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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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IBM PML Vs Google MapReduce why you need to read

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IBM Parallel Machine Learning Toolbox (PML) is similar to that of Google's MapReduce programming model (Dean and Ghemawat, 2004) and the open source Hadoop system,which is to provide Application Programming Interfaces (APIs) that enable programmers who have no prior experience in parallel and distributed systems to nevertheless implement parallel algorithms with relative ease.
google mapreduce

Google MapReduce Vs IBM PML

  1. Like MapReduce and Hadoop, PML supports associative-commutative computations as its primary parallelization mechanism. 
  2. Unlike MapReduce and Hadoop, PML fundamentally assumes that learning algorithms can be iterative in nature, requiring multiple passes over data.
  3. The ability to maintain the state of each worker node between iterations, making it possible, for example, to partition and distribute data structures across workers
  4. Efficient distribution of data, including the ability of each worker to read a subset of the data, to sample the data, or to scan the entire dataset.
  5. Access to both sparse and dense datasetsParallel merge operations using tree structures for efficient collection of worker results on very large clusters.
  6. In order to make these extensions to the computational model and still address ease of use, PML provides an object-oriented API in which algorithms are objects that implement a predefined set of interface methods.

PML Unique Features

  • The PML infrastructure then uses these interface methods to distribute algorithm objects and their computations across multiple compute nodes-An object-oriented approach is employed to simplify the task of writing code to maintain, update, and distribute complex data structures in parallel environments.
  • Several parallel machine learning and data mining algorithms have already been implemented in PML, including Support Vector Machine (SVM) classifiers, linear regression, transform regression, nearest neighbors classifiers, decision tree classifiers, k-means, fuzzy k-means, kernel k-means, principal component analysis (PCA), kernel PCA, and frequent pattern mining.

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