Design Connectivity 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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Tell me about a time when you had to sign a Non-Disclosure Agreement (NDA). Please provide specific details about the situation, including the parties involved, the nature of the confidential information, and the purpose of the NDA. Describe the scope of the agreement, including what was covered and any limitations. Explain your responsibilities under the NDA and how you ensured compliance with its terms. What steps did you take to protect the confidential information? Did you face any challenges in complying with the NDA, and if so, how did you overcome them? Share any lessons you learned from this experience regarding confidentiality and intellectual property protection. For example, imagine you were working on a sensitive project involving a new product or technology. You had to sign an NDA with a third-party vendor or partner to share certain confidential details. What were the key terms of the NDA, and how did you ensure that you and your team adhered to them? Or, describe a situation where you left a previous company and signed an NDA regarding trade secrets and customer information. How did you navigate your subsequent work to avoid any potential conflicts or violations of the NDA?