.png){kind=logo}
- Insights
- Cryptocurrencies
- Stocks
- White Papers
- Industry
- Geography
- Insights
- Cryptocurrencies
- Stocks
- White Papers
- Industry
- Geography
All these years we are familiar with Amazon Web Services (AWS) and knew that AWS is prominent in data solutions and cloud computing. With more companies finding themselves dealing with copious amounts of data, it is important to ensure access to big data solutions for computing and analyzing this data. Amazon web services offer an efficiently distinguished variety of cloud computing tools that provide everything from the framework for computing big data to analytics for understanding that data. Additionally, AWS offers other actions and tools for big data management. As we go deep into the article, we will dig into some of the various features of the most popular big data solutions offered by AWS, and the benefits of AWS for big data. With a better understanding of how AWS is approaching big data, your business can make actionable decisions on big data solutions in the future.
Before you can do anything meaningful with big data, it must be processed. The managed frameworks and distributed frameworks within AWS allow for quick, cost-effective, and simple processing of large amounts of data. With the capacity to run a variety of popular frameworks and only pay for what you use, businesses have the freedom to utilize the big data framework that best fits their processing needs.
Big data requires big storage. Most importantly, storage options for big data should always be scalable in order to maintain the variations in cost-effectiveness. Amazon offers several database and storage options to meet certain needs of a business' data, with all choices being highly scalable and flexible with broad capabilities.
AWS offers Amazon Redshift, a data warehouse which is entirely organized. Traditional data warehousing is expensive and difficult to maintain. So, Amazon created Redshift to make it simple and cost-effective to manage and analyze large amounts of data. Users can run queries on structured data in Redshift with nearly quick responses.
The essence of big data is present in the business intelligence which you are able to gain from the data. More so, businesses need insights which are actionable whereas to truly take benefit of BI. Inside the AWS platform, patrons have access to Amazon QuickSight (AQS). AQS is a business intelligence solution for big data, providing business calculations, valuations, and rich visuals.
The process involved in examining large and varied datasets is known as big data analytics. Even though there are various analytical tools to choose from, the big data analytics tools present in AWS serve to load large amounts of data while concurrently loading and processing that data.
These real-time analytics platforms boast powerful services. Capabilities include loading streaming data into AWS, analytics to run queries and instantly scale, and the option to build custom apps directly in AWS to process and analyze streaming data.
Handling servers is a tradition in the past. With big data server compute through AWS Lambda, users are only charged for when the code is running and compute is being utilized. There will be a huge bonus when working with large amounts of data, as this is highly scalable and cost-effective.
With so many options for big data solutions, it is easy to become overly burdened with information. With that said, AWS is absolutely leading the charge in big data solutions. However, there are other platforms to be considered — such as Google Cloud Platform and Azure. It comes down to the needs of the individual business and the big data that business is working with.
