AWS Big Data Blog

Category: Technical How-to

Non-JSON ingestion using Amazon Kinesis Data Streams, Amazon MSK, and Amazon Redshift Streaming Ingestion

Organizations are grappling with the ever-expanding spectrum of data formats in today’s data-driven landscape. From Avro’s binary serialization to the efficient and compact structure of Protobuf, the landscape of data formats has expanded far beyond the traditional realms of CSV and JSON. As organizations strive to derive insights from these diverse data streams, the challenge […]

Process and analyze highly nested and large XML files using AWS Glue and Amazon Athena

In today’s digital age, data is at the heart of every organization’s success. One of the most commonly used formats for exchanging data is XML. Analyzing XML files is crucial for several reasons. Firstly, XML files are used in many industries, including finance, healthcare, and government. Analyzing XML files can help organizations gain insights into […]

Architecture Diagram

Build event-driven architectures with Amazon MSK and Amazon EventBridge

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 […]

Using Experian identity resolution with AWS Clean Rooms to achieve higher audience activation match rates

This is a guest post co-written with Tyler Middleton, Experian Senior Partner Marketing Manager, and Jay Rakhe, Experian Group Product Manager. As the data privacy landscape continues to evolve, companies are increasingly seeking ways to collect and manage data while protecting privacy and intellectual property. First party data is more important than ever for companies […]

Manage your workloads better using Amazon Redshift Workload Management

Amazon Redshift workload management (WLM) helps you maximize query throughput and get consistent performance for the most demanding analytics workloads by optimally using the resources of your existing data warehouse. This post provides examples of analytics workloads for an enterprise, and shares common challenges and ways to mitigate those challenges using WLM. We guide you through common WLM patterns and how they can be associated with your data warehouse configurations. We also show how to assign user roles to WLM queues and how to use WLM query insights to optimize configuration.

Set up fine-grained permissions for your data pipeline using MWAA and EKS

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.

Stitch Fix seamless migration: Transitioning from self-managed Kafka to Amazon MSK

Stitch Fix is a personalized clothing styling service for men, women, and kids. In this post, we will describe how and why we decided to migrate from self-managed Kafka to Amazon Managed Streaming for Apache Kafka (Amazon MSK).

Externalize Amazon MSK Connect configurations with Terraform

Managing configurations for Amazon MSK Connect, a feature of Amazon Managed Streaming for Apache Kafka (Amazon MSK), can become challenging, especially as the number of topics and configurations grows. In this post, we address this complexity by using Terraform to optimize the configuration of the Kafka topic to Amazon S3 Sink connector. By adopting this […]

Operational Data Processing Framework for Modern Data Architectures

Simplify operational data processing in data lakes using AWS Glue and Apache Hudi

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.

Securely process near-real-time data from Amazon MSK Serverless using an AWS Glue streaming ETL job with IAM authentication

Streaming data has become an indispensable resource for organizations worldwide because it offers real-time insights that are crucial for data analytics. The escalating velocity and magnitude of collected data has created a demand for real-time analytics. This data originates from diverse sources, including social media, sensors, logs, and clickstreams, among others. With streaming data, organizations […]