I am currently learning to use RabbitMQ. I am trying to publish a message to RabbitMQ from Azure Databricks using pyspark. Any idea about how would that be achievable?
Unfortunately, RabbitMQ is not supported as a source in Azure Databricks.
Azure Databricks - Streaming Data Sources and Sinks
Structured Streaming has built-in support for a number of streaming data sources and sinks (for example, files and Kafka) and programmatic interfaces that allow you to specify arbitrary data writers.
Apache Kafka
Azure Event Hubs
Delta Lake Tables
Read and Write Streaming Avro Data with DataFrames
Write to Arbitrary Data Sinks
Optimized Azure Blob Storage File Source with Azure Queue Storage
As per my research, I have found a third-party tool named "Panoply" which integrate Databricks and RabbitMQ using Panoply.
Hope this helps.
Related
Hi Everyone,
I've requirement to read streaming data from Azure EventHub and dump it to blob location. As per the cost optimization, i cannot prefer either Stream Analytics or Spark Streaming. I can only go with Spark batch job, that i need to explore how to read data from Azure EventHub as a batch(preferably previous day's data) and dump it to blob. My Azure EventHub holds 4 days of data, i need to make sure that i should avoid duplicates every-time i read the data from Azure EventHub.
I'm planning to read the data from azure event-hub once in a day using spark, is there a way i can maintain some sequence every time i read the data so to avoid duplicates.
Any help would be greatly appreciated.
The Azure client libraries for Event Hubs have an EventProcessor. This processor processes events from supports a checkpoint store that persists information about what events have been processed. Currently, there is one implementation of a checkpoint store that persists checkpoint data to Azure Storage Blobs.
Here is the API documentation for the languages I know it is supported in. There are also samples in the GitHub repository and samples browser.
.NET documentation
Java documentation
Python documentation
TS/JS documentation
If you are looking for just transferring events into "a blob location", Event Hubs supports capture into Azure Storage Blobs.
If stream process is all about dumping events to Azure Storage then you should consider enabling capture instead where service can dump events to your choice of storage account as events arrive. https://learn.microsoft.com/en-us/azure/event-hubs/event-hubs-capture-overview
In a brief, I've achieved this by Spark Structured Streaming + Trigger.Once.
processedDf
.writeStream
.trigger(Trigger.Once)
.format("parquet")
.option("checkpointLocation", "s3-path-to-checkpoint")
.start("s3-path-to-parquet-lake")
The main thing is i want to connect Azur SQL to confluent kafka using CDC approach and then i want to take that data into s3.
There are various ways of getting data out of a database into Kafka. You'll need to check what Azure SQL supports but this talk (slides) goes into the options and examples, usually built using Kafka Connect.
To stream data to S3 from Kafka use Kafka Connect (which is part of Apache Kafka), using the S3 sink connector which is detailed in this article.
To see an example of database-S3 pipelines with transformations included have a look at this blog post.
I am working for an energy provider company. Currently, we are generating 1 GB data in form of flat files per day. We have decided to use azure data lake store to store our data, in which we want to do batch processing on a daily basis. My question is that what is the best way to transfer the flat files into azure data lake store? and after the data is pushed into azure I am wondering whether it is good idea to process the data with HDInsight spark? like Dataframe API or SparkSQL and finally visualize it with azure?
For a daily load from a local file system I would recommend using Azure Data Factory Version 2. You have to install Integration Runtimes on Premise (more than one for High Avalibility). You have to consider several security topics (local firewalls, network connectivity etc.) A detailed documentation can be found here. There are also some good Tutorials available. With Azure Data Factory you can trigger your upload to Azure with a Get-Metadata-Activity and use e. g. an Azure Databricks Notebook Activity for further Spark processing.
I am using flink streaming to read the data from the file in AzureDataLake store.Is there any connector available to read the data from the file stored in Azure Data Lake continuously as the file is updated.How to do it?
Azure Data Lake Store (ADLS) supports REST API interface that is compatible with HDFS and is documented here. https://learn.microsoft.com/en-us/rest/api/datalakestore/webhdfs-filesystem-apis.
Currently there are no APIs or connectors available that poll ADLS and notify/read-data as the files/folders are updated. This is something that you could implement in a custom connector using the APIs provided above. Your connector would need to poll the ADLS account/folder on a recurring basis to identify changes.
Thanks,
Sachin Sheth
Program Manager
Azure Data Lake
I am using Apache flink for Streaming .I am taking data from Apache Kafka as a stream through flink and do some possessing and save the resulted stream in Azure Data lake.Is there any connector available in flink to dump the stream data in Azure data lake?
Flink supports all file systems that implement org.apache.hadoop.fs.FileSystemas noted here: https://ci.apache.org/projects/flink/flink-docs-release-0.8/example_connectors.html.
So you should be able to set it up to output data to Azure Data Lake Store. Here is a blog that shows how to connect Hadoop to Azure Data Lake Store. The same approach in theory should work for Flink. https://medium.com/azure-data-lake/connecting-your-own-hadoop-or-spark-to-azure-data-lake-store-93d426d6a5f4