AWS Big Data Blog

Category: Intermediate (200)

Analyze Amazon S3 storage costs using AWS Cost and Usage Reports, Amazon S3 Inventory, and Amazon Athena

Since its launch in 2006, Amazon Simple Storage Service (Amazon S3) has experienced major growth, supporting multiple use cases such as hosting websites, creating data lakes, serving as object storage for consumer applications, storing logs, and archiving data. As the application portfolio grows, customers tend to store data from multiple application and different business functions […]

How Amazon Devices scaled and optimized real-time demand and supply forecasts using serverless analytics

Every day, Amazon devices process and analyze billions of transactions from global shipping, inventory, capacity, supply, sales, marketing, producers, and customer service teams. This data is used in procuring devices’ inventory to meet Amazon customers’ demands. With data volumes exhibiting a double-digit percentage growth rate year on year and the COVID pandemic disrupting global logistics […]

Visualize multivariate data using a radar chart in Amazon QuickSight

At AWS re:Invent 2022, we announced the general availability of two new Amazon QuickSight visuals: small multiples and text boxes. We are excited to add another new visual to QuickSight: radar charts. With radar charts, you can compare two or more items across multiple variables in QuickSight. In this post, we explore radar charts, its […]

Create more partitions and retain data for longer in your MSK Serverless clusters

In April 2022, Amazon Managed Streaming for Apache Kafka (Amazon MSK) launched an exciting new capability, Amazon MSK Serverless. Amazon MSK is a fully managed service for Apache Kafka that makes it easier for developers to build and run highly available, secure, and scalable applications based on Apache Kafka. With MSK Serverless, developers can run […]

Run Apache Spark workloads 3.5 times faster with Amazon EMR 6.9

In this post, we analyze the results from our benchmark tests running a TPC-DS application on open-source Apache Spark and then on Amazon EMR 6.9, which comes with an optimized Spark runtime that is compatible with open-source Spark. We walk through a detailed cost analysis and finally provide step-by-step instructions to run the benchmark. With Amazon EMR 6.9.0, you can now run your Apache Spark 3.x applications faster and at lower cost without requiring any changes to your applications. In our performance benchmark tests, derived from TPC-DS performance tests at 3 TB scale, we found the EMR runtime for Apache Spark 3.3.0 provides a 3.5 times (using total runtime) performance improvement on average over open-source Apache Spark 3.3.0.

Introducing native support for Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue for Apache Spark, Part 1: Getting Started

AWS Glue is a serverless, scalable data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources. AWS Glue provides an extensible architecture that enables users with different data processing use cases. A common use case is building data lakes on Amazon Simple Storage Service (Amazon S3) using AWS […]

Automate deployment and version updates for Amazon Kinesis Data Analytics applications with AWS CodePipeline

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Amazon Kinesis Data Analytics is the easiest way to transform and analyze streaming data in real time using Apache Flink. Customers are already using Kinesis Data Analytics […]

A dive into redBus’s data platform and how they used Amazon QuickSight to accelerate business insights

This post is co-authored with Girish Kumar Chidananda from redBus. redBus is one of the earliest adopters of AWS in India, and most of its services and applications are hosted on the AWS Cloud. AWS provided redBus the flexibility to scale their infrastructure rapidly while keeping costs extremely low. AWS has a comprehensive suite of services […]

Automate data lineage on Amazon MWAA with OpenLineage

In modern data architectures, datasets are combined across an organization using a variety of purpose-built services to unlock insights. As a result, data governance becomes a key component for data consumers and producers to know that their data-driven decisions are based on trusted and accurate datasets. One aspect of data governance is data lineage, which […]

Enable cross-account sharing with direct IAM principals using AWS Lake Formation Tags

With AWS Lake Formation, you can build data lakes with multiple AWS accounts in a variety of ways. For example, you could build a data mesh, implementing a centralized data governance model and decoupling data producers from the central governance. Such data lakes enable the data as an asset paradigm and unleash new possibilities with […]