AWS Big Data Blog

A recurring business problem is achieving the ability to capture data in near-real time to act upon any significant event close to the moment it happens. For example, you may want to tap into a data stream and monitor any anomalies that need to be addressed immediately rather than during a nightly batch. Building these […]

We announced the preview of AWS Lake Formation transactions, cell-level and row-level security, and acceleration at AWS re: Invent 2020. In Parts 1 , 2, and 3 of this series, we explained how to set up governed tables, add streaming and batch data to them, and use ACID transactions. In this post, we focus on […]

With the new SUPER data type and the PartiQL language, Amazon Redshift expands data warehouse capabilities to natively ingest, store, transform, and analyze semi-structured data. Semi-structured data (such as weblogs and sensor data) fall under the category of data that doesn’t conform to a rigid schema expected in relational databases. It often contain complex values […]

Amazon Elasticsearch Service (Amazon ES) supports multiple instance types based on your use case. In 2021, AWS announced general purpose (M6g), compute optimized (C6g), and memory optimized (R6g, R6gd) instance types for Amazon ES version 7.9 or later powered by AWS Graviton2 processors, which delivers a major leap in capabilities and better price/performance improvement over […]

In this post, we briefly walk through the most common design patterns adapted by enterprises to build lake house solutions to support their business agility in a multi-tenant model using the AWS Lake Formation cross-account feature to enable a multi-account strategy for line of business (LOB) accounts to produce and consume data from your data […]

For organizations moving towards a serverless microservice approach, Amazon DynamoDB has become a preferred backend database due to its fully managed, multi-Region, multi-active durability with built-in security controls, backup and restore, and in-memory caching for internet-scale application. , which you can then use to derive near-real-time business insights. The data lake provides capabilities to business teams to plug in […]

Customers create business continuity plans and disaster recovery (DR) strategies to maximize resiliency for their applications, because downtime or data loss can result in losing revenue or halting operations. Ultimately, DR planning is all about enabling the business to continue running despite a Regional outage. This post explains how to make Apache Kafka resilient to […]

Amazon Kinesis Data Analytics Studio makes it easy to analyze streaming data in real time and build stream processing applications using standard SQL, Python, and Scala. With a few clicks on the AWS Management Console, you can launch a serverless notebook to query data streams and get results in seconds. Kinesis Data Analytics reduces the […]

Amazon Elasticsearch Service (Amazon ES) supports real-time anomaly detection, which uses machine learning (ML) to proactively detect anomalies in real-time streaming data. When used to analyze application logs, it can detect anomalies such as unusually high error rates or sudden changes in the number of requests. For example, a sudden increase in the number of […]

Customers are looking for ways to securely and cost-efficiently manage large volumes of sensitive data archival and deletion in their data lake by following regulations and data protection and privacy laws, such as GDPR, POPIA, and LGPD. This post describes a way to automatically identify sensitive data stored in your data lake within AWS, tag […]