Summary of Learn Apache Spark in 10 Minutes | Step by Step Guide
The exponential growth of data in the early 2000s led to the emergence of Big Data, which refers to large and complex datasets that traditional methods struggle to process.
Hadoop was developed in 2006 as a distributed processing framework to address the challenges of Big Data.
Hadoop consists of HDFS for storing data and MapReduce for processing data in parallel.
Apache Spark was developed in 2009 to overcome the limitations of Hadoop, offering faster in-memory processing and support for multiple programming languages.
Apache Spark components include Spark Core, Spark SQL, Spark Streaming, and MLlib for machine learning.
The architecture of Apache Spark involves a cluster manager, driver processes, and executor processes.
Apache Spark uses lazy evaluation and actions to execute code efficiently.
An example project using Apache Spark involves importing data, creating a temporary view, and running SQL queries.
Apache Spark allows for the conversion of Spark data frames into Pandas data frames for additional analysis.
Speakers/sources
Not applicable.
Notable Quotes
— 03:15 — « Spark processes the entire data in just memory. The meaning of memory here is the RAM (Random Access Memory) stored inside our computer. And this in-memory processing of data makes Spark 100 times faster than Hadoop. »
— 04:13 — « Apache Spark became a powerful tool for processing and analyzing Big Data. Nowadays, in any company, you will see Apache Spark being used to process Big Data. »
— 05:42 — « The driver process is the heart of the Apache Spark application because it makes sure everything runs smoothly and allocates the right resources based on the input that we provide. »
Category
Educational