Data is unorganized information in raw form. We are currently in the era of Big Data, i.e. large data sets that can be characterized by large volume, complexity, variety, speed, resolution, flexibility, etc. This data cannot be managed with traditional software systems, but with modern frameworks capable of managing huge volumes, complexity and discovering which data is useful. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get Original Essay This paper discusses all the various challenges and problems that we face while mining big data. We also present various technologies and tools that can be used to overcome such problems. We live in the era where everything around is in digital form. Data is everywhere and in huge quantities. Big Data is nothing but large sets of these, of which some are useful, known as information, and the rest is waste. It is this information we need that will help analyze current trends that will help industries make strategic decisions based on them. Some even argue that Big Data is the current fuel for future economic infrastructure. Big Data in a simple perspective is a link that connects the physical world, human society and cyberspace. Data can be found in various forms, such as structured, semi-structured, and unstructured formats. We need new, advanced forms of tools and technologies that can handle complexity and process high volumes of data at high speed. In the future economy, labor productivity will not be a crucial factor in deciding how the economy will shape itself. itself but instead the efficiency of the technologies that will be able to manage Big Data and the fact that they are an inexhaustible resource, will have a greater role in deciding the direction in which the economy will go. Review of large data sets useful for studying trends, patterns and associations are called Big Data. The quantity of data is not as important as the quality because having tons of data on waste does not help in economic decisions. The main purpose behind collecting and analyzing data is to gain valuable insights. Analysts have provided the “Three Vs” to describe Big Data: Volume: Business organizations collect data from every source, be it devices, social media, or transactions, and data is collected in huge quantities due to countless sources from which data can be extracted. Speed: Data is collected from every corner of the world in a trillionth of a second and that too in huge quantities because billions of people access devices around the world that constantly monitor activities, also known as data mining. Variety: The collected data does not have a fixed format or structure, it can be found in any digital format we know, such as audio, documents, videos, financial transactions, emails etc. With this article, we are trying to focus on the challenges and problems faced when managing such a complex set of information and the solutions such as an advanced framework for tools and technologies to process it at high speed and capable of handling huge quantities of it. We will now focus on the various challenges we face while managing Big Data. As we all know, whenever we are provided with opportunities we always encounter some kind of challenge or obstacle when trying to make the most of the opportunity provided to us. This is the case with Big Data: being such an immensely powerful resource, it comes with its own specific set of challenges. There are many issues like computational complexity, data securityacquired, the mathematical and statistical methods required to handle such large data sets. We will be able to discuss the various challenges and possible solutions one by one. With these approaches, companies can address the problem of big data volume by reducing its size or downsizing. Investing in good infrastructure all depends on the company's cost and budget requirements. Combining multiple datasets: We don't always get the data in the right form, we get it in raw form from all web pages, social media, emails, streams, etc. Data complexity increases with the increase in various data types and formats. Possible Solutions: OLAP Tools (Online Analytical Processing Tools) – OLAP is one of the best tools when dealing with various types of data, it assembles data in a logical manner for easy access. Establishes the connection between information. But it processes all the data, no matter whether it is useful or not, this is one of the disadvantages of OLAPApache tools HADOOP – It is an open source software and its main task is to process huge amount of data by dividing it into different segments and distribute it send it to different infrastructures system to process it. HADOOP creates a map of the content so that it can be easily accessed. SAP HANA – HANA is another great tool that can be deployed as a local application or even used in cloud systems. It can be used to perform real-time analytics and develop and deploy real-time applications. While these approaches are revolutionary in themselves, they are not big enough to solve the variety problem on their own. HANA is the only tool that allows users to process data in real time. Meanwhile, HADOOP is great for scalability and cost-effectiveness. By combining them together, scientists can create the most powerful big data solution. Volume – First and foremost, the biggest and most basic obstacle we face when dealing with large data sets is always quantity or volume. In this age of technological advancements, the volume of data is exploding. Every year it will grow exponentially. Many analysts have predicted that the data volume will surpass Zetabytes by 2020. Social media is one such source where they collect data from devices such as mobile phones. Possible Solutions: HADOOP – There are various tools currently available such as “HADOOP ” which is a great tool when it comes to managing large amounts of data. But since it is a new technology and not many professionals don't know about it, it is not that popular. But the downside of this is that it takes a lot of resources to learn and may ultimately divert attention from the main problem. Robust hardware: Another way is to improve the hardware that processes the data, for example by increasing the parallel processing capacity or increasing memory size to handle such a large volume, one of the examples is Grid Computing, represented by a large number of servers interconnected to each other using a high-speed network. Spark: This platform uses the plus-in memory computing method to create huge performance improvements in diversified data and high volumes.Velocity Challenge – Real-time data processing is a real obstacle when it comes to big data. Furthermore, data flows at an incredible speed, which challenges us on how we respond to the flow of data and how to manage it. Possible solutions: Flash Memory – In dynamic solutions, where we need to differentiate data between hot (or highly accessed data) or cold (rarely accessed data), we need high-speed flash memory in order to provide a cache area. Hybrid cloud model – This model proposes the idea of ​​expanding the private cloudin a hybrid model that enables additional computing power needed to analyze data and to select hardware, software and business process changes to handle high-paced data needs. Sampling data: Statistical analysis techniques are used to select, manipulate, and examine data to recognize patterns. There are many tools that use cloud computing to access data at high speeds and also help reduce IT support costs. With one of these Hybrid SaaS, known as Software as a Service, is a web browser client that allows instant customization and promotes collaboration. It is used in hybrid mode because only with SaaS users do not have much control over their data or applications. But in hybrid mode it provides much more control over the data as to where the user wants to store it and in what type of environment and provides encryption to increase data security. Other tools are PaaS, IaaS, ITaaS, DaaS etc. Quality and Usefulness – It is important that when we collect data, it is contextualized or has some relevance to the problem, otherwise we will not be able to make the right data-driven decisions. Therefore, determining the quality or usefulness of the data is of utmost importance. If data quality control is not present, incorrect information may be transmitted. Possible solutions – Data visualization – When data quality is involved. Visualization is an effective way to keep data clean because visually we can know where unwanted data is located. We can plot data points on a graph, which can be difficult when dealing with large volumes of data. Another way is to group the data so you can visually distinguish it. Special Algorithms: Data quality was not a new concern, it was there since we started dealing with data. Additionally, keeping “dirty” or irrelevant data is costly for businesses. Therefore, special algorithms created specifically for data management, maintenance and cleaning are needed. Although when we are dealing with Big Data challenges, the sheer volume, variety and its security always have top priority. Data quality is equally important, as it wastes time, money and space by storing irrelevant data. Privacy and Security – In this rush to find trends by extracting data from all possible sources has left the privacy of the users from whom the data comes. data is collected, ignored. Special care needs to be taken while extracting information to help people not compromise their privacy Possible solutions: Look into cloud providers: Cloud storage is really useful when storing huge amounts of data, we just need to make sure that the cloud provider provides good protection mechanisms and includes penalties when adequate security is compromised. Access Control Policy: This is a key point for storing data anywhere. It is always necessary to have adequate control policies in place to provide access only to authorized users so as to allow misuse of personal data. Data Protection: All stages of data must be protected from the raw and clean data until the final stage of analysis. There should be encryption to protect sensitive data from leaking. There are many encryptions currently used by businesses, such as attribute-based encryption, which is a type of public key encryption where a user's secret key and text depend on attributes. Real-time monitoring: Surveillance should be used to monitor who attempts to access data. Threat inspections.