1. Knowledge is defined as: a. raw facts and figures.

The Raw Facts and Figures are Called Data. The word raw means that the facts have not yet been processed to get their exact meaning. Data is collected from different sources. Data may consist of numbers, characters, symbols or pictures etc.

Data are facts or figures in raw form.

Data are raw facts. The word raw indicates that the facts have not yet been processed to reveal their meaning. Information is the result of processing raw data to reveal its meaning. Data constitute the building blocks of information.

Data is the result of processing raw facts to reveal it’s meaning.

Raw data is also known as eggy data or the sourcey data which means that the data is left unprocessed.

Raw data can be used as source data for an anti-fraud algorithm. For example, timestamp or amount of cookie occurrences or analysis of data points can be used within the scoring system to detect fraud or to make sure that a message receiver is not a bot (so-called Non-Human Traffic).

Which of the following is characteristic of Raw Data? Explanation: Raw data is data that has not been processed for use.

Answer. Answer: Raw data is a set of information that was delivered from a certain data entity to the data provider and hasn’t been processed yet by machine nor human. This information is gathered out of online sources to deliver deep insight into users’ online behavior.

Raw data that has undergone processing are sometimes referred to as “cooked” data in a colloquial sense. Although raw data has the potential to be transformed into “information,” extraction, organization, analysis, and formatting for presentation are required before raw data can be transformed into usable information.

Raw data refers to data that have not been changed since acquisition. Editing, cleaning or modifying the raw data results in processed data. For example, raw multibeam data files can be processed to remove outliers and to correct sound velocity errors.

The four main stages of data processing cycle are:

• Data collection.
• Data input.
• Data processing.
• Data output.

Six stages of data processing

1. Data collection. Collecting data is the first step in data processing.
2. Data preparation. Once the data is collected, it then enters the data preparation stage.
3. Data input.
4. Processing.
5. Data output/interpretation.
6. Data storage.

Answer: Process data is called information. The data that is processed is known as information.

Data processing is a series of operations that use information to produce a result. Common data processing operations include validation, sorting, classification, calculation, interpretation, organization and transformation of data. The following are illustrative examples of data processing.

A data processing unit (DPU) is a programmable specialized electronic circuit with hardware acceleration of data processing for data-centric computing. The data is transmitted to and from the component as multiplexed packets of information. A DPU generally contains a CPU, NIC and programmable data acceleration engines.

There are data quality characteristics of which you should be aware. There are five traits that you’ll find within data quality: accuracy, completeness, reliability, relevance, and timeliness – read on to learn more.

The seven characteristics that define data quality are:

• Accuracy and Precision.
• Legitimacy and Validity.
• Reliability and Consistency.
• Timeliness and Relevance.
• Completeness and Comprehensiveness.
• Availability and Accessibility.
• Granularity and Uniqueness.

Following are the some of the characteristics of Real-time System:

• Time Constraints: Time constraints related with real-time systems simply means that time interval allotted for the response of the ongoing program.
• Correctness:
• Embedded:
• Safety:
• Concurrency:
• Distributed:
• Stability:

Real time processing requires a continual input, constant processing, and steady output of data. A great example of real-time processing is data streaming, radar systems, customer service systems, and bank ATMs, where immediate processing is crucial to make the system work properly.

Correct Answer:

• Data is processed periodically.
• Low latency is expected.
• High latency is acceptable.
• Data is processed as it is created.

In real-time processing, information is always up to date. Hence, it makes the organization able to take immediate action. Also, when responding to an event, issue or scenario in the shortest possible span of time. It also makes the organization able to gain insights from the updated data.

5 Real-time Streaming Platforms for Big Data

• Apache Flink. Flink is an open-source streaming platform capable of running near real-time, fault tolerate processing pipelines, scalable to millions of events per second.
• Apache Spark.
• Apache Storm.
• Apache Samza.
• Amazon Kinesis.

Real-time data processing is the execution of data in a short time period, providing near-instantaneous output. The processing is done as the data is inputted, so it needs a continuous stream of input data in order to provide a continuous output. Real-time data processing is also known as stream processing.

Discussion Forum

7 Essential Elements in a Real-Time Streaming Analytics Platform

• Introduction.
• What must it do?
• Open source.
• Future-proof.
• Low latency.
• Data integration with Lambda architecture.
• Rapid application development.
• Linear scale out.

The distinction between “near real-time” and “real-time” varies, and the delay is dependent on the type and speed of the transmission. The delay in near real-time is typically in a range of 1-10 seconds.

One distinction that could be made for other areas of usage might be this: “live” can refer to a recorded live performance–i.e., a “live” version of a song in concert, verses the studio-recorded version; “real-time” can only mean one thing–that you are watching or listening as it happens.