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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

Poor Data Quality New Job Roles in Data Quality

Data quality is on rising and important to organizations today. Since in Experian research it has found that poor data quality causing losses to the companies.

Experian research suggests companies in the UK, the US, Australia, and western Europe have poorer quality data this year than last. The credit information company’s 2015 Global Data Quality Research among 1,239 organizations found a dramatic lack of data quality “ownership”, and 29% of respondents were still cleaning their data by hand.
data quality
The number of organizations that suspect inaccurate data has jumped from 86% in 2014 to 92%. Also, respondents reckoned 26% of their data to be wrong, up from 22% in 2014 and 17% in 2013. Some 23% of respondents said this meant lost sales, up from 19% in 2013.

Boris Huard, managing director of Experian Data Quality, said: “Getting your data strategy right is vital if you want to be successful in this consumer-driven, digitalized age. 

It is encouraging that companies are increasingly switching on to the value of their data assets, with 95% of respondents stating that they feel driven to use their data to understand customer needs, find new customers or increase the value of each customer.”

Poor Data Quality costs millions of pounds to the companies. About one-third of organizations use automated systems, such as monitoring and audit technology (34%), data profiling (32%) or matching and linkage technology (31%) to clean their data. A total of 29% still use manual checking to clean their data.

Huard added: “As our Dawn of the CDO research demonstrated, a new breed of chief data officers, chief digital officers, and director of insights are emerging – new roles that have come about in response to the pressure and opportunity presented by big data.”

However, only 35% of respondents said they manage data quality by way of a single director and nearly 63% are missing a coherent, centralized approach to data quality. More than half said individual departments still go their own way with respect to data quality enforcement, and 12% described their data quality efforts as “ad hoc”.

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