A-Z of Data

Explaining the fundamentals of generative AI, large language models, and their applications in business settings. The text focuses on the history, technology, and use cases of LLMs, emphasizing their potential to revolutionize enterprise data management and analysis. It highlights the importance of data governance and security in these contexts, recommending the use of cloud data platforms to prov...

Data mining, why is it important and various concepts, including data preprocessing, data warehousing, OLAP, association rule mining, classification, prediction, cluster analysis, and graph mining. It also discusses the applications of data mining in different domains, such as business, finance, healthcare, and intrusion detection.

Comprehensively exploring various aspects of data analytics. The text covers topics such as data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. The book emphasizes practical applications and utilizes real-world examples to illustrate different data analytics techniques. It incorporates a range of models and algorithms, including statistical meth...

Master Data Management (MDM) and Customer Data Integration (CDI) in a global enterprise setting. The book explores the challenges and opportunities of integrating customer data across various systems within an organization, covering topics such as data governance, data quality, security, and compliance. It also discusses the architecture, design, and implementation of CDI solutions, including Data...

Data architectures , which are the blueprints for how businesses manage their data. It specifically covers popular data architectures, including the  relational data warehouse ,  data lake ,  modern data warehouse ,  data fabric ,  data lakehouse , and  data mesh .

Data Management Body of Knowledge (DMBOK) 2.0, a guide for data management professionals. It  explains  the  principles  and  best practices  for  managing  and  governing  data within organizations. The text  covers  various aspects of data management, including  data governance ,  data quality ,  data security ,  data integration ,  data warehousing , and  data modeling . It also  discusses  the...