And even today they keep continuing to demonstrate their pathfinding abilities. Besides the great history lessons and customer talks, Adam of course presented us with many new services and features that will be launched soon. We have collected our favourites for you here and explain their real-world pathfinding abilities!
Read the full announcement here.
Pushing the edge of cloud: AWS keeps delivering this promise in every way we can use the cloud. In the words of Adam: “Everything must be seamless. Cloud services must ‘be AWS’, not just ‘feel like AWS’.” This year at re:Invent we get a first peek at their latest mobile networking innovation: AWS Private 5G. With this service, AWS enables enterprises to rapidly roll out private 5G networks. After indicating the location of where you want to roll out your network and the capacity you expect to need, AWS will provide you with the necessary hardware, software and SIM cards to get you started within days. And as is with most cloud services: there are no upfront costs and you only end up paying for your actual network capacity and throughput.
Read the full announcement here.
AWS Lake Formation now supports fine grained access policies on cell-level, ACID transactions on tables and automatic compaction of data in your data lake. These new features make it easier to ingest and process data at any scale and allow multiple users to reliably work with the data concurrently. With automatic compaction AWS Lake Formation will optimize the underlying objects in S3 into larger objects to optimize access by services like Amazon Athena and Amazon Redshift Spectrum. By utilizing row and cell-level security you can safely share (parts of) your data with other users on the platform without the need to create separate, partial data sets for each of them.
More info can be found here.
Serverless and on-demand analytics
AWS released a number of serverless analytics options for their current services. You can now use Redshift, EMR, Kinesis Data Streams and Managed Streaming for Apache Kafka without the need to worry about sizing and scaling. This serverless on-demand capacity option is great when you are not yet familiar with what the workload will be on the service, when you have unpredictable workloads or if you just don’t want to worry about scaling and capacity management.
If you want to know more about the on-demand capacity options:
Following the trend of low-code platforms, AWS introduced us to a new way to train advanced ML models without any ML experience or having to write actual code. Amazon SageMaker Canvas allows anyone to build ML workflows with a point-and-click interface to generate accurate predictions from your data. This enables anyone in an organization to predict business outcomes, without first bothering data scientists. Amazon SageMaker Canvas is built on top of the other Amazon SageMaker functionalities, and supports multiple problem types such as binary or multi-class classification and time series forecasting amongst many others. Possible problems to tackle without knowing any code are fraud detection and inventory optimization, as mentioned by AWS.
AWS IoT TwinMaker helps developers create a digital twin of real-world systems. It does so by allowing you to connect and access data from different sources without the need to collect and centralize that data yourself. By using built-in connectors for IoT SiteWise, Kinesis and S3 you can collect and store time-series sensor data, video streams and other data like business data or drawings. It also allows you to implement your own data connectors to use with other data sources that you might have. AWS IoT TwinMaker forms a graph that understands the relationships between the different data sources which you can then use to visualize the data in a Grafana based dashboard.
Getting data from a large and diverse fleet of vehicles can be challenging. AWS IoT FleetWise aims to make it easier and cost effective for automakers to collect and process data from vehicles into the cloud in near-real time. It aids automakers by providing a service that will allow them to model a vehicle and all the sensors, collect all the data using an edge agent and collection schemas. Use-cases range from proactive maintenance to sharing data between vehicles in the fleet to improve navigation. Click here to learn more about AWS IoT FleetWise.
Make sure to check our coverage of the keynote by Amazon CTO Werner Vogels!