The raw analog data from the satellite is processed by Amazon’s modem digitizer into a data stream. It is then routed to an EC2 instance which is responsible for processing the signal so that it could be turned into a byte stream. Once the data is in digital form, streaming, processing, analytics, and storage options get available.
Amazon Kinesis Data Streams (KDS), a massively scalable and durable real-time data streaming service is used for capturing, processing, and storing data streams. KDS continuously captures gigabytes of data per second from thousands of sources such as website clickstreams, financial transactions, database event streams, social media feeds, location-tracking events and IT logs.The data which gets collected then becomes available in milliseconds to enable real-time analytics use cases such as real-time anomaly detection, real-time dashboards, dynamic pricing, and more.
Amazon Rekognition, based on a highly scalable, deep learning technology developed by Amazon’s computer vision scientists is used to analyze billions of images and videos daily. No machine learning expertise is required to use it. It is a simple and easy to use API which quickly analyzes any image or video file stored in Amazon S3. It also provides highly accurate facial analysis and facial recognition on images and videos.
Amazon SageMaker, a fully-managed platform helps developers and data scientists to build, train, and deploy machine learning models at any scale, easily. It also makes it easier for the developers as it removes all the complexity.
Amazon Redshift, a fast and scalable data warehouse makes it simple and cost-effective to analyze all the data across the data warehouse and data lake.It delivers ten times faster performance than other data warehouses by using machine learning. It is easy to setup and deploy a new data warehouse in minutes, and run queries across petabytes of data in the Redshift data warehouse.
Amazon Simple Storage Service (Amazon S3), an object storage service offers industry-leading scalability, security, data availability, and performance. It can be used by industries to store and protect any amount of data for a range of use cases, such as mobile applications, websites, archive, backup and restore, enterprise applications, etc. Amazon S3 Glacier is also useful as it is secure, durable, and extremely low-cost cloud storage service. It is used for data archiving and long-term backup.
Though the idea of AWS Ground Station sounds very interesting but the cost is still a question. Users have to pay per minute of downlink time and which is expensive. So, the idea of low cost fails here. Also, observations might not be that accurate as the orbit determination needs the control of the antenna. To convince the ones who would still be interested in building their own Ground Station and not relying on the third party, would be difficult.
To know more about this news, check out Amazon’s official blog.
Amazon re:Invent announces Amazon DynamoDB Transactions, CloudWatch Logs Insights and cloud security conference, Amazon re:Inforce 2019
3 announcements about Amazon S3 from re:Invent 2018: Intelligent-Tiering, Object Lock, and Batch Operations
Amazon FreeRTOS adds a new ‘Bluetooth low energy support’ feature