AWS Big Data Blog

Category: Amazon Simple Notification Service (SNS)

Use Snowflake with Amazon MWAA to orchestrate data pipelines

This blog post is co-written with James Sun from Snowflake. Customers rely on data from different sources such as mobile applications, clickstream events from websites, historical data, and more to deduce meaningful patterns to optimize their products, services, and processes. With a data pipeline, which is a set of tasks used to automate the movement […]

Monitor data pipelines in a serverless data lake

AWS serverless services, including but not limited to AWS Lambda, AWS Glue, AWS Fargate, Amazon EventBridge, Amazon Athena, Amazon Simple Notification Service (Amazon SNS), Amazon Simple Queue Service (Amazon SQS), and Amazon Simple Storage Service (Amazon S3), have become the building blocks for any serverless data lake, providing key mechanisms to ingest and transform data […]

Build and automate a serverless data lake using an AWS Glue trigger for the Data Catalog and ETL jobs

September 2022: This post was reviewed and updated with latest screenshots and instructions. Today, data is flowing from everywhere, whether it is unstructured data from resources like IoT sensors, application logs, and clickstreams, or structured data from transaction applications, relational databases, and spreadsheets. Data has become a crucial part of every business. This has resulted […]

How to build a front-line concussion monitoring system using AWS IoT and serverless data lakes – Part 2

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. In part 1 of this series, we demonstrated how to build a data pipeline in support of a data lake. We used key AWS services such as Amazon Kinesis Data Streams, Kinesis […]

How to build a front-line concussion monitoring system using AWS IoT and serverless data lakes – Part 1

In this two-part series, we show you how to build a data pipeline in support of a data lake. We use key AWS services such as Amazon Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and AWS Lambda. In part 2, we focus on generating simple inferences from that data that can support RTP parameters.