Category: Learning Levels

Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. Using Spark SQL to run Hive workloads provides not only the simplicity of SQL-like queries but also taps into the exceptional speed and performance provided by Spark. Spark SQL is an Apache Spark module for structured data processing. One […]

Snapshot Management helps you create point-in-time backups of your domain using OpenSearch Dashboards, including both data and configuration settings (for visualizations and dashboards). You can use these snapshots to restore your cluster to a specific state, recover from potential failures, and even clone environments for testing or development purposes. In this post, we share how to use Snapshot Management to take automated snapshots using OpenSearch Service.

In this post, we show you some different options for handling large records within Kinesis Data Streams and the benefits and disadvantages of each approach. We provide some sample code for each option to help you get started with any of these approaches with your own workloads.

Amazon EMR Serverless provides a serverless runtime environment that simplifies the operation of analytics applications that use the latest open source frameworks, such as Apache Spark and Apache Hive. With EMR Serverless, you don’t have to configure, optimize, secure, or operate clusters to run applications with these frameworks. You can run analytics workloads at any scale with automatic […]

Data lakes have been gaining popularity for storing vast amounts of data from diverse sources in a scalable and cost-effective way. As the number of data consumers grows, data lake administrators often need to implement fine-grained access controls for different user profiles. They might need to restrict access to certain tables or columns depending on […]

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.

Customers face a challenge when distributing cloud resources between different teams running workloads such as development, testing, or production. The resource distribution challenge also occurs when you have different line-of-business users. The objective is not only to ensure sufficient resources be consistently available to production workloads and critical teams, but also to prevent adhoc jobs […]

In this post, we describe how Cognizant’s Data & Intelligence Toolkit (CDIT)- ETL Conversion Tool can help you automatically convert legacy ETL code to AWS Glue quickly and effectively. We also describe the main steps involved, the supported features, and their benefits.

New enhancements in Trino with Amazon EMR provide improved resiliency for running ETL and batch workloads on Spot Instances with reduced costs. This post showcases the resilience of Amazon EMR with Trino using fault-tolerant configuration to run long-running queries on Spot Instances to save costs. We simulate Spot interruptions on Trino worker nodes by using AWS Fault Injection Simulator (AWS FIS).

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. Over the years, data lakes on Amazon Simple Storage […]