AI/Machine Learning Engineer

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Table: Sales +-------------+-------+ | Column Name | Type | +-------------+-------+ | sale_id | int | | product_id | int | | year | int | | quantity | int | | price | int | +-------------+-------+ (sale_id, year) is the primary key (combination of columns with unique values) of this table. product_id is a foreign key (reference column) to Product table. Each row of this table shows a sale on the product product_id in a certain year. Note that the price is per unit. Table: Product +--------------+---------+ | Column Name | Type | +--------------+---------+ | product_id | int | | product_name | varchar | +--------------+---------+ product_id is the primary key (column with unique values) of this table. Each row of this table indicates the product name of each product. Write a solution to select the product id, year, quantity, and price for the first year of every product sold. Return the resulting table in any order. Example: Sales table: +---------+------------+------+----------+-------+ | sale_id | product_id | year | quantity | price | +---------+------------+------+----------+-------+ | 1 | 100 | 2008 | 10 | 5000 | | 2 | 100 | 2009 | 12 | 5000 | | 7 | 200 | 2011 | 15 | 9000 | Product table: +------------+--------------+ | product_id | product_name | +------------+--------------+ | 100 | Nokia | | 200 | Apple | | 300 | Samsung | Output: +------------+------------+----------+-------+ | product_id | first_year | quantity | price | +------------+------------+----------+-------+ | 100 | 2008 | 10 | 5000 | | 200 | 2011 | 15 | 9000 | `

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Design a high data volume real-time data pipeline, discussing both high-level and low-level details. For example, imagine you're building a real-time clickstream processing pipeline for an e-commerce website that receives millions of clicks per minute. The pipeline needs to process this data to provide real-time insights such as top-selling products, website traffic analysis, and fraud detection. Consider aspects such as data ingestion, storage, processing, and serving. Discuss the technologies you would choose for each stage and justify your choices. Also, address potential challenges like data volume, velocity, variety, and veracity. How would you ensure the pipeline is scalable, reliable, and fault-tolerant? What monitoring and alerting mechanisms would you implement? How would you handle backpressure and ensure data consistency? Finally, how would you optimize the pipeline for cost-effectiveness and performance?

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Where are you from in the US?