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Amp, Amazon’s new live radio app, is a reinvention of radio, with human-curated live audio programming. It is designed to provide listeners and creators with a seamless customer experience by delivering interactive live audio shows from your favorite artists, radio DJs, podcasters and friends.
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However, as a new product in Amazon’s new territory, Amp needed more relevant data to inform their decision-making process. Amp needed a scalable data and analytics platform to easily access data and run machine learning (ML) experiments for real-time audio transcription, content moderation, feature engineering and personal program recommendation services, and to investigate or measure business KPIs and metrics.
This article is the first in a two-part series. Part 1 showed how to collect and process data using a data and analytics platform, and Part 2 showed how to use the data to create show recommendations using Amazon SageMaker, a fully managed ML service. Since launching in May 2022, the Personalized Show Recommendations listing service has shown a 3% increase in tracked customer engagement metrics such as liking a show, following a creator or enabling notifications for upcoming shows.
Amp’s data sources can be broadly categorized as streaming (near real-time) or batch (point-in-time). Source data comes from Amp-owned systems or other Amazon systems. The two different data types are as follows:
Amp creates a serverless streaming intake pipeline capable of ingesting data from sources without requiring infrastructure management, as shown in the diagram below.
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The pipeline is able to integrate Amp show catalog data (which are shows on Amp) and feed it into a data lake for two different use cases: one for near real-time analysis and one for batch analysis.
As part of the digest pipeline, the Amp team has an Amazon Simple Queue Service (Amazon SQS) queue that receives messages from an upstream Amazon Simple Notification Service (Amazon SNS) topic that contains information about changes to shows in the catalog. These changes may be the addition of new programs or adjustments to existing programs that are already planned.
When the SQS queue receives a message, it triggers a Lambda function to make an API call to the Amp Catalog service. The lambda function retrieves the required display metadata, filters the metadata, and sends the output metadata to Amazon Kinesis Data Streams. Amazon Kinesis Data Firehose receives records from a data stream. Kinesis Data Firehose then invokes a secondary Lambda function to perform data transformation, flatten the received JSON records, and write the transformed records to an Amazon Simple Storage Service (Amazon S3) data lake for consumption by Amp stakeholders.
Kinesis Data Firehose supports buffering and writing data to Amazon S3 every 60 seconds. This helps the Amp team make near-real-time programming decisions that affect external clients.
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Streaming intake pipelines support the following goals: performance, availability, scalability and flexibility to send data to various downstream applications or services:
Amp creates a transient batch (point-in-time) intake pipeline capable of data ingestion, processing and transformation, and storage, as shown in the diagram below.
Due to the batch nature and unknown data volume of these workloads, transient extract, transform and load (ETL) and extract, load and transform (ELT) job methods were implemented. As part of workflow automation, Amazon SQS is used to trigger Lambda functions. The lambda function then activates the glue crawler to infer the schema and data types. Crawlers write schema metadata to the Glue Data Catalog, providing a unified metadata store for data exchange.
ETL and ELT jobs must run on set schedules or event-driven workflows. To meet these needs, Amp used Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Apache Airflow is a Python-based open source workflow management platform. Amazon MWAA is a fully managed service that handles scaling automatically. It provides sequencing, error handling, retry logic and status. With Amazon MWAA, Amp was able to take advantage of Airflow for job orchestration without the need to manage or maintain a dedicated Airflow server. Additionally, by using Amazon MWAA, Amp was able to create code repositories and workflow pipelines stored in Amazon S3 that Amazon MWAA could access. The pipeline allows Amp data engineers to easily deploy Airflow DAG or PySpark scripts across multiple environments.
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Amp uses Amazon EMR on Amazon Elastic Kubernetes Service (Amazon EKS) to provide and manage containers for its data processing and transformation tasks. Due to the unique nature of the Amp Service, the amount of data that was initially expected to be processed was relatively unknown. To provide flexibility as the service grows, the team decided to use Amazon EMR on top of EKS to remove any unnecessary operational disruption needed to bootstrap and scale Amazon EMR for data processing. This approach allowed them to run a transient hybrid EMR cluster supported by a mix of Fargate and Amazon Elastic Compute Cloud (Amazon EC2) nodes, where all system tasks and workloads were offloaded to Fargate, while Amazon EC2 handled all Apache Spark processing and transformations has This provides the flexibility to run a cluster on one node, while the Amazon EKS Autoscaler dynamically instantiates and bootstraps any additional EC2 nodes needed for the job. They are automatically removed by the cluster autoscaler after the jobs are completed. This pattern eliminates the need for teams to manage any cluster bootstrapping or scaling required to respond to changing workloads.
Amazon S3 is used as a central data lake and the data is stored in the Apache Parquet (Parquet) format. Parquet is a columnar format that speeds up data retrieval and provides efficient data compression. Amazon S3 provided Amp with the flexibility, scalability and security it needed. With Amazon S3, the Amp team was able to centralize data storage in one place and access data via virtually any service or tool inside or outside of . The data lake is divided into two S3 buckets: one for the raw data and one for transformed data output. Amazon EMR performs the transformation from raw data to transformed data. Using Amazon S3 as a central data lake, Amp is able to securely expose and share data with other teams at Amp and Amazon.
To simplify data definition, table access configuration, and table deletion and deletion, they used the Glue Crawler and the Glue Data Catalog. Because Amp was a new and evolving service, the team needed a way to easily define, access, and manage tables in the data lake. The crawler handles data definition (including schema changes) and the addition and removal of tables, while the data catalog acts as a unified metadata store.
Amp chose to store data in an S3 data lake rather than a data warehouse. This allows them to be accessed in a unified way through the Glue Data Catalog and provides more flexibility for data consumers. This results in faster data access via various services or tools. Since the data is stored in Amazon S3, it also reduces data storage infrastructure costs because the cost is a function of the type of computation and the amount of data stored.
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The Amazon Redshift RA3 node type is used as a computing layer, enabling actors to query data stored in Amazon S3. Amazon Redshift RA3 nodes decouple storage and compute and are designed for access patterns through the Glue Data Catalog. RA3 nodes present Amazon Redshift Managed Storage supported by Amazon S3. The combination of these capabilities enables Amp to properly leverage the cluster and provide its customers with better query performance while minimizing costs.
Amazon Redshift configuration is automated with a Lambda function that connects to a given cluster and runs parameterized SQL statements. SQL statements include the logic to deploy schemas, user groups, and users, while Secrets Manager is used to automatically generate, store, and rotate Amazon Redshift user passwords. The underlying configuration variables are stored in Amazon DynamoDB. The lambda function retrieves variables and requests temporary Amazon Redshift credentials to perform the configuration. This process allowed the Amp team to set up Amazon Redshift clusters in a consistent manner.
In this article, we looked at how Amp used user behavior data from streaming and batch sources to create their data analytics platform. A key factor driving implementation is the need to provide a data analytics platform that is flexible, scalable, cost-effective and labor-saving. Design choices were made when evaluating different services.
Part 2 of this series shows
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