![]() ![]() Lambda supports multiple programming languages, and for our use case, we use Python 3.8. It’s a computing service that runs code in response to events and automatically manages the computing resources required by that code. Lambda is an event-driven, serverless computing platform provided by AWS. ![]() In our use case, our target storage layer is Amazon Redshift, so Kinesis Data Firehose fits great to simplify the solution. It can also batch, compress, transform, and encrypt the data before loading it, minimizing the amount of storage used at the destination and increasing security. It’s a fully managed service that automatically scales to match the throughput of your data and requires no ongoing administration. It can capture, transform, and load streaming data into Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon OpenSearch Service, and Splunk, enabling near-real-time analytics with existing business intelligence (BI) tools and dashboards you’re already using today. Kinesis Data Firehose is the easiest way to reliably load streaming data into data lakes, data stores, and analytics tools.We use Kinesis Data Streams because it’s a serverless solution that can scale based on usage. The data collected is available in milliseconds to enable real-time analytics use cases such as real-time dashboards, real-time anomaly detection, dynamic pricing, and more. It can continuously capture gigabytes of data per second from hundreds of thousands of sources, such as website click-streams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events. Kinesis Data Streams is a massively scalable and durable real-time data streaming service.This solution uses AWS services for the following purposes: To demonstrate the implementation approach, we use AWS cloud services like Amazon Kinesis Data Streams as the message bus, Amazon Kinesis Data Firehose as the delivery stream with Amazon Redshift data warehouse as the target storage solution, and AWS Lambda as record transformer of Kinesis Data Firehose, which flattens the nested XML structure with custom parser script in Python. In this post, we discuss a use case where XMLs are streamed through a real-time processing system and can go through a custom XML parser to flatten data for easier business analysis. When systems interact with each other and process data through different pipelines, they expect real-time processing or availability of data, so that business decisions can be instant and quick. But for XML files, we need to consider a custom parser, because the format is custom and can be very complex. When applications deal with CSV or JSON, it becomes fairly simple to parse because most programming languages and APIs have direct support for CSV or JSON. ![]() Most third-party system integrations happen through SOAP or REST web services, where the input and output data format is either XML or JSON. When we look at enterprise data warehousing systems, we receive data in various formats, such as XML, JSON, or CSV. ![]()
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