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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
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How to Use ML in IoT Projects

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Why you need machine learning skills? Let us start with Big data. Big data relates to extremely large and complex data. So, the availability of huge data makes machine learning is popular to use in future prediction.

6 ideas how to use ML in IoT

  1. Machine Learning comprises algorithms that learn from data, make predictions based on their learning, and have the ability to improve their outcomes with experience. Due to the enormity of data involved with Machine Learning, various technologies and frameworks have been developed to address the same. Hadoop is an open-source framework targeted for commodity hardware to address big data scale.
  2. The distributed design of the Hadoop framework makes it an excellent fit to crunch data and draw insights from it by unleashing Machine Learning algorithms on it. 
  3. So, the true value of IoT comes from ubiquitous sensors’ relaying of data in real-time, getting that data over to Hadoop clusters in a central processing unit, absorbing the same, and performing Machine Learning on data to draw insights; all at petabyte scale or more.
  4. In reviewing the use cases and challenges from preceding sections, one thing is very clear. That is to do with the quickness with which certain analytics must be performed. Imagine sending a critical alert late because computing could not be done any faster. Two key gaps here include absorbing incoming data at such a high rate reliably and in observing that Hadoop was not created for real-time streaming data.
  5. It was originally envisaged as a framework for batch processing. Innovators have responded to those challenges well. Let us review some of those technologies now.
  6. SAP HANA with the internet of things came into the picture with real-time processing of data compared to Hadoop which is only batch processing. 
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