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Data provenance is the record of the source and history of data, which is important for validating the authenticity and integrity of the data. The cloud can assist by offering tracing and logging services that help maintain the lineage of big data.

Cloud storage solutions offer services to store and manage data on the internet. With respect to big data, these solutions provide a scalable, accessible, and secure environment for storing vast amounts of data.

Batch processing refers to processing large volumes of data at once, usually at a scheduled time. Cloud computing facilitates big data batch processing by providing large quantities of compute resources that can scale to the batch size as needed.

A Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. In the cloud, data lakes enable businesses to store vast amounts of data without the limitations of on-premise hardware.

Machine learning involves algorithms that can learn from data. Using cloud computing, big data can be efficiently used to train machine learning models due to the virtually unlimited computational resources and storage available.

Big Data refers to the large volumes of data, both structured and unstructured, that inundate a business on a day-to-day basis. Its cloud relevance lies in the fact that cloud platforms provide the infrastructure and tools to store, process, and analyze big data effectively and cost-efficiently.

A data warehouse is a system used for reporting and data analysis, and is considered a core component of business intelligence. Cloud-based data warehouses offer on-demand scalability and minimized overhead, providing a cost-effective solution for big data analytics.

Cloud disaster recovery involves using cloud resources to protect applications and data from disruption caused by disaster. For big data, the cloud provides replicated storage and fast recovery capabilities that ensure minimal downtime and data loss.

Cloud analytics refers to the use of cloud computing to perform data analysis. For big data analytics, the cloud offers a suite of tools and services that make it easier to handle vast datasets and complex computational analysis with greater speed and efficiency.

Data integration involves combining data residing in different sources and providing users with a unified view. The cloud facilitates big data integration through services that support various data sources and allow for seamless data movement and transformation.

Data privacy is about proper handling of data – consent, notice, and regulatory obligations regarding the data. Cloud providers play a critical role in ensuring data privacy when dealing with big data by offering services that help comply with laws and regulations.

Cloud governance is a set of rules and procedures that organizations follow to ensure compliance and manage risks in their cloud environments. With big data, governance is key to managing data access, cost control, and maintaining data policy compliance.

MapReduce is a programming model for processing large data sets with a distributed algorithm on a cluster. Cloud relevance comes from its ability to scale up for big data processing across many cloud instances.

Data visualization is the representation of data in a graphical format. Cloud computing supports big data visualization by offering services that can process large data sets and convert them into visual insights accessible from anywhere.

Elasticity is the ability of a system to grow and shrink dynamically in resources. In the context of big data, cloud computing ensures elasticity by automatically scaling computing resources to the demands of the data being processed.

NoSQL databases are designed to handle a variety of data models, including key-value, document, columnar, and graph formats. They are significant in the cloud for providing high-performance, scalable, and flexible data storage solutions for big data.

Data mining is the process of discovering patterns and knowledge from large amounts of data. The cloud aids in data mining by providing the massive processing power and storage necessary to analyze big datasets in an efficient manner.

Predictive analytics involves using data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Cloud computing supports predictive analytics on big data by providing the necessary computational power and advanced analysis tools.

Data security involves protecting digital data from unauthorized access. Cloud computing impacts this by offering advanced security features that can be more robust and cost-effective than on-premise solutions, which is paramount when handling big data.

There are three primary cloud service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Each plays a unique role in big data by providing different levels of control, management, and scalability.

Real-time processing is the capability to process data immediately as it becomes available. In the cloud, big data can be processed in real time using services designed for high throughput and low-latency, enabling immediate insights and action.

Hadoop is an open-source framework used for distributed storage and processing of big data sets using the MapReduce programming model. Cloud platforms can host Hadoop clusters, offering scalability and reducing the need for physical infrastructure.

Data analytics refers to the process of analyzing raw data to make conclusions. Cloud computing enables data analytics at a large scale by allowing access to powerful analytics tools and computing resources, often in a pay-as-you-go model.

Distributed computing involves a group of computers working together as a system to tackle a large computational task. Cloud platforms inherently support distributed computing, making them suitable for processing big data tasks across multiple cloud servers.

Data processing is the conversion of data into usable and desired form. For big data, cloud computing provides a wealth of on-demand data processing services capable of handling large-scale data sets with several computational paradigms.

IoT involves a network of physical devices, vehicles, home appliances, and other items embedded with sensors and software for connectivity. The cloud's role is to store, analyze, and manage the vast amounts of data generated by IoT devices efficiently.

Cloud-native refers to technologies that are designed to thrive in a cloud environment. For big data, cloud-native technologies include services like containers and microservices which can process data efficiently in a distributed and agile way.

Hybrid cloud combines on-premises infrastructure, or private clouds, with public clouds, allowing data and applications to be shared between them. For big data, this offers flexibility by using cloud bursting to handle peaks and providing a balance between cost and performance.

Scalability refers to the capacity to be changed in size or scale. Cloud computing offers scalability for big data by allowing systems to easily expand and handle increasing data loads without the constraints of physical hardware.