This extends 50+ SQL scenarios based interview Q&As – identifying consecutive records – job status repetition. A popular SQL coding interview question. Setup the data on db-fiddle MySQL V8.0 to practice. Q. Given the below input, how will you list user_id & … Read more ›...
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29: 50+ SQL scenarios based interview Q&As – Non-Equi Join problem
Q01. What is a Non-Equi Join? A01. A Non-Equi Join in SQL that uses operators other than equality operator (i.e. ‘=’), like , = to join two tables. Q02. When to use a Non-Equi Join? … Read more ›...
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30: 50+ SQL scenarios based interview Q&As – Data cleaning, quality checks & profiling
This post covers SQL examples to perform quality checks, clean data and profile data. Q01. What is Data Cleaning? Can SQL do data cleaning? A01. Data cleaning is the process of fixing bad data. … Read more ›...
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31: 50+ SQL scenarios based interview Q&As – Finding records from first table where no match found in second table
Setup the data on db-fiddle MySQL V9.0 to practice. There are business scenarios for:
1) Finding records in the first table where matching records are found in the second table are shown in the output. This can be done with a semi-join.
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32: 50+ SQL scenarios based interview Q&As – use of unpivot, lag(..), row_num(..), cumulative sum() and CTE
Setup the data on db-fiddle MySQL V9.0 to practice. Make sure to select latest MySQL version to ensure Common Table Expressions (i.e CTE) is supported. This is not a real life scenario problem, but a simple example that uses many of the SQL syntaxes & techniques. … Read more ›...
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33: 50+ SQL scenarios based interview Q&As – Nuances of NULL in SQL
NULL is a special marker used in SQL to indicate that a data value does not exist in the database.
Setup the data on db-fiddle MySQL V9.0 to practice. Make sure to select latest MySQL version to ensure Common Table Expressions (i.e CTE) is supported.
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5 Amazon EMR Vs. Cloudera interview Q&As
Q1. What is Amazon EMR? A1. EMR stands for Elastic Map Reduce, which is an Amazon Web Services (AWS) tool for big data processing and analysis. Amazon EMR offers elastic, cost-effective, and expandable low-configuration service as an alternative to running an on premise or EC2 cloude based Hadoop cluster (E.g....
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5 Apache YARN interview Q&As
Q1. What is Apache YARN? A1. Apache Hadoop YARN (aka Yet Another Resource Negotiator) is a framework to provide computational resources for execution engines like Map Reduce, Apache Spark, Apache Storm, Hive, Hbase, etc. As a key Hadoop component, YARN is responsible for allocating system resources (E.g. … Read more...
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5 Spark streaming & Apache storm Interview Q&As
Q116. What is “Spark streaming” in the Spark ecosystem with Spark core, Spark SQL, Spark MLlib, Spark GraphX, etc? A116. Spark is a distributed and scalable batch processing framework that supports in-memory processing in sub-seconds. The batch processes are scheduled with something like Oozie or Unix cron jobs to run...
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6 Delta Lake interview questions & answers
Q01. What is delta lake for Apache Spark? A01. Delta Lake is an open source storage layer that brings reliability to data lakes. It as a dependency JAR that needs to be added to your Apache Spark project. … Read more ›...
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6 LDAP Interview Q&As
Q1. What is an LDAP server? And what is it used for in an enterprise environment? A1. LDAP stands for Lightweight Directory Access Protocol. This is an extensible open network protocol standard that provides access to distributed directory services. … Read more ›...
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8 Apache Spark repartition Vs. coalesce scenarios interview Q&As
Q01: Why is partitioning of data required in Apache Spark?
A01: Partitioning is a key concept in distributed systems where the data is split into multiple partitions so that you can execute transformations on multiple partitions in parallel, which helps a job to finish faster.
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8 Apache Spark streaming interview questions and answers
Q01 What is Spark streaming? A01 Spark streaming extends Spark core/sql to what is called a micro-batching to give Near-Real-Time processing where the arriving live stream of data from sources like Kafka, Kinesis, Flume, HDFS file system, etc divided into batches of predefined interval. … Read more ›...
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