DSC652 Big Data Applications And Issues UITM Assignment Example Malaysia
Big Data is a hot topic in today’s world. This course will teach you about the essentials of Big Data Applications and give insight on how it can be applied for various purposes, including increased productivity or scientific breakthroughs!
You’ll also learn state-of -the art techniques that are being adopted by professionals across industries who want more out their work than just basic information gathering; we’re going all out when it comes to providing our students with real life skills they’ll use every day after taking this class ̶ no matter what field(or fields) they may want to specialize in!
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Assignment Brief 1: Demonstrate an understanding on various big data tools, applications and implementation techniques
There are a number of different big data tools, applications, and implementations. Here are just a few examples:
- Hadoop is a popular big data tool that enables organisations to store and process large datasets in-house. Hadoop is often used for tasks such as data analysis, machine learning, and predictive modeling.
- Spark is another popular big data tool that offers powerful processing capabilities for real-time streaming data. Spark can be run on top of Hadoop or on its own, and is often used for tasks such as fraud detection, recommendations systems, and structured data processing.
- Tableau is a popular big data visualization tool that allows users to create interactive visualizations from large datasets. Tableau enables people from all backgrounds to easily build and share data analytics.
- SAS is a big data-friendly analytics software platform that can be used to analyse large datasets, create dashboards, and perform predictive modeling work. In addition to SAS’s own software packages, it also integrates with a number of other popular big data tools such as MongoDB, Cassandra, and Hadoop.
- Pentaho is another popular big data analytics platform that integrates with a variety of big data tools such as MongoDB, Hadoop, and Cloudera. Pentaho can also be extended through open source modules that add additional functionality to the core product.
Assignment Brief 2: Display social responsibilities in carrying out tasks related to big data application and issues
There is a lot of discussion these days about the role of businesses in society and the responsibilities they have to act ethically and with social responsibility. This is particularly relevant when it comes to big data, as the use of big data can have a profound impact on people’s lives.
When it comes to using big data for marketing or other purposes, businesses need to be aware of the potential implications of their actions and take into account how their decisions might affect people’s lives. They also need to be transparent about how they are using big data and what they are doing with it.
Businesses should also consider the ethical implications of collecting and using personal data, and should make sure that they are getting consent from people before they use their data.
Here are a few of the key issues related to big data and responsibility:
1) Are businesses taking full responsibility for the personal data they hold?
Businesses have a legal responsibility to ensure that they process people’s personal data in accordance with the law, so one important point is whether businesses have taken appropriate steps to protect people’s data once they have it.
This means that businesses need to make sure that they are prepared for a security breach, which might happen as the result of human error or an external attack. It is not enough simply to put measures in place at the beginning, businesses also need to be able to deal with any breaches appropriately when they occur.
2) Are businesses using people’s personal data in an ethical way?
Businesses also need to ensure that they only use the personal data of individuals when they have a legitimate reason for doing so, and that they are handling the information fairly. Businesses should be transparent about what they are doing with people’s information, and tell them why they are collecting it and how they will use it. Businesses should not mislead people about why they are collecting data or the possible consequences of supplying their personal information.
In addition, businesses need to make sure that they do not discriminate against people when they process their personal information – whether this is by denying them access to certain services, charging them more for products, or not hiring them for a position.
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Assignment Brief 3: Demonstrate practical skills in completing task related to big data application.
The following are some tips for completing a big data application:
- Choose the right tool for the job. There is a wide range of big data tools available, so be sure to select one that is best suited to the task at hand.
- Collect relevant data. Be sure to gather all of the data that is needed in order to complete the application.
- Store data in a structured format. This will make it easier to work with the data later on.
- Use parallel processing for faster results. Many big data applications can take advantage of parallel processing in order to speed up the execution time.
- Test and debug your code thoroughly. Bugs can cause serious problems in big data applications, so it is important to eliminate them as soon as they are found.
- Use a tool like Hadoop if necessary. This open-source technology will allow you to add data nodes and processing nodes easily without affecting production workloads on existing nodes.
- Verify end results with quality assurance tools. As the saying goes, “Trust, but verify.”
- Utilize cloud computing if possible. This will allow you to take advantage of on-demand resources without having to invest in extra hardware or software.
- Scale as necessary, but judiciously. The wrong type of scaling can have a negative impact on performance and cost, so be sure to gauge each situation to determine the right course of action.
- Administer, monitor and maintain your big data application properly. Without good administration, tools like Hadoop can become a “wild west” environment where anything goes!
These are just some suggestions for tackling large-scale data analysis projects. It should be noted that the points listed above are not necessarily listed in order of importance. Each developer will need to analyze the requirements of a given application before developing a strategy for success.
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