The end of WhatsApp as we know it?

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This week’s problem uses two different algorithmic patterns in the same problem. Let’s see if you can solve it.

Today we will cover:

• Word Search

• Data Lake vs Data Warehouse 

Read time: under 4 minutes

CODING CHALLENGE

Word Search

Given an m x n grid of characters board and a string word, return true if word exists in the grid.

The word can be constructed from letters of sequentially adjacent cells, where adjacent cells are horizontally or vertically neighboring. The same letter cell may not be used more than once.

Example 1:

Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "ABCCED"

Example 2:

Input: board = [["A","B","C","E"],["S","F","C","S"],["A","D","E","E"]], word = "ABCB"
Output: false

Solve the problem here before reading the solution.

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SOLUTION

To solve this problem, we'll use a depth-first search (DFS) approach with backtracking. The idea is to start at each cell of the board and try to match the characters of the word by exploring adjacent cells recursively.

We'll define a helper function that performs the DFS. This function will check if the current cell matches the current character of the word and then explore its neighboring cells. We'll use a visited set to ensure we don't use the same cell more than once.

The time complexity is O(m * n * 4^L), where m and n are the dimensions of the board, and L is the length of the word. In the worst case, we might explore 4 directions for each character of the word.

SYSTEM DESIGN

Data Lake vs Data Warehouse

Organizations collect massive amounts of data, but storing and analyzing it efficiently can be challenging. Two popular solutions are Data Lakes and Data Warehouses. Let's understand when to use each one.

A Data Warehouse is like a well-organized library where data is processed, structured, and stored in a specific format. Before data enters a warehouse, it goes through ETL (Extract, Transform, Load) process - cleaning, formatting, and organizing the data respectively. This makes it easy to analyze but takes time to set up and requires you to define the structure upfront.

A Data Lake, on the other hand, is more like a storage container where you can dump any type of data in its raw form - structured, semi-structured, or unstructured. There's no need to process the data before storing it. This makes it quick to set up and flexible for future needs, but analyzing the data requires more work later.

Data Warehouses are great when you know exactly what kind of analysis you need. For example, if you're tracking sales performance, a Data Warehouse can efficiently store and process sales data in a way that makes reporting easy.

Data Lakes shine when you're not sure how you'll use the data in the future. They're perfect for machine learning projects where you might need raw data in different formats, or when storing data that doesn't fit neatly into tables, like images or text documents.

Here's a comparison of Data Lake vs Data Warehouse:

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NEWS

This Week in the Tech World

Meta Finally Brings Ads to WhatsApp: After 11 years, Meta is adding ads to WhatsApp's Updates tab. The move aims to monetize the app's 3 billion users while keeping personal messages separate from promotions.

Amazon Extends Prime Day to Four Days: Amazon's Prime Day will run July 8-11 for the first time as a four-day event. The company is adding daily themed deals to create urgency and boost its $139 yearly membership.

Google Cuts Ties with Scale AI After Meta Deal: Google plans to end its $200 million annual contract with Scale AI after Meta took a 49% stake. Other major clients like Microsoft and xAI are also backing away over rivalry concerns.

Spotify CEO Leads $694M Defense AI Investment: Daniel Ek's Prima Materia led a 600 million euro funding round for European defense startup Helsing. The AI firm analyzes battlefield data and makes military drones.

OpenAI Wins $200M Defense Contract: The Defense Department awarded OpenAI a one-year $200 million contract for AI tools. This marks OpenAI's first major government deal as it launches "OpenAI for Government."

Google Apologizes for Major Cloud Outage: Google released an incident report after a seven-hour outage disrupted 70+ services including Gmail and Google Drive. The crash was caused by flawed updates and affected global users.

Anne Wojcicki Buys Back 23andMe for $305M: 23andMe founder outbid Regeneron to regain control of the bankrupt genetic testing company through her nonprofit TTAM Research Institute, protecting customer DNA data.

BONUS

Just for laughs 😏

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Cheers,

Sahil + Sarra