Big Data Technology Fundamentals

Questions and Answers

Maximizing resource utilization and scalability

Enabling scalable and on-demand access to computing resources

Collecting and transporting data from various sources

Storing and managing large amounts of data Signup and view all the answers

Enabling easy scalability and deployment of big data clusters Signup and view all the answers

Reducing costs associated with hardware, maintenance, and personnel Signup and view all the answers

Batch or real-time data transport from sources to targets Signup and view all the answers

Supporting collaboration and data sharing across organizations and locations Signup and view all the answers

Distributed data storage Signup and view all the answers

Enabling scalable and efficient processing and analysis of large datasets Signup and view all the answers

Study Notes

Virtualization: a technology that enables multiple virtual machines (VMs) to run on a single physical machine, maximizing resource utilization and scalability.
Benefits in Big Data:
- Enables multiple big data nodes to run on a single physical machine, improving resource utilization and reducing hardware costs.
- Allows for easy scalability and deployment of big data clusters.

Cloud Computing: a model for delivering computing services over the internet, providing on-demand access to a shared pool of resources.
Benefits in Big Data:
- Enables scalable and on-demand access to computing resources, ideal for big data processing and analytics.
- Reduces costs associated with hardware, maintenance, and personnel.
- Supports collaboration and data sharing across organizations and locations.

Data Ingestion: the process of collecting and transporting data from various sources to a centralized location for processing and analysis.
Components:
- Data Ingestion Tools: Flume, Sqoop, Kafka, and NiFi.
- Data Transport: batch or real-time data transport from sources to targets.

Distributed File Systems: store and manage large amounts of data across a cluster of machines.
Components:
- HDFS (Hadoop Distributed File System): a widely used distributed file system for big data storage.
- NoSQL Databases: handle large amounts of unstructured or semi-structured data.

Data Processing: the process of transforming and analyzing data to extract insights and knowledge.
Components:
- MapReduce: a programming model for processing large datasets in parallel across a cluster of nodes.
- Spark: an in-memory processing engine for large-scale data processing.

Data Analytics: the process of extracting insights and knowledge from processed data.
Components:
- Data Warehousing: a centralized repository for storing and managing data for analytics.
- Business Intelligence Tools: provide data visualization, reporting, and analytics capabilities.