IT301 1st Term Final Coverage-4-5.pdf

Other Information in the PPT: In the context of Big Data, "Variety" refers to the diversity of data types that need to be managed Relational databases organize data in...

Other Information in the PPT: In the context of Big Data, "Variety" refers to the diversity of data types that need to be managed Relational databases organize data in and analyzed. structured tables with fixed schemas, while NoSQL databases, such as document-based, Key-Value Store NoSQL databases organize data graph-based, and key-value stores, offer more in pairs, allowing for quick retrieval of values flexible data storage options without strict based on their associated keys. structure. While OLTP systems manage daily transactional Business Intelligence (BI) encompasses the data, they are not typically part of a data tools and techniques used to turn data into warehouse architecture, which focuses on actionable insights, helping organizations make analytical processing. informed decisions. OLAP is designed for analytical purposes, A Data Warehouse consolidates data from allowing users to perform multidimensional different sources, allowing for complex queries analysis of business data. and reporting to aid in business analytics. Business Intelligence is not focused on This characteristic defines a Data Warehouse, transactional data entry, but rather on analyzing as it holds historical data that remains unchanged and interpreting data for insights. and is organized around specific subjects to Document-based NoSQL databases are facilitate analysis. specifically designed to handle unstructured data Data mining involves analyzing vast amounts of in the form of documents. data to uncover patterns, trends, and insights that One of the significant challenges associated with can inform business decisions. Big Data is the complexity of processing and This technique is commonly used in data mining gaining insights from large datasets. to understand relationships between variables A Data Mart is used to meet the specific and make predictions based on historical data. analytical needs of a particular business unit. ETL is a crucial process in data warehousing that The primary goal of data mining is to analyze involves extracting data from various sources, data to uncover trends that may not be transforming it into a usable format, and loading immediately visible. it into a data warehouse for analysis. This term describes how a Data Warehouse A data warehouse consolidates data from maintains historical data that changes over time multiple sources, enhancing the ability to perform to reflect past states of the data. comprehensive analysis and generate reports. NoSQL databases are often chosen for Big Data Business Intelligence tools help organizations applications because they can easily scale to visualize data and derive insights that support accommodate growing data volumes and diverse strategic decisions. data types. MongoDB is an example of a NoSQL database Tableau is a popular Business Intelligence tool that stores data in a flexible, document-oriented used for data visualization, enabling users to format, differing from traditional relational create interactive and shareable dashboards. databases. In the context of Big Data, "Velocity" refers to Hadoop enables the distributed storage and the rapid pace at which new data is created and processing of large datasets across clusters of needs to be analyzed. computers. "Schema-less" in NoSQL databases allows for The primary purpose of data mining is to identify easy modification of data structures without the trends and correlations that can inform business need for extensive reconfiguration. strategies and decisions.  The primary purpose of a Data Warehouse is to These tools facilitate data visualization, making provide a centralized repository for data that can it easier to interpret sales performance data. be analyzed for insights. Use a combination of document-based, key- This type of database is best suited for handling value, and graph databases based on the data large volumes of unstructured data, such as text type and access patterns. or multimedia content. This approach leverages the strengths of various Denormalization in a data warehouse design NoSQL database types for diverse datasets. helps optimize data retrieval for reporting Design the system to route transactional data to purposes by reducing the complexity of joins. relational databases and analytical data to NoSQL These techniques are commonly used in data databases, ensuring proper integration between mining to identify patterns in customer them. purchasing behavior. This hybrid approach optimizes both This sequence describes the ETL process for transactional and analytical workloads. integrating data from various sources into a data Technologies like Apache Kafka, Apache Storm, warehouse. or Apache Flink, and integrate with a data lake or These techniques help improve query distributed file system. performance by optimizing how data is accessed This setup enables efficient real-time analytics in and organized in a data warehouse. a Big Data environment. Graph databases are specifically designed to A comprehensive data mining project plan manage and analyze relationships between users, must cover all essential stages to ensure making them suitable for social media platforms. successful analysis. These Big Data technologies are ideal for Effective integration is crucial for leveraging BI implementing real-time analytics and processing tools to provide meaningful data visualizations. of large datasets. Use data lakes for storing raw, unstructured These data mining techniques can help predict data and data warehouses for structured, future sales based on historical data patterns. processed data, based on the company’s need for These methods can help optimize performance flexibility and processing power. in a data warehouse that struggles with large This recommendation provides the best of both volumes of data. worlds in data storage and analysis Hadoop and HDFS are designed to handle the storage and processing of large datasets, making them suitable for managing log data in real-time. Use clustering and classification techniques. These data mining methods can effectively segment a customer base for targeted marketing strategies. This approach helps improve business decision- making by offering a holistic view of the data. These techniques help distribute data across multiple servers and optimize data retrieval, addressing performance bottlenecks in NoSQL databases. BI tools like Tableau or Power BI to create interactive dashboards and reports.