AWS Big Data Blog
Category: Learning Levels
Build an ETL process for Amazon Redshift using Amazon S3 Event Notifications and AWS Step Functions
In this post we discuss how we can build and orchestrate in a few steps an ETL process for Amazon Redshift using Amazon S3 Event Notifications for automatic verification of source data upon arrival and notification in specific cases. And we show how to use AWS Step Functions for the orchestration of the data pipeline. It can be considered as a starting point for teams within organizations willing to create and build an event driven data pipeline from data source to data warehouse that will help in tracking each phase and in responding to failures quickly. Alternatively, you can also use Amazon Redshift auto-copy from Amazon S3 to simplify data loading from Amazon S3 into Amazon Redshift.
Monitor Apache Spark applications on Amazon EMR with Amazon Cloudwatch
To improve a Spark application’s efficiency, it’s essential to monitor its performance and behavior. In this post, we demonstrate how to publish detailed Spark metrics from Amazon EMR to Amazon CloudWatch. This will give you the ability to identify bottlenecks while optimizing resource utilization.
Monitoring Amazon OpenSearch Serverless using AWS User Notifications
Amazon OpenSearch Serverless is a serverless deployment option for Amazon OpenSearch Service that makes it simple for you to run search and analytics workloads without having to think about infrastructure management. The compute capacity used for data ingestion, and search and query in OpenSearch Serverless is measured in OpenSearch Compute Units (OCUs). Customers can configure […]
Automate the archive and purge data process for Amazon RDS for PostgreSQL using pg_partman, Amazon S3, and AWS Glue
The post Archive and Purge Data for Amazon RDS for PostgreSQL and Amazon Aurora with PostgreSQL Compatibility using pg_partman and Amazon S3 proposes data archival as a critical part of data management and shows how to efficiently use PostgreSQL’s native range partition to partition current (hot) data with pg_partman and archive historical (cold) data in […]
Optimizing Amazon OpenSearch Service performance: Fine-tuning shard size with Amazon CloudWatch storage and shard skew health
In this post, we explore how to deploy Amazon CloudWatch metrics using an AWS CloudFormation template to monitor an OpenSearch Service domain’s storage and shard skew, as well as shard sizes. This solution uses an AWS Lambda function to extract storage and shard distribution metadata from your OpenSearch Service domain, calculates the level of skew and shard sizes, and then pushes this information to CloudWatch metrics so that you can easily monitor, alert, and respond. This information will help you to meet the recommended settings for read and write throughput, performance, and fault tolerance.
Try semantic search with the Amazon OpenSearch Service vector engine
Amazon OpenSearch Service has long supported both lexical and vector search, since the introduction of its kNN plugin in 2020. With recent developments in generative AI, including AWS’s launch of Amazon Bedrock earlier in 2023, you can now use Amazon Bedrock-hosted models in conjunction with the vector database capabilities of OpenSearch Service, allowing you to implement semantic search, retrieval augmented generation (RAG), recommendation engines, and rich media search based on high-quality vector search. The recent launch of the vector engine for Amazon OpenSearch Serverless makes it even easier to deploy such solutions.
Introducing AWS Glue crawler and create table support for Apache Iceberg format
Apache Iceberg is an open table format for large datasets in Amazon Simple Storage Service (Amazon S3) and provides fast query performance over large tables, atomic commits, concurrent writes, and SQL-compatible table evolution. Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time […]
Derive operational insights from application logs using Automated Data Analytics on AWS
Automated Data Analytics (ADA) on AWS is an AWS solution that enables you to derive meaningful insights from data in a matter of minutes through a simple and intuitive user interface. ADA offers an AWS-native data analytics platform that is ready to use out of the box by data analysts for a variety of use […]
Introducing Apache Airflow version 2.6.3 support on Amazon MWAA
Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a managed orchestration service for Apache Airflow that makes it simple to set up and operate end-to-end data pipelines in the cloud. Trusted across various industries, Amazon MWAA helps organizations like Siemens, ENGIE, and Choice Hotels International enhance and scale their business workflows, while significantly improving security […]
Perform Amazon Kinesis load testing with Locust
February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. Building a streaming data solution requires thorough testing at the scale it will operate in a production environment. Streaming applications operating at scale often handle large volumes of up to GBs per […]









