Hadoop System Overview Quiz

Hadoop Overview

• HDFS (Hadoop Distributed File System) stores large data sets of structured and unstructured data across nodes, maintaining metadata in log files.
• SQL queries can be run on data stored in HDFS using tools like Hive or Spark SQL.
• YARN (Yet Another Resource Negotiator) manages resources across clusters, enhancing resource allocation and scheduling in Hadoop environments, implemented in Java.

Hadoop Frameworks

• Hadoop version 2 and above consists of three primary frameworks:
  • HDFS for data storage, serving as the default storage layer in Hadoop clusters.
  • YARN for resource management, facilitating efficient resource distribution.
  • MapReduce for bulk and batch data processing, utilizing distributed and parallel algorithms.

MapReduce Process

• Input files are split into multiple pieces for processing; the number of splits can be high in real scenarios.
• Multiple map programs process these splits in parallel, grouping data by specified criteria.
• The MapReduce system merges outputs from the map phase and prepares the data for the reduce phase, which performs calculations like summation.

Business Intelligence (BI)

• BI involves descriptive data analysis and informed business decision-making through specialized technologies and tools.
• The term "Big Data" refers to mammoth data volumes analyzed by organizations like Google and large research projects, defined by three "V" dimensions:
  • Volume: Data starts at terabyte scales with no upper limit.
  • Velocity: Speed of data production and processing must meet demand.
  • Variety: Diversity in sources, formats, and structures of data.

Handling Big Data

• Traditional RDBMS struggle with the "three V's" of big data.
• Data engineers utilize the Hadoop ecosystem to manage big data by breaking it into smaller, analyzable datasets.

Characteristics of Hadoop

• Open-source framework written in Java, designed for distributed processing of large datasets across clusters of commodity hardware.
• Supports parallel processing by distributing data across multiple nodes.
• Cluster architecture connects various machines via Local Area Network (LAN), leveraging cost-effective, lower-performance hardware.

Tools in Hadoop Ecosystem

• Tableau for creating interactive visualizations.
• WEKA for data mining and machine learning.
• Other tools include Excel, Apache Spark, BigML, D3.js, SAS, Jupyter.

Data Scientist Profile

• Data scientists extract insights, identify patterns, and employ skills across several domains including mathematics, statistics, data engineering, and domain expertise.

Concentrations in Data Science

• Focus areas include Mathematics, Applied Statistics, Programming (R, Python, SQL), Data Mining, and Data Storage Management.

Data Science Project Lifecycle

• Steps to solve data science problems include:
  • Defining the problem statement/business requirement.
  • Collecting, cleaning, and exploring data.
  • Data modeling and final deployment with optimization.

Data Science Applications

• Applications of data science encompass:
  • Machine learning for energy optimization.
  • Strategies for achieving business and scientific goals.
  • Disease prediction and identification.
  • Intrusion detection systems, fraud detection, image/speech recognition, airline route planning, healthcare recommendations.

Roles Comparison in Data Science

• Data Scientist: Builds models and makes relevant data-driven suggestions.
• Data Engineer: Prepares and manages data to ensure it is consumable for analysis.
• Data Analyst: Interprets current data to provide actionable insights for business strategic decisions.

Test your understanding of the Hadoop system, including HDFS and YARN functionalities. This quiz covers the core frameworks and technologies used in managing large data sets across nodes. Dive into the specifics of SQL queries, resource management, and implementation in Java.