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SQL Interview Success: Unlocking the Top 5 Frequently Asked Queries

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 Here are the five top commonly asked SQL queries in the interviews. These you can expect in Data Analyst, or, Data Engineer interviews. Top SQL Queries for Interviews 01. Joins The commonly asked question pertains to providing two tables, determining the number of rows that will return on various join types, and the resultant. Table1 -------- id ---- 1 1 2 3 Table2 -------- id ---- 1 3 1 NULL Output ------- Inner join --------------- 5 rows will return The result will be: =============== 1  1 1   1 1   1 1    1 3    3 02. Substring and Concat Here, we need to write an SQL query to make the upper case of the first letter and the small case of the remaining letter. Table1 ------ ename ===== raJu venKat kRIshna Solution: ========== SELECT CONCAT(UPPER(SUBSTRING(name, 1, 1)), LOWER(SUBSTRING(name, 2))) AS capitalized_name FROM Table1; 03. Case statement SQL Query ========= SELECT Code1, Code2,      CASE         WHEN Code1 = 'A' AND Code2 = 'AA' THEN "A" | "A

Top Hadoop Architecture Interview Questions

The hadoop.apache.org web site defines Hadoop as "a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models." Quite simply, that's the philosophy: to provide a framework that's simple to use, can be scaled easily, and provides fault tolerance and high availability for production usage. The idea is to use existing low-cost hardware to build a powerful system that can process petabytes of data very efficiently and quickly. More : Top selected Hadoop Interview Questions Hadoop achieves this by storing the data locally on its DataNodes and processing it locally as well. All this is managed efficiently by the NameNode, which is the brain of the Hadoop system. All client applications read/write data through NameNode. Hadoop has two main components: the Hadoop Distributed File System (HDFS) and a framework for processing large amounts of data in parallel using the MapReduce paradigm HDFS