AWS Big Data Blog

Category: Thought Leadership

AWS named a Leader in IDC MarketScape: Worldwide Analytic Stream Processing Software 2024 Vendor Assessment

We’re thrilled to announce that AWS has been named a Leader in the IDC MarketScape: Worldwide Analytic Stream Processing Software 2024 Vendor Assessment (doc #US51053123, March 2024). We believe this recognition validates the power and performance of Apache Flink for real-time data processing, and how AWS is leading the way to help customers build and […]

Achieve peak performance and boost scalability using multiple Amazon Redshift serverless workgroups and Network Load Balancer

As data analytics use cases grow, factors of scalability and concurrency become crucial for businesses. Your analytic solution architecture should be able to handle large data volumes at high concurrency and without compromising speed, thereby delivering a scalable high-performance analytics environment. Amazon Redshift Serverless provides a fully managed, petabyte-scale, auto scaling cloud data warehouse to […]

Power analytics as a service capabilities using Amazon Redshift

Analytics as a service (AaaS) is a business model that uses the cloud to deliver analytic capabilities on a subscription basis. This model provides organizations with a cost-effective, scalable, and flexible solution for building analytics. The AaaS model accelerates data-driven decision-making through advanced analytics, enabling organizations to swiftly adapt to changing market trends and make […]

Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

We’re living in the age of real-time data and insights, driven by low-latency data streaming applications. Today, everyone expects a personalized experience in any application, and organizations are constantly innovating to increase their speed of business operation and decision making. The volume of time-sensitive data produced is increasing rapidly, with different formats of data being […]

Synchronous enrichment performance

Implement Apache Flink real-time data enrichment patterns

You can use several approaches to enrich your real-time data in Amazon Managed Service for Apache Flink depending on your use case and Apache Flink abstraction level. Each method has different effects on the throughput, network traffic, and CPU (or memory) utilization. For a general overview of data enrichment patterns, refer to Common streaming data enrichment patterns in Amazon Managed Service for Apache Flink. This post covers how you can implement data enrichment for real-time streaming events with Apache Flink and how you can optimize performance. To compare the performance of the enrichment patterns, we ran performance testing based on synthetic data. The result of this test is useful as a general reference. It’s important to note that the actual performance for your Flink workload will depend on various and different factors, such as API latency, throughput, size of the event, and cache hit ratio.

Unstructured Data Management - AWS Native Architecture

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.

Automated data governance with AWS Glue Data Quality, sensitive data detection, and AWS Lake Formation

Data governance is the process of ensuring the integrity, availability, usability, and security of an organization’s data. Due to the volume, velocity, and variety of data being ingested in data lakes, it can get challenging to develop and maintain policies and procedures to ensure data governance at scale for your data lake. In this post, we showcase how to use AWS Glue with AWS Glue Data Quality, sensitive data detection transforms, and AWS Lake Formation tag-based access control to automate data governance.

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 […]

Optimize checkpointing in your Amazon Managed Service for Apache Flink applications with buffer debloating and unaligned checkpoints – Part 2

February 2024: This post was reviewed and updated for accuracy. This post is a continuation of a two-part series. In the first part, we delved into Apache Flink‘s internal mechanisms for checkpointing, in-flight data buffering, and handling backpressure. We covered these concepts in order to understand how buffer debloating and unaligned checkpoints allow us to […]

Optimize checkpointing in your Amazon Managed Service for Apache Flink applications with buffer debloating and unaligned checkpoints – Part 1

This post is the first of a two-part series regarding checkpointing mechanisms and in-flight data buffering. In this first part, we explain some of the fundamental Apache Flink internals and cover the buffer debloating feature. In the second part, we focus on unaligned checkpoints. Apache Flink is an open-source distributed engine for stateful processing over […]