AWS Big Data Blog

This post presents a simple approach to aggregating AWS WAF logs into a central data lake repository, which lets teams better analyze and understand their organization’s security posture. I walk through the steps to aggregate regional AWS WAF logs into a dedicated S3 bucket. I follow that up by demonstrating how you can use Amazon ES to visualize the log data. I also present an option to offload and process historical data using AWS Glue ETL. With the data collected in one place, I finally show you how you can use Amazon Athena and Amazon QuickSight to query historical data and extract business insights.

Amazon always works backwards from the customer’s needs. Customers have made strong requests that they want stored procedures in Amazon Redshift, to make it easier to migrate their existing workloads from legacy, on-premises data warehouses.

With that primary goal in mind, AWS chose to implement PL/pqSQL stored procedure to maximize compatibility with existing procedures and simplify migrations. In this post, we discuss how and where to use stored procedures to improve operational efficiency and security. We also explain how to use stored procedures with AWS Schema Conversion Tool.

In this blog post, I use this architecture to demonstrate the combined capabilities of Storage Gateway and Athena. AWS Storage Gateway is a hybrid storage service that enables your on-premises applications to seamlessly use AWS cloud storage. The File Gateway configuration of the AWS Storage Gateway offers you a seamless way to connect to the cloud in order to store application data files and backup images as durable objects on Amazon S3 cloud storage.

Combining the speed and flexibility of Amazon EMR with the utility and ubiquity of Apache Hive provides you with the best of both worlds. However, getting started with big data projects can feel intimidating. Whether you want to deploy new data on EMR or migrate an existing project, this post provides you with the basics to get started.

Amazon Athena is a serverless query engine for data on Amazon S3. Many customers use Athena to query application and service logs, schedule automated reports, and integrate with their applications, enabling new analytics-based capabilities. Different types of users rely on Athena, including business analysts, data scientists, security, and operations engineers. In this post, I show you how to use workgroups to separate workloads, control user access, and manage query usage and costs.

Modern data lakes depend on extract, transform, and load (ETL) operations to convert bulk information into usable data. This post walks through implementing an ETL orchestration process that is loosely coupled using AWS Step Functions, AWS Lambda, and AWS Batch to target an Amazon Redshift cluster.

In this post, I show you how to use AWS Glue to extract data from a Salesforce.com account object and save it to Amazon S3. You then use Amazon Athena to generate a report by joining the account object data from Salesforce.com with the orders data from a separate order management system.

On April 8, Amazon ES launched support for event monitoring and alerting. To use this feature, you work with monitors—scheduled jobs—that have triggers, which are specific conditions that you set, telling the monitor when it should send an alert. An alert is a notification that the triggering condition occurred. When a trigger fires, the monitor takes action, sending a message to your destination.

This post uses a simulated IoT device farm to generate and send data to Amazon ES.

If you are a developer or data scientist using long-running Amazon EMR clusters, you face fast-changing workloads. These changes often require different application configurations to run optimally on your cluster. With the reconfiguration feature, you can now change configurations on running EMR clusters. Starting with EMR release emr-5.21.0, this feature allows you to modify configurations […]

Building a data lake on Amazon S3 provides an organization with countless benefits. It allows you to access diverse data sources, determine unique relationships, build AI/ML models to provide customized customer experiences, and accelerate the curation of new datasets for consumption. However, capturing and loading continuously changing updates from operational data stores—whether on-premises or on […]