Big Data Hadoop refers to a collection of open-source software utilities that facilitate the processing of large and complex data sets, often referred to as "big data." It is designed to handle massive volumes of data that cannot be managed by traditional data processing tools.Big Data Hadoop is a critical tool in the realm of big data analytics, allowing organizations to efficiently process and analyze massive amounts of data to extract valuable insights and inform decision-making.
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Introduction to Big Data
Overview of Big Data
Characteristics and Importance
Types of Data (Structured, Unstructured, Semi-structured)
Challenges in Traditional Data Processing
Introduction to Hadoop
Hadoop Distributed File System (HDFS)
Hadoop Ecosystem Components (e.g., Pig, Hive, HBase)
Setting up a Hadoop Cluster
Hadoop Architecture and Components
Hadoop Cluster Architecture
JobTracker and TaskTracker
NameNode and DataNode
Data Ingestion and Storage
Data Ingestion Techniques
Avro and Parquet Formats
Data Replication and Backup
Writing MapReduce Jobs
Mapper and Reducer Functions
Input and Output Formats
MapReduce Job Optimization
Data Processing with Pig and Hive
Introduction to Pig
Pig Latin Language
Data Processing with Pig
Introduction to Hive
HiveQL and Data Querying
HBase and NoSQL Databases
Introduction to HBase
HBase Data Model
HBase Shell and APIs
NoSQL Databases Overview
Cassandra and MongoDB
Data Analysis with Spark
Introduction to Apache Spark
Spark Architecture and Components
RDDs and DataFrames
SparkSQL and Data Analysis
Data Integration and ETL
Data Integration Tools (e.g., Talend, Pentaho)
Data Pipelines and Workflows
Data Quality and Cleansing
ETL Best Practices
Data Warehousing and Business Intelligence
Data Warehousing Concepts
ETL in Data Warehousing
OLAP and Data Cubes
BI Tools (e.g., Tableau, Power BI)
Data Governance and Security
Data Governance Frameworks
Data Privacy and Compliance
Data Security Best Practices
Access Control and Encryption
Auditing and Monitoring
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