AWS Big Data Blog
Category: AWS Step Functions
Enrich your serverless data lake with Amazon Bedrock
Organizations are collecting and storing vast amounts of structured and unstructured data like reports, whitepapers, and research documents. By consolidating this information, analysts can discover and integrate data from across the organization, creating valuable data products based on a unified dataset. This post shows how to integrate Amazon Bedrock with the AWS Serverless Data Analytics Pipeline architecture using Amazon EventBridge, AWS Step Functions, and AWS Lambda to automate a wide range of data enrichment tasks in a cost-effective and scalable manner.
Build a serverless data quality pipeline using Deequ on AWS Lambda
Poor data quality can lead to a variety of problems, including pipeline failures, incorrect reporting, and poor business decisions. For example, if data ingested from one of the systems contains a high number of duplicates, it can result in skewed data in the reporting system. To prevent such issues, data quality checks are integrated into […]
Migrate workloads from AWS Data Pipeline
After careful consideration, we have made the decision to close new customer access to AWS Data Pipeline, effective July 25, 2024. AWS Data Pipeline existing customers can continue to use the service as normal. AWS continues to invest in security, availability, and performance improvements for AWS Data Pipeline, but we do not plan to introduce […]
Automate data loading from your database into Amazon Redshift using AWS Database Migration Service (DMS), AWS Step Functions, and the Redshift Data API
Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing ETL (extract, transform, and load), business intelligence (BI), and reporting tools. Tens of thousands of customers use Amazon Redshift to process exabytes of data per […]
Disaster recovery strategies for Amazon MWAA – Part 2
Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a fully managed orchestration service that makes it straightforward to run data processing workflows at scale. Amazon MWAA takes care of operating and scaling Apache Airflow so you can focus on developing workflows. However, although Amazon MWAA provides high availability within an AWS Region through features […]
Automate AWS Clean Rooms querying and dashboard publishing using AWS Step Functions and Amazon QuickSight – Part 2
Public health organizations need access to data insights that they can quickly act upon, especially in times of health emergencies, when data needs to be updated multiple times daily. For example, during the COVID-19 pandemic, access to timely data insights was critically important for public health agencies worldwide as they coordinated emergency response efforts. Up-to-date […]
Disaster recovery strategies for Amazon MWAA – Part 1
In the dynamic world of cloud computing, ensuring the resilience and availability of critical applications is paramount. Disaster recovery (DR) is the process by which an organization anticipates and addresses technology-related disasters. For organizations implementing critical workload orchestration using Amazon Managed Workflows for Apache Airflow (Amazon MWAA), it is crucial to have a DR plan […]
Enable metric-based and scheduled scaling for Amazon Managed Service for Apache Flink
Thousands of developers use Apache Flink to build streaming applications to transform and analyze data in real time. Apache Flink is an open source framework and engine for processing data streams. It’s highly available and scalable, delivering high throughput and low latency for the most demanding stream-processing applications. Monitoring and scaling your applications is critical […]
Build efficient ETL pipelines with AWS Step Functions distributed map and redrive feature
AWS Step Functions is a fully managed visual workflow service that enables you to build complex data processing pipelines involving a diverse set of extract, transform, and load (ETL) technologies such as AWS Glue, Amazon EMR, and Amazon Redshift. You can visually build the workflow by wiring individual data pipeline tasks and configuring payloads, retries, […]
Unstructured data management and governance using AWS AI/ML and analytics services
In this post, we discuss how AWS can help you successfully address the challenges of extracting insights from unstructured data. We discuss various design patterns and architectures for extracting and cataloging valuable insights from unstructured data using AWS. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data.