Remove Bytes Remove Programming Language Remove Scala Remove Structured Data
article thumbnail

Apache Spark vs MapReduce: A Detailed Comparison

Knowledge Hut

Big data sets are generally huge – measuring tens of terabytes – and sometimes crossing the threshold of petabytes. It is surprising to know how much data is generated every minute. quintillion bytes of data are created every single day, and it’s only going to grow from there. As estimated by DOMO : Over 2.5

Scala 96
article thumbnail

100+ Big Data Interview Questions and Answers 2023

ProjectPro

Data Variety Hadoop stores structured, semi-structured and unstructured data. RDBMS stores structured data. Data storage Hadoop stores large data sets. RDBMS stores the average amount of data. Works with only structured data. Hardware Hadoop uses commodity hardware.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

50 PySpark Interview Questions and Answers For 2023

ProjectPro

PySpark runs a completely compatible Python instance on the Spark driver (where the task was launched) while maintaining access to the Scala-based Spark cluster access. Although Spark was originally created in Scala, the Spark Community has published a new tool called PySpark, which allows Python to be used with Spark.

Hadoop 52
article thumbnail

Hadoop MapReduce vs. Apache Spark Who Wins the Battle?

ProjectPro

Spark follows a general execution model that helps in in-memory computing and optimization of arbitrary operator graphs, so querying data becomes much faster than disk-based engines like MapReduce. With Apache Spark, you can write collection-oriented algorithms using Scala's functional programming language.

Hadoop 40