OLTP and Data Mart Concepts

Quick, real-time transactional tasks

To store condensed, subject-oriented data for easy access and analysis

To query and analyze multidimensional data from data warehouses

<p>Organizing data into predefined categories based on input features</p> Signup and view all the answers

<p>Identifying unusual patterns or outliers in the data</p> Signup and view all the answers

<ol> <li>Data Integration: It consolidates data from multiple sources into a single, coherent repository, providing a unified view of information across the organization. 2. Improved Decision Making: By providing easy access to comprehensive &amp; reliable data, data warehouses enable organizations to make informed decisions based on accurate insights and analysis.</li> </ol> Signup and view all the answers

<ol> <li>Spark Core: Provides basic functionality for distributed task scheduling, memory management, &amp; fault recovery. 2. Spark SQL: Allows working with structured data using SQL queries, and integrates SQL queries with Spark programs. 3. Spark Streaming: Enables processing real-time streaming data in micro-batches or real-time modes. 4. MLlib (Machine Learning Library): Provides scalable machine learning algorithms for classification, regression, clustering, etc. 5. GraphX: A graph processing library for analyzing graph-structured data.</li> </ol> Signup and view all the answers

<ol> <li>Data Quality: Relies heavily on the quality &amp; accuracy of data. 2. Privacy Concerns: Raises ethical &amp; privacy issues related to the use of personal data.</li> </ol> Signup and view all the answers

<p>To uncover valuable insights &amp; patterns hidden within data.</p> Signup and view all the answers

<p>It provides organizations with a competitive edge by identifying trends and opportunities.</p> Signup and view all the answers