Category: Application Integration
In today’s data-driven world, the ability to effortlessly move and analyze data across diverse platforms is essential. Amazon AppFlow, a fully managed data integration service, has been at the forefront of streamlining data transfer between AWS services, software as a service (SaaS) applications, and now Google BigQuery. In this blog post, you explore the new Google BigQuery connector in Amazon AppFlow and discover how it simplifies the process of transferring data from Google’s data warehouse to Amazon Simple Storage Service (Amazon S3), providing significant benefits for data professionals and organizations, including the democratization of multi-cloud data access.
Based on immutable facts (events), event-driven architectures (EDAs) allow businesses to gain deeper insights into their customers’ behavior, unlocking more accurate and faster decision-making processes that lead to better customer experiences. In EDAs, modern event brokers, such as Amazon EventBridge and Apache Kafka, play a key role to publish and subscribe to events. EventBridge is […]
This blog post shows how to improve security in a data pipeline architecture based on Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and Amazon Elastic Kubernetes Service (Amazon EKS) by setting up fine-grained permissions, using HashiCorp Terraform for infrastructure as code.
AWS has invested in native service integration with Apache Hudi and published technical contents to enable you to use Apache Hudi with AWS Glue (for example, refer to Introducing native support for Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue for Apache Spark, Part 1: Getting Started). In AWS ProServe-led customer engagements, the use cases we work on usually come with technical complexity and scalability requirements. In this post, we discuss a common use case in relation to operational data processing and the solution we built using Apache Hudi and AWS Glue.
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 […]
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 […]
In the world of software engineering and development, organizations use project management tools like Atlassian Jira Cloud. Managing projects with Jira leads to rich datasets, which can provide historical and predictive insights about project and development efforts. Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other […]
Automate secure access to Amazon MWAA environments using existing OpenID Connect single-sign-on authentication and authorization
Customers use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run Apache Airflow at scale in the cloud. They want to use their existing login solutions developed using OpenID Connect (OIDC) providers with Amazon MWAA; this allows them to provide a uniform authentication and single sign-on (SSO) experience using their adopted identity providers (IdP) […]
Today, AWS is announcing the availability of in-place version upgrades for Amazon Managed Workflow for Apache Airflow (Amazon MWAA). This enhancement allows you to seamlessly upgrade your existing Apache Airflow version 2.x environments to newer available versions while retaining the workflow run history and environment configurations. You can now take advantage of the latest capabilities […]
Organizations across all industries have complex data processing requirements for their analytical use cases across different analytics systems, such as data lakes on AWS, data warehouses (Amazon Redshift), search (Amazon OpenSearch Service), NoSQL (Amazon DynamoDB), machine learning (Amazon SageMaker), and more. Analytics professionals are tasked with deriving value from data stored in these distributed systems […]