One of the important technology trends that we use today and in the future is big data and cloud technology. Big data helps in many ways in an organization. Most organizations use big data technology for their data and analysis cases. The usage of heavy data and its generations make the importance of big data technology at an organizational level. And also, cloud integration helps the growth of Big data to a high level and the data concepts and its analysis also changed after the evolution of big data.
What is Big Data?
Big data simply means a large amount of data that is generated from a lot of programs. Mainly large amounts of data are referred to as big data. It may be combined with structured and unstructured data collected by companies and used for different applications and analytics purposes. They are mainly represented using three V. Volume, which is a large amount of data from different environments, Variety is another one that means a variety of data and then velocity means the velocity of the data which is collected and processed. Companies mainly used to improve their business and operations using this big data.
In the case of the medical field, big data helps to identify risks, factors and doctors can easily diagnose the illness of a patient. And the combination of data from media sites, electronic health records, web and so many other sources give healthcare org and government agencies get updates information about infectious diseases or threats. Various kinds of data types needed to be managed and store in big data systems. In velocity case data processing speed has importance. How much faster the data is processed and mainly real time data is processed.
Veracity is also an important factor in the case of big data which means how accurate is the data collected and it is trusted or not. The data is collected in raw format from different sources and has quality issues if that is not correctly monitored it may cause bad data and analytics errors. So, the data analytics team should be ensuring that the data is accurate or not. Another important thing is value, the value of collected data. Variability is also had very important because how the data is differently formatted or changed is classified in the variability sessions.
Big data is stored mainly in data lakes. Different kinds of analytics are also used in big data like comparative analysis, social media analysis, Market Analytics, Sentiment analytics etc. Currently, all the big data services and its platform are combined for use in cloud like Amazon EMR, Cloudera data platforms, Cloud Data Proc of Google, Microsoft Azure HDInsight, HPE Ezmeral data Fabric etc. Mainly one of the challenges is processing capacity and designing the big data Architecture is also very challenging for users. All these issues can be overcome by using managed cloud services. So, at that end need to consider the cloud costing and on-premise data migration to the cloud.
Ways in which Big data and cloud Related
Cloud technology is trending day by day. Using the cloud technology and the high-performance remote devices and services that are served by each of the cloud platforms like Microsoft Azure, Amazon Web services (AWS), Google cloud etc.
Make it more popular today. The usage of Cloud computing technology in big data makes very easily processing of the huge amount of data generated from lots of devices. The data that is processing in big data is larger than just data stored in a volume. There are structured and unstructured data’s that need to process will be in the category of big data.
The amount of data generated in each and every second is not predictable. In the year of 2021, it is growing tremendously. The usage of smart devices, gadgets and iot devices also makes the data production in a heavy manner. The main reason for this kind of data is mostly unstructured and not processed in most cases. This huge data generation will lead organizations to think about rather than using the old method of data storage and processing to new cloud-based technologies.
Edge computing Technology
In this case edge computing has importance. Edge computing helps to distribute the data that is processed. It distributes the processing load to the device before moving the data to the server. In edge computing technology optimizes the storage, bandwidth, network technologies used for processing the data. And also make faster data processing and end user responses.
The usage of smart devices is increasing day by day like wearable gadgets like smartwatches, fitness bands etc. If the doctor wants to get the real time data from a patient in faster processing mode in all these scenarios, we can use this edge computing.
Big data in hybrid cloud and data lakes
Comparing to past decades today organizations will have large data requirements. Previously they maintain all the data, storage, security services in on premise environments to store and process the data. As per the increase in data requirements and storage, organizations want to implement or switch to cloud platforms like Microsoft Azure, AWS, Google cloud or etc. Also, the usage of hybrid environments makes it easier to manage their on-premise network and cloud resources.
By using the pay-as-go basis organization needs to be paid only for how much cloud resource is utilized and how much data is stored. The cost also will be one of the major factors in the case of Organization requirements. So, the organization will use the advantages of cloud implementation from their on-premise data center. The Implementation of the data lake concept has also become popular today. The data lake which stores the data in raw/natural format normally as file and blobs. mainly a single store for data in a raw format and also transformed data for specialized uses of reporting, machine learning, analytics etc. The data lake can be implemented in private/public cloud environments.
