Excel  Tutorial

Excel Tutorial

1658384415

How to Web Scrape Data in Google Sheets!

In this video, I take you step by step on how to web scrape data in google sheets. I focus on following three main web scraping strategies in this video:

1. Scraping full data sets
2. Scraping individual metrics from data sets
3. Automating the web scraping process in google sheets

The HTML code I use to inspect tables is the following:

var i = 1; [].forEach.call(document.querySelectorAll('table'), function(x) { console.log(i++, x); });


If you are trying to inspect a list instead of a table, use the following HTML code:

var i = 1; [].forEach.call(document.querySelectorAll('ul,ol'), function(x) { console.log(i++, x); });


The google sheets codes used in this video are below:

=IMPORTHTML("https://en.wikipedia.org/wiki/List_of_National_Basketball_Association_single-game_scoring_leaders","Table","2")

=IMPORTHTML("https://finviz.com/quote.ashx?t=AAPL","Table","7")

=(index(IMPORTHTML("https://finviz.com/quote.ashx?t=AAPL","Table","7"),7,2))

=(index(IMPORTHTML("https://finviz.com/quote.ashx?t="&A3,"Table","7"),7,2))

 

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How to Web Scrape Data in Google Sheets!
Ray  Patel

Ray Patel

1623262740

Cloud Based Web Scraping for Big Data Applications 

Have you ever wondered how companies started to maintain and store big data? Well, flash drives were only prevalent at the start of the millennium. But with the advancement of the internet and technology, the big data analytics industry is projected to reach $103 billion by 2027, according to** Statista**.

As the need to store big data and access instantly increases at an alarming rate, scraping and web crawling technologies are becoming more and more useful. Today, companies mainly use web scraping technology to regulate price, calculate the consumer satisfaction index, and assess its intelligence. Read on to find the uses of cloud-based web scraping for big data apps.

What is Web Scraping?

How Cloud-Based Web Scraping Benefits an Organisation?

#data-analytics #web-scraping #big-data #cloud based web scraping for big data applications #big data applications #cloud based web scraping

 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

How POST Requests with Python Make Web Scraping Easier

When scraping a website with Python, it’s common to use the

urllibor theRequestslibraries to sendGETrequests to the server in order to receive its information.

However, you’ll eventually need to send some information to the website yourself before receiving the data you want, maybe because it’s necessary to perform a log-in or to interact somehow with the page.

To execute such interactions, Selenium is a frequently used tool. However, it also comes with some downsides as it’s a bit slow and can also be quite unstable sometimes. The alternative is to send a

POSTrequest containing the information the website needs using the request library.

In fact, when compared to Requests, Selenium becomes a very slow approach since it does the entire work of actually opening your browser to navigate through the websites you’ll collect data from. Of course, depending on the problem, you’ll eventually need to use it, but for some other situations, a

POSTrequest may be your best option, which makes it an important tool for your web scraping toolbox.

In this article, we’ll see a brief introduction to the

POSTmethod and how it can be implemented to improve your web scraping routines.

#python #web-scraping #requests #web-scraping-with-python #data-science #data-collection #python-tutorials #data-scraping

Ray  Patel

Ray Patel

1623327060

How to Deal With the Most Common Challenges in Web Scraping

For those who practice data extraction as an essential business tactic, we’ve revealed the most common web scraping challenges.

Introduction

In the world of business, big data is key to competitors, customer preferences, and market trends. Therefore, web scraping is getting more and more popular. By using web scraping solutions, businesses get competitive advantages in the market. The reasons are many, but the most obvious are customer behavior research, price and product optimization, lead generation, and competitor monitoring. For those who practice data extraction as an essential business tactic, we’ve revealed the most common web scraping challenges.

#big data #data analytics #web scraping #data scraping #deal with the most common challenges in web scraping #scraper