Skip to main content Link Menu Expand (external link) Document Search Copy Copied

Database Workloads

  • Customer Relationship Management (CRM) databases,
  • Enterprise Resource Planning (ERP)

Workload types

Most modeling techniques focus on using the data and optimizing that usage, thereby optimizing the intended workload

OLTP
  • OLTP stands for Online Transaction Processing.
    • Old-fashioned mainframes where you didn’t interact with the system directly.
    • You kept track of changes during the day and processed all changes during the night in batches of work.
  • Transaction Processing of OLTP refer to the workload.
    • Two main characteristics
      • A lot of small queries are executed.
      • A lot of new rows are added to the database and existing rows need to be updated on a regular basis.
  • Databases that focus on OLTP workloads should be normalized.
OLAP
  • Online Analytical Processing
    • OLAP is about reporting and analysis.
      • Analytical processing means that we use the data for data analysis or analytics.
        • Perhaps we are trying to answer questions such as what type of customers buy what type of products?
    • Characteristics
      • Read only
      • Large Datasets
    • Databases that are OLAP-oriented often benefit from dimensional modeling.

More Workloads

Multiple database workload types refer to the various use cases and demands that different applications and systems place on databases. These workloads vary in terms of data processing requirements, access patterns, and performance expectations. While the user profile mentions being an Azure .NET Developer and Architect, understanding these workload types is relevant for designing and optimizing databases in the Azure cloud. Here are some common database workload types:

  1. ERP (Enterprise Resource Planning):
    • ERP systems manage core business processes, including finance, human resources, inventory, and supply chain management.
    • ERP databases often handle high transaction volumes, complex queries, and require data consistency and integrity.
  2. CRM (Customer Relationship Management):
    • CRM systems focus on managing interactions and relationships with customers, storing customer data, sales, and marketing information.
    • CRM databases need to provide quick access to customer data, support reporting and analytics, and enable personalized marketing campaigns.
  3. OLTP (Online Transaction Processing):
    • OLTP workloads involve a high volume of short, transactional database operations, such as inserting, updating, and deleting records.
    • These workloads require low-latency and high-concurrency database performance to support real-time transaction processing.
  4. OLAP (Online Analytical Processing):
    • OLAP workloads are centered around complex analytical queries and reporting on large datasets.
    • Data warehouses and analytical databases are optimized for OLAP workloads and often involve aggregations, joins, and data transformations.
  5. Data Warehousing:
    • Data warehousing workloads involve collecting, storing, and analyzing historical data for decision-making and reporting.
    • These databases are optimized for fast query performance and often use techniques like columnar storage.
  6. E-commerce:
    • E-commerce databases handle online shopping transactions, product catalogs, and inventory management.
    • They require high availability, scalability, and support for real-time inventory updates.
  7. Content Management:
    • Content management systems (CMS) store and manage digital content, including articles, images, videos, and user-generated content.
    • These databases need to handle content indexing, versioning, and efficient content retrieval.
  8. IoT (Internet of Things):
    • IoT databases handle massive streams of data generated by sensors and devices.
    • They need to ingest, process, and store large volumes of time-series data efficiently.
  9. Big Data and Analytics:
    • Big data workloads involve processing and analyzing massive datasets using technologies like Hadoop and Spark.
    • These databases require distributed and parallel processing capabilities.
  10. NoSQL Databases:
    • NoSQL databases cater to unstructured or semi-structured data and are used in various scenarios like social media, real-time analytics, and IoT.
    • Types of NoSQL databases include document-oriented, key-value, column-family, and graph databases.
  11. Search Engines:
    • Search engine databases enable full-text search and retrieval of information from large text datasets.
    • They are crucial for applications like search engines, e-commerce search, and document search.
  12. Gaming Databases:
    • Online games often require databases to manage player profiles, leaderboards, in-game items, and real-time game state.
    • Low-latency and high-concurrency support are essential for gaming databases.

Understanding these different workload types helps in selecting the appropriate database technologies and configurations, whether you are developing solutions as an Azure .NET Developer or architecting databases in Azure to meet specific application needs. Azure offers a range of database services and tools to address various workload requirements.