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Data join question: The higher the Key Performance Indicator is, the better the performance of the tower. Please provide a solution to determine the best and worst performing tower, as well as the average tower performance per market. Datasets KPI Dataset = TOWERID, DATE, KPI Market Info = TOWERID, MARKET
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Big Data Engineer

Interviewed at AT&T

3.3★
Oct 16, 2015

Data join question: The higher the Key Performance Indicator is, the better the performance of the tower. Please provide a solution to determine the best and worst performing tower, as well as the average tower performance per market. Datasets KPI Dataset = TOWERID, DATE, KPI Market Info = TOWERID, MARKET

1.SQL: **d_customers** +-------------+-----------------------+---------------------+ | customer_id | membership_start_date | membership_end_date | +-------------+-----------------------+---------------------+ | 114 | 2015-01-01 | 2015-02-15 | | 116 | 2015-02-01 | 2015-03-15 | | 120 | 2015-02-15 | 2015-04-01 | | 221 | 2015-03-15 | 2015-10-01 | | 120 | 2015-05-15 | 2015-07-01 | +-------------+-----------------------+---------------------+ **d_shipments** +-------------+------------+-----------------------+----------+ | shipment_id | ship_date | receiving_customer_id | quantity | +-------------+------------+-----------------------+----------+ | 1 | 2015-02-13 | 114 | 2 | | 2 | 2015-03-01 | 116 | 4 | | 2 | 2015-03-01 | 116 | 1 | | 3 | 2015-06-01 | 116 | 1 | | 4 | 2015-03-01 | 120 | 6 | | 5 | 2015-10-01 | 120 | 3 | | 6 | 2015-03-01 | 321 | 10 | +-------------+------------+-----------------------+----------+ Populate **a_shipments** +-----------+-----------+----------+----------+----------+ | ship_date | customer_id | is_member | quantity | +-----------+-----------+----------+----------+----------+ the column [is_member]: if [ship_date] is between [membership_start_date] and [membership_end_date] then 'y', else 'N' sample of otput: 2015-03-01 | 116 | Y | 5 | 2015-06-01 | 116 | N | 1 | 2. Coding task. Check whether a string is palindrome. I have been asked to code a solution by iterative and recursive approach. 3. Big Data questions: 3.1. What format of files in Hadoop do I know? What is a difference between Avro and Parquet format? 3.2. How compression is used in Avro and Parquet formats? 3.3. Most difficult big data performance challenges you have faced and resolved? 3.4. Spark optimization. Spark cost based optimizer
avatar

Big Data Engineer II

Interviewed at Amazon

3.5★
May 18, 2021

1.SQL: **d_customers** +-------------+-----------------------+---------------------+ | customer_id | membership_start_date | membership_end_date | +-------------+-----------------------+---------------------+ | 114 | 2015-01-01 | 2015-02-15 | | 116 | 2015-02-01 | 2015-03-15 | | 120 | 2015-02-15 | 2015-04-01 | | 221 | 2015-03-15 | 2015-10-01 | | 120 | 2015-05-15 | 2015-07-01 | +-------------+-----------------------+---------------------+ **d_shipments** +-------------+------------+-----------------------+----------+ | shipment_id | ship_date | receiving_customer_id | quantity | +-------------+------------+-----------------------+----------+ | 1 | 2015-02-13 | 114 | 2 | | 2 | 2015-03-01 | 116 | 4 | | 2 | 2015-03-01 | 116 | 1 | | 3 | 2015-06-01 | 116 | 1 | | 4 | 2015-03-01 | 120 | 6 | | 5 | 2015-10-01 | 120 | 3 | | 6 | 2015-03-01 | 321 | 10 | +-------------+------------+-----------------------+----------+ Populate **a_shipments** +-----------+-----------+----------+----------+----------+ | ship_date | customer_id | is_member | quantity | +-----------+-----------+----------+----------+----------+ the column [is_member]: if [ship_date] is between [membership_start_date] and [membership_end_date] then 'y', else 'N' sample of otput: 2015-03-01 | 116 | Y | 5 | 2015-06-01 | 116 | N | 1 | 2. Coding task. Check whether a string is palindrome. I have been asked to code a solution by iterative and recursive approach. 3. Big Data questions: 3.1. What format of files in Hadoop do I know? What is a difference between Avro and Parquet format? 3.2. How compression is used in Avro and Parquet formats? 3.3. Most difficult big data performance challenges you have faced and resolved? 3.4. Spark optimization. Spark cost based optimizer

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