AWS Big Data Blog

Category: Amazon DynamoDB

How Plugsurfing doubled performance and reduced cost by 70% with purpose-built databases and AWS Graviton

Plugsurfing aligns the entire car charging ecosystem—drivers, charging point operators, and carmakers—within a single platform. The over 1 million drivers connected to the Plugsurfing Power Platform benefit from a network of over 300,000 charging points across Europe. Plugsurfing serves charging point operators with a backend cloud software for managing everything from country-specific regulations to providing […]

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Accelerate Amazon DynamoDB data access in AWS Glue jobs using the new AWS Glue DynamoDB Export connector

Modern data architectures encourage the integration of data lakes, data warehouses, and purpose-built data stores, enabling unified governance and easy data movement. With a modern data architecture on AWS, you can store data in a data lake and use a ring of purpose-built data services around the lake, allowing you to make decisions with speed […]

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Optimize Federated Query Performance using EXPLAIN and EXPLAIN ANALYZE in Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. In 2019, Athena added support for federated queries to run SQL […]

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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, and Amazon Redshift as […]

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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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