
	    }
	    url = 'https://mnrwefss2q-dsn.algolia.net/1/indexes/Listing_production/'
	    params = f'page=1&hitsPerPage=100&facetFilters=[["designers.name:{designer}"],["category_path:{category}"]]'
	    req = requests.get(url, headers=headers, params=params)
	    if req.status_code == 200:
	        return req.json()
	    else:
	        return req.status_code

This by-passes scraping data from the actual html on the site and instead requests data from a server directly. I will use designers.name and category_path facets later on to dynamically generate a set of queries in my scraper.

#data-science #web-scraping #python #fashion #google-cloud-platform

What is GEEK

Buddha Community

Sneaker Data Pipeline - Grailed listings
 iOS App Dev

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Cyrus  Kreiger

Cyrus Kreiger

1618039260

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling

1597579680

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

Uriah  Dietrich

Uriah Dietrich

1618457700

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data