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Lesson 6Database and data management
ObjectiveDescribe the role and vendors of database and data management products in ecommerce.

Database and data management packaged software products

Database and data management packaged software products provide an engine for the storage, retrieval, and management of relationships between discrete information objects[1]. We use the term object somewhat loosely here, for in truth only object databases actually treat information as objects. Still, the general principal applies to all database and data management products, though the terminology of the information might involve items like rows, columns, records, and fields rather than objects.

DBMS Vendors

Data Management Standards

SQL, or structured query language, is the standard access language for relational databases. The world's most popular relational database, produced by Oracle, has a slightly modified version of SQL, known as PL/SQL.
ODBC[2] and JDBC[3] are middleware constructs that allow program objects and/or components to access databases regardless of the underlying physical implementation of the database. Database and data management can be further broken down into six subgroups.

A Comprehensive Overview of Databases: Exploring Six Fundamental Categories

In the intricate world of data management, databases emerge as the cornerstone, serving as structured repositories that store, organize, and facilitate data retrieval. The intricate landscape of databases can be classified into multiple categories, each with its distinct characteristics and applications. Here, we delve into six pivotal categories that define the modern database paradigm.
  1. Relational Databases: These databases use tables to store data and are based on the relational model, which employs a schema to define data relationships.
    • Characteristics:
      1. Data is stored in rows and columns within tables.
      2. Utilizes Structured Query Language (SQL) for querying.
      3. Enforces data integrity and consistency using primary and foreign keys.
    • Examples: Oracle, Microsoft SQL Server, MySQL, PostgreSQL.
  2. Amazon Relational Database Service (RDS): Amazon RDS is a cloud-based database service provided by Amazon Web Services (AWS) designed to simplify the setup, operation, and scaling of relational databases.
    • Characteristics:
      1. Automated backups, patch management, and failover to ensure availability and durability.
      2. Supports multiple database engines such as MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server.
      3. Scalable performance using instance resizing and read replicas.
  3. Object Databases: Unlike the table-based structure of relational databases, object databases store data in the form of objects, mirroring object-oriented programming.
    • Characteristics:
      1. Objects contain both data and methods to manipulate the data.
      2. Eliminates the need for object-relational mapping, offering a more direct representation of real-world entities.
      3. Highly efficient for applications with complex data structures.
    • Examples: ObjectDB, db4o, Versant Object Database.
  4. Analytical Databases: These databases are designed to support business analysis activities by providing a high-performance environment for querying large datasets.
    • Characteristics:
      1. Often utilize columnar storage for faster query performance.
      2. Built for complex queries and aggregations, rather than transactional operations.
      3. Can integrate with visualization tools for data representation.
    • Examples: Google BigQuery, SAP HANA, Teradata.
  5. Data Warehousing: A data warehouse is a specialized type of database optimized for the analysis and reporting of large volumes of data, often consolidated from various sources.
    • Characteristics:
      1. Employs a star or snowflake schema for data organization.
      2. Uses Extract, Transform, Load (ETL) processes to ingest data.
      3. Stores historical data to enable trend analysis over time.
    • Examples: Amazon Redshift, Snowflake, Microsoft Azure Synapse Analytics.
  6. Business Intelligence (BI): BI refers to technologies, practices, and tools that collect, integrate, analyze, and present business information to aid decision-making.
    • Characteristics:
      1. Utilizes data visualization tools like dashboards and reports.
      2. Employs data mining, online analytical processing, and querying.
      3. BI tools can source data from multiple databases and offer insights via a user-friendly interface.
    • Examples: Tableau, Microsoft Power BI, QlikView.
The multifaceted world of databases encompasses a spectrum of technologies and approaches, each tailored for specific needs and challenges. Understanding these categories is pivotal for any organization or individual aiming to leverage data effectively, ensuring that their data infrastructure aligns seamlessly with their operational objectives and analytical aspirations.
Note: Many enterprise applications vendors include their own Business Intelligence (BI) and data warehousing capabilities as add-ons to their core products. The problem with many of these add-on products, however, is that they only apply to the enterprise application, and are not designed to be used as general purpose data warehousing or business intelligence products.

[1]Discrete Information Objects: A chunk of information. For example, first name would be one discrete information object, while social security number would be another.
[2] Open Data Base Connectivity (ODBC): Inserts a middle layer, better known as a database driver, making it possible to access any data from any application, regardless of which database management system handles the data. For this to occur, the application must be capable of issuing ODBC commands and the database management system must be capable of responding to them.
[3] Java Database Connectivity (JDBC): Enables Java programs to interact with any SQL-compliant database and execute SQL statements. It is just like ODBC but is designed to work with Java.