Machine learning and Artificial Intelligence (AI)
Because of the huge data generation, old types of data analysis are challenging and a very low amount of automation can be set up in that case. The coming of distributed processing technologies like Hadoop and spark helps organizations process huge amounts of data at high speed. Hadoop helps to store data and run applications on a cluster basis.
Provide heavy storage of different data types, High quality power for data processing and multitasking features, Flexibility, Scalability, low cost are its benefits. Apache Spark is also an open source, and distributed type of file system mainly used for big data works.
It is fast and used for large scale data processing, Flexibility, Memory compute features, real time data processing and better data analysis also features. The usage of machine learning and AI helps to spot easily and find any issues and helps to make better predictions than earlier.AI is used by most organizations to optimize and run their business at a top level.
Using this AI and machine learning companies use chatbots for their customer support without using support staff. This AI System will collect large amounts of customer data and details on large scales if we integrate with data lakes and make it more flexible. Graphical chart representations can also be generated with the help of Artificial Intelligence enabled analytics tools and improves the organization’s decision making capability also.
Importance of Data Scientist
The requirements of data scientists and chief data officers will be increased due to the increase in data growth. It is because big data is nothing without analysis. The data scientists are the people who collect and analyses the data using analytics and tools for reporting
Privacy
One of the challenging parts is the data security and privacy in huge data processing cases and protecting from cyber-attacks and from intruders is main. Because of the increase in data, security also is very important.
Fast data, Actionable data become Important
The future of big data is related to fast data and actionable kinds of data. The fast data analyses the real time data and processes in real time. Because of this data can be analyzed in milliseconds of time. This help company or organization for easy and fast decision making when the data comes and the importance of real time data also increases. In the case of actionable data that is missing the connection between business and big data. That is by using analytical platforms to solve this and get accurate information to organizations from the big data and improve their process.
Future of data and cloud: Vintage Trends
Cloud computing is growing day by day. Most of the software will be cloud based. Iaas, PaaS, SaaS have become more trending and economic in case of a company scenario. The hardware concept will be diminished and everything become software defined. And the data stored in cloud also be analyzed and that would become also more popular during these days and future. Most of the companies are planning to migrate their on-premise data centers to the cloud during the next few years step by step and the cloud trends have become more popular during those days.
The flexibility and cost-effective technology will be opted by most organizations so cloud computing is a better option for those who are planning to a migration. The Hybrid benefits also can be utilized for companies that have lots of critical servers in their own data centers like a private cloud. They can easily expand their infrastructure with minimum cost to the cloud data centers. The purchasing of physical hardware and its licensing is not a cost effective one, Because the initial investment is very large compared to building the same infrastructure in a cloud platform. So, in this case, companies can utilize the pay as you go feature offered by different cloud service providers and choose their services in an easy manner.
The storage technology offered by the cloud also provides high performance data transfer rate throughput and IOPS along with flexible usage. As Service will be trending now and also the coming of covid-19 companies started new application deployments and learn its operations in the changing world.
And also, more cybersecurity issues also happened so the cloud security concerns also improved. And Cloud automation technologies will become more effective and provide multi-cloud data analysis also become popular. Cloud agnostics have also become popular nowadays provide more flexibility of application, prevent overloading of servers and costly downtime.
The evolution of containerization and container technology improve the app flexibility, load time and become more scalable. As we said earlier AI is one of the key technology trends in the present and future. The data fabric technology is another trending one because it integrates data transmission between on premise and cloud and speeds up the digital transformation. During 2021-25 The demand for serverless computing is growing tremendously and without heavy capital investment, smaller organizations can deploy their applications in a serverless manner. It provides safe sandboxes for companies to run their codes and also can avoid backend failures. The importance of cloud data monitoring is also increasing in the future and controlling using a centralized management console is becoming popular. Native cloud applications have become most popular and provide too much flexibility, scalability and manageability. So, the requirement for cloud computing is increasing on a daily basis and the technology trends are also rapidly changing.
Conclusion
Everything can be easily integrated with the cloud that was one of the trending features of cloud platforms and easy to administer and manage the virtual servers and services. And the evolution of big data also increases the data storage and analytics of different data originated from different sources and helps companies to manage and make decisions from that so big data and cloud computing become trending for the next years and better options for all kinds of companies and a better all in one package for their infrastructure and data.
