Video description
This video helps you to design databases and processes that can manage millions or billions (or more) rows of data for analytics and reporting. Analytic workloads follow different architectural and query patterns than standard transactional systems, and the associated data stores grow differently over time as compared to online transaction-processing systems. This video covers the purpose, usage, and effects of growth on analytic data stores with a focus on storing the data directly in SQL Server.
Topics covered in this video include design issues such as compression and advanced indexing techniques for online analytical processing (OLAP) data. Also covered are performance optimization and efficient query patterns. Watching this video helps you learn to better retrieve data with the ultimate goal of answering organizational questions using your analytics data store.
What You Will Learn
- Understand the nature of OLAP data, its structure, and its usage
- Store OLAP data natively in SQL Server using best-practice options
- Minimize resource consumption by compressing your data
- Enhance query performance through columnstore indexing
- Scale your OLAP query performance to account for future growth
- Add new data elements to existing analytic data stores
Who This Video Is For
For data and analytics professionals who create, manage, and query large analytic data stores. For database professionals who expect to do so in the future, who want to know the options that are available in Microsoft SQL Server.
Table of contents
- Overview
- Introduction to Analytic Data
- OLAP vs. OLTP - How Do They Differ
- Assigning Business Goals and Meaning to Data
- Classifying Data - Dimensions vs. Facts
- Optimizing Data Load Processes
- Indexing - B-Tree Versus Columnstore Indexes
- ETL, Validation, and Analyzing Data Sources
- Caching Challenging Metrics for Quick Retrieval
- Scaling Analytic Data
- Conclusion
Product information
- Title: Performance Optimization for OLAP Workloads in SQL Server: Designing Efficient Processes for Collecting and Managing Large Analytic Data Sets
- Author(s):
- Release date: February 2021
- Publisher(s): Apress
- ISBN: 9781484270004
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