Data Engineering - Session : 2

In this session, we discussed the complete working of the Hadoop ecosystem, HDFS architecture, and MapReduce processing in detail with practical examples. The session covered how large files are stored in distributed clusters, how data blocks are created, replicated, processed, and optimized using Hadoop frameworks.

Key concepts explained in this video:

Difference between traditional file formats and columnar formats like Parquet and ORC
Compression benefits of Parquet/ORC files
Faster reading performance using columnar storage
HDFS architecture explained step-by-step
Name Node, Data Node, Secondary Name Node, and Standby Name Node concepts
FS Image and Edit Logs working
Metadata management in Hadoop
Block storage and replication mechanism
Heartbeat mechanism in Hadoop clusters
Fault tolerance and failover concepts
Resource Manager and Node Manager working in YARN
Complete Read and Write operation flow in HDFS
MapReduce architecture explained with Word Count example
Mapper, Reducer, Combiner, Shuffle and Sort phases
Input Splits vs HDFS Blocks
Record Reader and Key-Value pair processing
Speculative Execution concept
Hadoop commands like copyFromLocal and get
Configuration files overview
Real-world production insights and interview-oriented explanations

This session is highly useful for:

Big Data beginners
Hadoop developers
Data Engineers
Spark and Databricks learners
Interview preparation for Big Data roles

If you are preparing for Data Engineering interviews or learning Hadoop ecosystem concepts from scratch, this session will help you understand the internal working of distributed processing systems clearly.

Hashtags

#Hadoop
#BigData
#DataEngineering
#MapReduce
#HDFS
#YARN
#ApacheHadoop
#Parquet
#ORC
#Spark
#Databricks
#DistributedSystems
#DataEngineer
#ETL
#ApacheSpark
#ClusterComputing
#TechInterview
#Coding
#Java
#CloudComputing
#AzureDatabricks
#GoogleCloud
#AWS
#SoftwareEngineering
#DataProcessing

Видео Data Engineering - Session : 2 канала Palin Analytics