Category: Amazon Web Services
-
How Zepto Scales Millions Daily Orders with DynamoDB
Zepto transformed from a single warehouse to processing millions of daily orders by migrating from PostgreSQL to DynamoDB. Learn how they achieved 60% faster API performance, eliminated operational overhead, and scaled seamlessly from quiet periods to high-demand peaks using purpose-built database architecture.
-
Modernizing Batch Processing with Amazon Redshift ORDS Architecture
Discover how Vanguard modernized their legacy mainframe system using Amazon Redshift as an Operational Read-only Data Store (ORDS). Learn about their transition to cloud-native, domain-driven architecture while preserving critical batch processing capabilities and enabling cross-domain analytics with improved performance and scalability.
-
Real-Time Baggage Analytics Using AWS Kinesis Data Streams
IBM and AWS developed a framework to modernize airline baggage analytics using Amazon Kinesis Data Streams, DynamoDB, Apache Flink, and QuickSight. This serverless architecture delivers real-time insights, cost savings, enhanced scalability, and improved operational efficiency for airlines.
-
Capture Data Lineage from dbt, Apache Airflow, and Apache Spark with Amazon SageMaker
Amazon SageMaker now offers enhanced data lineage capabilities compatible with OpenLineage, allowing users to track data flow from tools like dbt, Apache Airflow, and Apache Spark. This integration creates transparency, builds trust, and centralizes governance of data assets in a single place.
-
Amazon PackScan: Revolutionizing Real-Time Sort Center Analytics with AWS Services
Discover how Amazon transformed its logistics operations with PackScan, an AWS-powered platform that reduced data latency from 1 hour to under 1 minute. This real-time analytics solution processes 500,000 scan events per second across 80 sort centers, resulting in 25% increased throughput and 12% reduction in labor hours.
-
Using Amazon Neptune for Real-time Anomaly Detection in Gaming Transactions
Discover how Zupee leveraged Amazon Neptune’s graph database to detect real-time anomalies in gaming wallet transactions. Learn how they overcame relational database limitations to build an integrity system that processes over 1 million daily transactions, identifies suspicious patterns, and ensures incentives reach legitimate users.
-
How Flutter UKI Optimized Data Pipelines with Amazon MWAA
Discover how Flutter UKI transformed their data pipelines by migrating from EC2-based Airflow to Amazon MWAA, managing 5,500 DAGs and 60,000 daily runs with improved stability and reduced operational overhead.
-
Scaling Apache Iceberg Tables with AWS Lake Formation Hybrid Access Mode
Apache Iceberg tables combined with AWS Lake Formation’s hybrid access mode provide a powerful solution for enterprises managing large datasets. This approach allows organizations to use Lake Formation for read access while maintaining IAM policy-based permissions for write operations, offering fine-grained access control without disrupting existing workflows.
-
Streamlining Cross-Account Orchestration with Amazon MWAA
Learn how to orchestrate data workflows across multiple AWS accounts and regions using Amazon Managed Workflows for Apache Airflow (MWAA). This article covers implementing secure cross-account access, creating custom Airflow operators, and following best practices for distributed data processing and machine learning pipelines.