AWS Big Data Blog

Category: Amazon DynamoDB

Featured Stateful Architecture

Doing more with less: Moving from transactional to stateful batch processing

Amazon processes hundreds of millions of financial transactions each day, including accounts receivable, accounts payable, royalties, amortizations, and remittances, from over a hundred different business entities. All of this data is sent to the eCommerce Financial Integration (eCFI) systems, where they are recorded in the subledger. Ensuring complete financial reconciliation at this scale is critical […]

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How ENGIE scales their data ingestion pipelines using Amazon MWAA

ENGIE—one of the largest utility providers in France and a global player in the zero-carbon energy transition—produces, transports, and deals electricity, gas, and energy services. With 160,000 employees worldwide, ENGIE is a decentralized organization and operates 25 business units with a high level of delegation and empowerment. ENGIE’s decentralized global customer base had accumulated lots […]

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Provide data reliability in Amazon Redshift at scale using Great Expectations library

Ensuring data reliability is one of the key objectives of maintaining data integrity and is crucial for building data trust across an organization. Data reliability means that the data is complete and accurate. It’s the catalyst for delivering trusted data analytics and insights. Incomplete or inaccurate data leads business leaders and data analysts to make […]

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How NortonLifelock built a serverless architecture for real-time analysis of their VPN usage metrics

This post presents a reference architecture and optimization strategies for building serverless data analytics solutions on AWS using Amazon Kinesis Data Analytics. In addition, this post shows the design approach that the engineering team at NortonLifeLock took to build out an operational analytics platform that processes usage data for their VPN services, consuming petabytes of […]

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Use ML predictions over Amazon DynamoDB data with Amazon Athena ML

Today’s modern applications use multiple purpose-built database engines, including relational, key-value, document, and in-memory databases. This purpose-built approach improves the way applications use data by providing better performance and reducing cost. However, the approach raises some challenges for data teams that need to provide a holistic view on top of these database engines, and especially […]

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Create a secure data lake by masking, encrypting data, and enabling fine-grained access with AWS Lake Formation

You can build data lakes with millions of objects on Amazon Simple Storage Service (Amazon S3) and use AWS native analytics and machine learning (ML) services to process, analyze, and extract business insights. You can use a combination of our purpose-built databases and analytics services like Amazon EMR, Amazon OpenSearch Service (successor to Amazon Elasticsearch […]

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Streaming Amazon DynamoDB data into a centralized data lake

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 […]

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Build seamless data streaming pipelines with Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose for Amazon DynamoDB tables

The global wearables market grew 35.1% year over year during the third quarter of 2020, with total shipments reaching 125 million units according to new data from the International Data Corporation (IDC) Worldwide Quarterly Wearable Device Tracker. The surge was driven by seasonality, new product launches, and the health concerns during the global pandemic. Given […]

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Build a data lake using Amazon Kinesis Data Streams for Amazon DynamoDB and Apache Hudi

Amazon DynamoDB helps you capture high-velocity data such as clickstream data to form customized user profiles and online order transaction data to develop customer order fulfillment applications, improve customer satisfaction, and get insights into sales revenue to create a promotional offer for the customer. It’s essential to store these data points in a centralized data […]

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The following architecture diagram illustrates the wind turbine protection system.

Building a real-time notification system with Amazon Kinesis Data Streams for Amazon DynamoDB and Amazon Kinesis Data Analytics for Apache Flink

Amazon DynamoDB helps you capture high-velocity data such as clickstream data to form customized user profiles and Internet of Things (IoT) data so that you can develop insights on sensor activity across various industries, including smart spaces, connected factories, smart packing, fitness monitoring, and more. It’s important to store these data points in a centralized […]

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