I am trying to read data from azure eventhub using sparkstreaming in Azure Databricks. However I need to write my consumer client in such a way that it reads from a particular partition ID of an eventhub.
Related
i am working in the IoT Space with 2 Databases. AWS Time Stream & AWS DynamoDB.
My sensor data is coming into Time Stream via AWS IoT Core and MQTT. I set up a rule, that gives permission to transfer the incoming data directly into Time Stream.
What i need to do now is to run some operations on the data and save the result of these operations into DynamoDB.
I know with DynamoDB there is function called DynamoDB Streams. Is there a solution like Streams in Time Stream as well? Or does anybody has an idea, how i can automatically transfer the results of the operations from Time Stream to DynamoDB?
Timestream does not have Change Data Capture capabilities.
Best thing to do is to write the data into DynamoDB from wherever you are doing your operations on Timestream. For example, if you are using AWS Glue to analyze your Timestream data, you can sink the results directly from Glue using the DynamoDB sink.
Timestream has the concept of Schedule Query. When a query has ran, you can be notified via a SNS topic. You could connect a lambda on that SNS topic to retrieve the query result and store it in DynamoDB.
My company is doing a POC on some streaming data and one of the tasks is sending data from AWS Kinesis to Azure Event Hubs.
Has anyone tryed to do something like this before?
I was thinking of a lambda function listening to kinesis firehose and sending the data to event hubs but I have no experience on Azure at all and I don't even know if this is possible.
Yes, this is very much possible.
Inter-Cloud environment where data can be streamed among two services can be achieved using AWS Kinesis and Azure Event Hub.
You can stream data from Amazon Kinesis directly to Azure Event Hub in Real-Time. Using ‘serverless’ model and cloud computing to process and transfer events without having the need to manage any native application written on an on-premise server.
You will be required connection string, SharedAccessKeyName, and SharedAccessKey from the Azure Event Hub. This will be needed to send data to Event Hub. Also, make sure the Event hub can receive data from the IP address you are running the program from.
Refer this third-party tutorial to accomplish the same
I have got one requirement where we have to write real-time data to AWS Aurora (PostgreSQL) using StreamSets Data Collector. I have never worked on StreamSets but I have learn that it's a data connector. I tried to search to get something on this topic but no luck. Any idea how StreamSets can be used to write data to Aurora?
You can use StreamSets Data Collector's JDBC Producer destination to write data to Aurora. Data Collector includes the JDBC driver required for PostgreSQL.
I am investigating the use of Amazon DynamoDB and would like to stream query results to my remote clients.
I cannot find any Amazon DynamoDB documentation that shows it supports said streams.
All I can find are Amazon DynamoDB stream endpoints for live streams as data is changed within the database.
These are not the streams Iam interested in.
I wish to query the Amazon DynamoDB and retrieve the results as a stream to enable me to transmit the streamed data to my remote clients via HTTP.
Does Amazon DynamoDB support this type of streaming of results?
I am looking to deploy code that resembles this on my server
private StreamingOutput getStreams() {
return new StreamingOutput() {
#Override
public void write(final OutputStream outputStream) throws IOException, WebApplicationException {
outputStream.write(getArticles());
outputStream.flush();
outputStream.close();
}
};
}
and my client uses Retrofit
#Streaming
#GET
Call<ResponseBody> fetchData();
Unless I misunderstood what you're trying to do, DynamoDB does allow streaming. From AWS Docs:
DynamoDB Streams captures a time-ordered sequence of item-level modifications in any DynamoDB table, and stores this information in a log for up to 24 hours. Applications can access this log and view the data items as they appeared before and after they were modified, in near real time.
...
To read and process a stream, your application will need to connect to a DynamoDB Streams endpoint and issue API requests.
Amazon DynamoDB Streams actions
What is the best practice for moving data from a Kafka cluster to a Redshift table?
We have continuous data arriving on Kafka and I want to write it to tables in Redshift (it doesn't have to be in real time).
Should I use Lambda function?
Should I write a Redshift connector (consumer) that will run on a dedicated EC2 instance? (downside is that I need to handle redundancy)
Is there some AWS pipeline service for that?
Kafka Connect is commonly used for streaming data from Kafka to (and from) data stores. It does useful things like automagically managing scaleout, fail over, schemas, serialisation, and so on.
This blog shows how to use the open-source JDBC Kafka Connect connector to stream to Redshift. There is also a community Redshift connector, but I've not tried this.
This blog shows another approach, not using Kafka Connect.
Disclaimer: I work for Confluent, who created the JDBC connector.