May 11, 2024
3 minute read
On-premises data refers to data that is stored and managed within an organization's own infrastructure, rather than being stored in the cloud or on a third-party server. This type of data is often considered to be more secure and reliable than cloud-based data, as it is not subject to the same risks of data breaches or outages. On-premises data can be stored on a variety of devices, including servers, storage area networks (SANs), and network-attached storage (NAS) devices.
Benefits of On-premises Data
There are many benefits to storing data on-premises, including:
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Find a path to becoming a On-premises Data. Learn more at:
OpenCourser.com/topic/gt5mlp/on
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
On-premises Data.
Covers big data analytics which includes on-premises data. The author has received various awards, including awards from the INFORMS society.
Data mining is discussed in this book with an emphasis on concepts and techniques. The authors are well-known researchers in the field of data mining.
Data science is discussed in this book with an emphasis on data-analytic thinking. The author well-known researcher in the field of data science.
Machine learning is covered in this book with a focus on using popular libraries such as Scikit-Learn, Keras, and TensorFlow. The author recognized expert in the field of machine learning.
Goes over data governance and its importance alongside on-premises data. The author recognized expert in the field of data management.
Discusses Apache Spark which is widely used for data processing, including working with on-premises data. It goes over topics such as programming with Spark, streaming data, and machine learning.
Covers data warehousing which can work with on-premises data. Goes over various concepts such as dimensional modeling, data integration, and data quality. The author has extensive experience in data warehousing and big data.
Goes over Apache Hadoop which is commonly used for working with on-premises data. It covers topics such as data storage, data processing, and data analysis.
An introduction to the topic of on-premises data management which includes writing schemas and performing table management. It also goes over data architecture for on-premises data and various tools.
Machine learning is covered in this book. The authors go over various topics such as supervised learning, unsupervised learning, and reinforcement learning.
Data integration which works with on-premises data is discussed in this book. It covers topics such as data quality, data governance, and data security.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/gt5mlp/on
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