Llbl 564 Quiz - 2

Big Data environments typically handle large volumes of data and require fast processing speeds. In order to efficiently process and analyze this data, a high input-output operations per second (IOPS) performance is necessary. This allows for quick data retrieval and processing, enabling organizations to derive valuable insights from their Big Data.

RAM, or Random Access Memory, is largely used in in-memory processing. This is because RAM is a type of computer memory that allows data to be read from and written to quickly. In in-memory processing, data is stored and processed directly in RAM, which enables faster and more efficient operations compared to accessing data from other storage devices such as ROM or Hard Disk. The processor, on the other hand, is responsible for executing instructions and performing calculations, but it does not store data like RAM does.

Google Refine is a tool set that can work with messy data, cleaning them up and transforming them into different formats for analytics. It provides various functionalities such as data cleaning, data transformation, and data exploration, which make it suitable for handling messy data. With Google Refine, users can easily clean up inconsistent data, remove duplicates, and transform data into a format that is suitable for further analysis. Therefore, Google Refine is the correct answer for this question.

All of these options contribute to making security manageable and omnipresent. Planning helps in creating strategies and implementing security measures. Logic helps in analyzing potential risks and finding effective solutions. Observation helps in monitoring and identifying any security breaches or vulnerabilities. Therefore, all of these factors are necessary for ensuring comprehensive security.

The scale of big data directly affects the storage platform because as the amount of data increases, more storage space is required to store and manage the data effectively. Big data refers to extremely large and complex datasets that cannot be easily managed using traditional storage methods. Therefore, a scalable and robust storage platform is necessary to accommodate the growing volume of data and ensure efficient data storage and retrieval.

Distributed systems allow analysis to occur where data resides by distributing the processing and storage of data across multiple computers or servers. This enables data to be analyzed in parallel, reducing the need for data movement and improving performance. In contrast, centralized systems typically require data to be transferred to a central location for analysis, which can be time-consuming and inefficient. Desktop systems and in-memory systems may offer local analysis capabilities, but they are limited in scale and may not be able to handle large datasets or complex analyses.

Redis is a popular open-source, in-memory data structure store that can be used as a database, cache, and message broker. It is often categorized as a NoSQL database because it does not use traditional relational database structures. Redis supports a wide range of data types and provides high-performance, scalability, and flexibility, making it a suitable choice for NoSQL applications. On the other hand, Kafka is a distributed streaming platform, S3 is an object storage service, and Datameer is a big data analytics tool, none of which are typically classified as NoSQL databases.

One of the biggest challenges for most organizations is finding data sources. This refers to the difficulty organizations face in locating and accessing relevant and reliable sources of data. Without access to appropriate data sources, organizations may struggle to gather the necessary information for decision-making, analysis, and other business processes. This challenge can hinder the organization's ability to effectively utilize data-driven strategies and make informed decisions.

All of these options, namely Hadoop, Cassandra, and Mongo DB, do not incorporate sufficient data security tools. This implies that none of these technologies provide adequate measures for ensuring the security of data.

Internal data refers to the data that is generated and collected within an organization. This can include information such as sales records, customer data, employee information, and financial data. Internal data is typically proprietary and specific to the organization, making it a reliable and valuable source of information for decision-making and analysis. Therefore, internal data can be considered as a source of data among the given options.