Due to the ever-increasing volume, velocity, and variety of data, traditional database systems are unable to meet the data management demands associated with big data. There are many different ways to define “big data,” but most incorporate the “three V’s”: volume, variety, and velocity.
Volume: size of data is measured in terabytes or petabytes
Variety: Data from a variety of sources and file types are included (e.g. web logs, social media interactions, ecommerce and online transactions, financial transactions, etc)
Velocity: Companies have strict requirements from the moment data is generated until the moment when it is used to inform decisions. As a result, data must be gathered, stored, processed, and analyzed in time frames as short as once per day and as long as in real-time as possible.
Reasons Why Big Data May Be Necessary
Despite all the talk about big data, many businesses either aren’t aware they have a problem or aren’t thinking of it as a big data problem. When a company’s current data storage and processing infrastructure becomes overwhelmed by a sudden surge in data volume, or when its data sources and types rapidly diversify, it may be time to consider implementing big data technologies.
Issues with big data can lead to declining productivity and competitiveness, as well as rising costs, if they aren’t dealt with properly. However, by transitioning resource-intensive processes to big data technologies and releasing new applications to take advantage of emerging opportunities, businesses can improve operational efficiency and cut costs.
Can you explain how Big Data operates?
Big data technologies have made it not only technically and economically feasible to collect and store larger datasets, but also to analyze them to discover new and valuable insights. This is because these technologies have introduced new tools that address the entire data management cycle. From the acquisition of raw data to its utilization as consumable intelligence, there is a common data flow in big data processing.
Collect. The first obstacle many companies face when dealing with big data is collecting the raw data, which can include anything from transactions and logs to information from mobile devices. For developers, this is simplified by a reliable big data platform that supports both real-time and batch data ingestion from a wide variety of sources.
Store. In order to store information before or after it has been processed, a big data platform needs a repository that is both safe and able to scale. Aside from permanent storage for at-rest information, you might also require transit storage for your unique needs.
To analyze and process. Here, basic operations like sorting, aggregating, joining, and even more complex functions and algorithms are applied to raw data in order to make it more usable. These data sets are either saved for later use or made accessible through business analytics and data visualization programs.
Take in and Picture It! One of the main goals of big data is to gain useful and actionable insights from data stores. Self-service business intelligence and agile data visualization tools make it possible for stakeholders to quickly and easily explore datasets. The information gleaned from analytics can be used to make statistical “predictions” in the case of predictive analytics or to prescribe courses of action in the case of prescriptive analytics, both of which can be consumed by end-users.
Big data processing has come a long way
Rapid progress has been made in many areas of the big data ecosystem. Today, many departments benefit from a wide variety of analytic approaches.
Questions like “What happened?” and “Why did this happen?” can be answered for users. with the aid of descriptive analytics. Conventional environments for querying and reporting data, such as those that feature scorecards and dashboards, are one such example.
Users are able to calculate the likelihood of a certain feature event with the aid of predictive analytics. Forecasting, anomaly detection, fraud detection, predictive maintenance, and early warning systems are all good examples.
As the name implies, prescriptive analytics prescribe actions for the user. Specifically, they answer the question “What should I do if “x” happens?”
Hadoop and other early big data frameworks only allowed for batch workloads, in which large datasets were processed in one go during a limited window of time (typically hours or days). The “velocity” of big data, however, has driven the development of new frameworks like Apache Spark, Apache Kafka, Amazon Kinesis, and others to support real-time and streaming data processing as time-to-insight became more of a priority.
Leveraging Amazon Web Services for Your Big Data Needs
If you’re looking to build, secure, and deploy big data applications, Amazon Web Services has you covered across the board. AWS eliminates the need to invest time and money into the procurement and upkeep of physical infrastructure, freeing up your team to instead investigate new avenues of inquiry. You can take advantage of cutting-edge technologies without making any long-term commitments thanks to the constant rollout of new features and functionalities.
Long provisioning and setup times are inherent in most big data technologies because they necessitate large clusters of servers. With AWS, you can set up your necessary systems in a flash. Your teams will be able to accomplish more, new ideas will be more easily tested, and products will be released faster.
Broad & Deep Capabilities
There is a wide range of big data workloads because there is a wide range of data assets that people want to analyze. Regardless of the data’s volume, velocity, or variety, a versatile platform will allow you to construct any big data application and handle any workload. For all your big data needs in the cloud, look no further than AWS and its fifty plus services and hundreds of new features each year.
Trusted & Secure
Confidential information is contained in big data. Therefore, it is crucial to safeguard data assets and secure infrastructure without compromising on responsiveness. For even the most stringent requirements, AWS has you covered across the board with their infrastructure, network, software, and business processes. Constant assessments are performed to maintain accreditations like ISO 27001, FedRAMP, DoD SRG, and PCI DSS. Twenty or more regulations, such as HIPAA, NCSC, and others, can be met with the assistance of assurance programs. To find out more, go to the Cloud Security Resource Center.
Hundreds of Partners & Solutions
When starting out with big data, having a large network of partners to help you learn the ropes can be invaluable. The AWS Partner Network is where you can find consulting partners who can assist you, as well as numerous tools and applications covering the full spectrum of data management needs.
Challenges with big data? We can help with that. If you let us handle the tough stuff, you’ll have more time and energy to devote to achieving your company’s or organization’s goals.