When it comes to streaming data processing, Azure and AWS are two major players in the cloud architecture space. Both of them provide services with their own strengths and features. In this post, we'll compare two services, Azure Stream Analytics and AWS Glue Streaming ETL, and help you identify which one is best for your needs.
Azure Stream Analytics
Azure Stream Analytics is a cloud-based service provided by Microsoft Azure, which enables you to process and analyse real-time streaming data using SQL-like query language. It supports various streaming sources such as Azure Event Hubs, Azure IoT Hub, and Apache Kafka. Once you've defined your query using SQL-like syntax, the query is processed continuously and the results can be stored in various destinations such as Azure Storage, Azure Cosmos DB, and Azure Data Lake Storage.
One of the significant advantages of Azure Stream Analytics is its low latency in processing data. It can process millions of events per second with less than one second of latency. Also, it provides various built-in functions such as windowing, data partitioning, and machine learning functions, which makes it easy to process and analyse streaming data.
AWS Glue Streaming ETL
AWS Glue Streaming ETL is a fully managed real-time streaming data processing service provided by Amazon Web Services (AWS). It is built on the Apache Spark framework and offers a user-friendly interface to perform ETL (Extract-Transform-Load) operations on streaming data. Glue Streaming ETL can process data from various sources such as Amazon Kinesis Streams and Apache Kafka. Once the data is processed, it can be stored in various destinations such as Amazon S3, Amazon Redshift, and Amazon Elasticsearch.
One of the significant advantages of AWS Glue Streaming ETL is its scalability. It automatically scales resources based on your needs and provides faster processing of large amounts of data. Also, it has a flexible pricing model based on the number of processing units you use.
A Comparison
| Features | Azure Stream Analytics | AWS Glue Streaming ETL |
|---|---|---|
| Latency | <1 second | >1 second |
| Supported Input | Azure Event Hubs, Azure IoT Hub, Apache Kafka, Azure Blob Storage, Azure Data Lake Storage | Amazon Kinesis Streams, Apache Kafka |
| Supported Output | Azure Blob Storage, Azure Data Lake Storage, Azure Cosmos DB, Azure SQL Database, Azure Stream Analytics, Power BI, Tableau | Amazon S3, Amazon Redshift, Amazon Elasticsearch |
| Scaling | Manual Scaling | Auto-scaling |
| Pricing Model | Pay-as-you-go | Processing Units |
Conclusion
Both Azure Stream Analytics and AWS Glue Streaming ETL are great services for processing streaming data. Azure Stream Analytics is an excellent choice when you need low latency querying, and you're processing data from Microsoft Azure sources. On the other hand, AWS Glue Streaming ETL is the best option when you need to perform ETL on streaming data from Amazon Web Services sources, and you need a scalable solution that can process large amounts of data.
It is always recommended to evaluate both services based on your requirements before making a decision.
References
- Azure Stream Analytics. (n.d). Retrieved from https://azure.microsoft.com/en-in/services/stream-analytics/
- AWS Glue Streaming ETL. (n.d). Retrieved from https://aws.amazon.com/glue/streaming-etl/