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Reply for below 2 posts. APA format and 100-150 words each discussion post

Discussion 1:

Big Data is a set of vast amounts of data, and it can be structured or unstructured form as per organizational process. There are different database techniques and software in the IT industry to handle the complex and massive database centers. For a complex database, there are several techniques to retrieve the data. However, at some point, its process with a higher fluctuate rate and accuracy of content was significant issues. Big data contain the massive lines of codes/information with a massive number of rows and columns that generate the complexity during the analysis of data. There are many aspects includes in Big data such as data storage, data visualization, data deduplication, database query processes, and many more. In the IT industry, Users consider three basic requirements (Basic Concepts) from database functionality such as Volume of Data, Type (Variety) of Data, and Velocity (Retrieval Rate) of overall data from the database centers.

Currently, every the organization needs an enormous amount of database space and faster retrieval rate, so for that, developers and data scientists are using the advanced techniques for the handling operation of big data. Different analysis methods include user-behavior analysis, artificial intelligence, and Cache memory techniques can be beneficial for database users. Data visualization tools can be helpful for the analysis of complex sets of data, but it requires appropriate input of process and advanced software tools to handle the operations.

Data Science can be helpful for the proper structuring of complex and large database sets; It is more helpful during the designing and the analyzation processes. Advanced algorithms and high intelligent software tools can improve the data retrieval rate and also perform the separation process among structured and unstructured processes. Data analytics is one efficient way to filter out the raw and corrupt data that is keeping a large amount of space in database centers. Data visualization tools can be use to represent the data, and after that, data analytics can help data scientists to eliminate the corrupted data sets from structured or unstructured data.


EMC Education Service (Eds). (2015). Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data. Indianapolis, IN: John Wiley & Sons, Inc.

Discussion 2:

Big Data can be defined as high volume of data sets which are growing with high velocities day by day. These are larger, complex data sets which can’t be handled by traditional relational databases. In real-time, these data increases from multiple source points and the businesses require these data to be processed for various situation analysis in environment. As these most of data can be unstructured and low-density data like audio, video, daily feeds, etc. put risk in the processing for businesses.

As mentioned above the data is required for the industries and organizations of all sectors for their business to lead with better decisions and strategic moves. In big data, 3 V’s are most important to be considered.

Volume: Data can be collected from various sources are hard for storing in the past, but now it eased through storages Data Lakes, Hadoop, Clouds.

Velocity: With increase of Internet availability and use of IoT devices, electronic sensors, smart meters, etc. the data stream speed also increased.

Variety: Different types of Data like structured, numeric data, unstructured text documents, emails, videos, audios, tweets, social media, stock ticker, video surveillance and financial transactions all encompass to Variety (Big Data Insights).

The importance of Bigdata techniques doesn’t rely on what data you have; it comes into to action when you want to use with it. When you combine High Powered Data Analytics, the data from any source can be analyzed which can lead to cost reductions, new strategic idea for a product development, Time saving, decision making etc. in any organization without any limitations.

For example, with use of data science analytics we can determine root cause analysis of failures, issues, fraudulent activity in near real-time before it affect the organization. If we take online shopping transactions, big data can be used to generate random coupons to user at point of sale, improve customer integrations with use of data from customers to improve marketing techniques from various data sources like social media, sensors, mobile devices, call log data etc.

In Supply Chain, Big data can improve efficiency of supply chain by analysis the shipment details before reaching destination by identifying inefficiencies and where time and costs can be saved. This will benefit the industry by analyzing bottlenecks.

In Medical, we can take an example of current ongoing pandemic Covid-19, where data from different sources can be put together like symptoms, race, severity of the patients affected and analyze the patterns of virus and study which can lead to success invention of vaccine.

In Banking, it can give banks on deep insights on customer spending patterns and habits.so that it helps bank how provide marketing campaigns for their customers, personalize product offers.

Finally, we can say Big data Analytics technology is changing at rapid pace and solving many challenges which were left unsolved from many years.


  1. Big Data Insights. https://www.sas.com/en_us/insights/big-data/what-is-big-data.html