AWS Big Data Blog

Category: Amazon DynamoDB

Analyze data in Amazon DynamoDB using Amazon SageMaker for real-time prediction

I’ll describe how to read the DynamoDB backup file format in Data Pipeline, how to convert the objects in S3 to a CSV format that Amazon ML can read, and I’ll show you how to schedule regular exports and transformations using Data Pipeline.

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How to retain system tables’ data spanning multiple Amazon Redshift clusters and run cross-cluster diagnostic queries

In this blog post, I present a solution that exports system tables from multiple Amazon Redshift clusters into an Amazon S3 bucket. This solution is serverless, and you can schedule it as frequently as every five minutes. The AWS CloudFormation deployment template that I provide automates the solution setup in your environment. The system tables’ data in the Amazon S3 bucket is partitioned by cluster name and query execution date to enable efficient joins in cross-cluster diagnostic queries.

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Building a Real World Evidence Platform on AWS

Deriving insights from large datasets is central to nearly every industry, and life sciences is no exception. To combat the rising cost of bringing drugs to market, pharmaceutical companies are looking for ways to optimize their drug development processes. They are turning to big data analytics to better quantify the effect that their drug compounds […]

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Analysis of Top-N DynamoDB Objects using Amazon Athena and Amazon QuickSight

If you run an operation that continuously generates a large amount of data, you may want to know what kind of data is being inserted by your application. The ability to analyze data intake quickly can be very valuable for business units, such as operations and marketing. For many operations, it’s important to see what […]

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Near Zero Downtime Migration from MySQL to DynamoDB

Many companies consider migrating from relational databases like MySQL to Amazon DynamoDB, a fully managed, fast, highly scalable, and flexible NoSQL database service. For example, DynamoDB can increase or decrease capacity based on traffic, in accordance with business needs. The total cost of servicing can be optimized more easily than for the typical media-based RDBMS. […]

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Data Lake Ingestion: Automatically Partition Hive External Tables with AWS

In this post, I introduce a simple data ingestion and preparation framework based on AWS Lambda, Amazon DynamoDB, and Apache Hive on EMR for data from different sources landing in S3. This solution lets Hive pick up new partitions as data is loaded into S3 because Hive by itself cannot detect new partitions as data lands.

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Monitor Your Application for Processing DynamoDB Streams

In this post, I suggest ways you can monitor the Amazon Kinesis Client Library (KCL) application you use to process DynamoDB Streams to quickly track and resolve issues or failures so you can avoid losing data. Dashboards, metrics, and application logs all play a part. This post may be most relevant to Java applications running on Amazon EC2 instances.

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