The great impact of big data

Nowadays, data generates anytime and anywhere. These data may seem disordered; however, the potential value makes it so important that enterprises crave to make use of it and even forecast with it. Big data does not only mean large datasets. It has been so commonly discussed in the past decades as the huge amount of datasets cannot be effectively analyzed by using traditional database method. The most known definition of Big Data are the three Vs.

  • Volume:the amount of data generated.
  • Variety:the content of data may contain text, images, audio, and video.
  • Velocity:the total time spent from data generated to the result comes up. The shorter the time, the higher the value is.

AWS Big Data Analytics Services

Analyzing big data requires a lot of computing ability, and the amount of computing ability can vary depending on the amount of data entered and the type of analysis. In the traditional architecture, due to the limited size of the device, the hardware device cannot meet the future needs in time; in contrast, on AWS, you can adjust the capacity and computing power within a few minutes, and let your system and application try to achieve the best possible efficiency.

There are several related services in AWS that support big data analytics and able to help you get to the full flow of big data analytics faster:

  • Amazon Kinesis:Instantly collects, processes, and stores streaming data without having to wait for all data to be collected.

  • Amazon Redshift:Adjust specifications or expanding data warehousing services according to user needs, also able to search an amount of data content at the same time. Therefore, the performance is 10 times faster than other data warehousing services and can be extended to Data Lake established by Amazon S3 for rapid analysis any size of data.

  • AWS Lambda:a serverless service, as long as the code updated and other AWS services set up, Lambda will trigger execution according to an event, and will only charge for the execution time.

  • Amazon EMR: AWS Managed Hadoop Framework. An application that can quickly build the required computing resources (EC2) to perform data-intensive tasks.
  • Amazon Athena:a serverless interactive query service, which can directly query the data in Amazon S3 using SQL syntax, and support multiple standard formats including JSON, CSV, Avro, ORC, Parquet.

  • AWS Glue:an AWS Managed ETL service, which can be used to sort, clean, and categorize data. With AWS Glue, you can significantly reduce the cost, complexity, and time required to create ETL jobs.

  • Amazon QuickSight:Business Analytics Services (BI) is provided by AWS to easily visually present material content through data.

AWS big data analytics and business intelligence (BI) services architecture

The focus of big data applications is the final “analysis”. The results of these analyses help a company’s management on making important decisions. Compared to the cold words, human brains are more willing to accept imaged reference information. The business intelligence tool is to let users understand the true meaning of the data more intuitive through the image and instant interactive way, and even see the deeper insights of the data to predict the future feasibility. In addition to being able to connect with native services, AWS Big Data Analytics tools can be connected to many third-party software.

The following is an example of the architecture diagram of the business intelligence software Tableau on AWS (Learn more):