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#PORTAL BUNDLE STEAM KEY AMAZON HOW TO#

You can create an AWS group that has write access permission to the stream and then add an existing or new user to the group.

The newly created stream is displayed on the stream's page, in this case, Amazon Kinesis→Data streams→ general_stream.Īfter the stream is created, you'll need to create a user in AWS that has to write access to the stream.

Click the Create data stream button at the bottom of the Create data stream page.

In this case, we'll name the stream general_stream.

Enter the name of the new data stream in the Data stream name box.

Select the Kinesis Data Streams option in the dialog that appears.

Search the AWS Services main page for the term "Kinesis "and then select the Kinesis service.

From the AWS Services page, choose Kinesis Data Streams and create a stream. In this article, we'll use the dashboard.įigure 3 illustrates the process for creating the Kinesis stream.įigure 3.

Create the stream directly using the AWS command line.

There are a few ways to set up a Kinesis stream: In this article, we'll just get data into Kinesis Data Streams. However, creating and using a consumer is beyond the scope of this article. There is actually a fourth step: In order for stream data to be useful, it needs to be processed by a consumer.

Submit stream data as that user (group) to the stream on the backend.

The user or group must have AWS permissions to use the stream.

Attach a user or user group to the stream.

Working with a Kinesis data stream is a three-step process, discussed in the sections that follow: In this article, we will use Kinesis Data Streams, which is a general streaming manager. Amazon Kinesis Data StreamsĪmazon Kinesis is made up of a number of subservices such as Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, and Amazon Kinesis Video Streams.

#PORTAL BUNDLE STEAM KEY AMAZON HOW TO#

Note: To learn how to implement message streams using OpenShift and Kafka, try this activity in the no-cost Developer Sandbox for Red Hat OpenShift: Connecting to your Managed Kafka instance from the Developer Sandbox for Red Hat OpenShift. In this article, we're going to look at the Amazon Kinesis service. The streaming component of Red Hat AMQ is based on Apache Kafka and integrates with Red Hat OpenShift.

In contrast, streams involve working with an enormous amount of messages flowing continuously in one direction all the time.Ī number of services support streaming. The important thing to understand about streaming is that it's very different from a request/response interaction that handles one message at a time, which is typical when web surfing or using an HTTP API such as REST. All that any party needs to do to consume information from the stream of interest is establish a connection to the stream manager. The user experience team might want to know how much time a user takes moving the mouse around a web page before executing an action. The marketing department might be interested in the ads the user clicks on. Then, on the server side, there might be any number of consumers interested in a user's mouse events. Each message might describe a particular mouse event from the user's browser. Each browser sends a continuous stream of messages back to a stream manager on the server side. Data streams from multiple producers can be combined in the streaming manager and sent to consumers.Īn example of this many-to-many scenario is where many browsers act as producers on the client side of a web application. Multiple consumers are easy to support, which is great for broadcasting.įigure 2. In this scenario, you can think of the video source as the producer and the viewers at home as consumers (Figure 1).

Then that stream is forwarded onto users' computers or smart TVs. The C-SPAN studio streams bytes of video data that make up a telecast through a streaming manager to a data center on the backend. Probably the best example of streaming data is video content from a producer such as C-SPAN. Understanding streaming patternsĪs the name implies, a stream is a continuous flow of data transmitted at high-speed rates between a source and target. This article takes you from the initial configuration through running a producer and checking reports on its behavior. Making it all work requires both an understanding of some basic streaming patterns and an awareness of the tools and techniques you can use to get the job done. This article shows how to get a stream up and running under Amazon Kinesis, a stream management service offered by Amazon Web Services (AWS).ĭata streaming is a different way of doing business. The difference between handling events and data streaming is like going from drinking water one glass at a time to taking it in from a garden hose. Working with streams adds a new dimension to application programming.

Streaming applications process data such as video, audio, and text as a continuous flow of messages.