Category: Analytics

In this post, we explore how to transition from using Rockset to OpenSearch Service for your DynamoDB use-case effectively. To illustrate this integration, we consider a real-world example of a gaming company that tracks user interactions, such as in-game purchases and player scores, using DynamoDB. This data needs to be analyzed in real time to provide insights into user behavior, detect anomalies, and personalize the gaming experience.

In this post, we demonstrate how you can use Spring Cloud to interact with Amazon DynamoDB and capture table-level changes using Kinesis Data Streams through familiar Spring constructs. We run you through a basic implementation and configuration that will help you get started.

In this post, we demonstrate how to create a centralized monitoring solution using Database Activity Streams and Amazon OpenSearch Service to meet audit requirements. The solution enables the security team to gather audit data from several Kinesis data streams, enrich, process, and store it with retention to meet compliance requirements, and produce relevant alarms and dashboards.

In this post, we show how you can use AWS Database Migration Service (AWS DMS) to migrate relational data from Amazon RDS into compressed archives on Amazon S3. We discuss partitioning strategies for the resulting archive objects and how to use S3 Object Lock to protect the archive objects from modification. Lastly, we demonstrate how to query the archive objects using SQL syntax through Athena with seconds latency, even on large datasets.

A knowledge graph combines data from many sources and links related entities. Because a knowledge graph is a gathering place for connected data, we expect many of its entities to be similar. When we find that two entities are similar to each other, we can materialize that fact as a relationship between them. In this […]

In Part 1 of this series, we discussed the high-level architecture of multi-threaded full load and change data capture (CDC) settings to tune related parameters for better performance to replicate data to an Amazon Kinesis Data Streams target using AWS Database Migration Service (AWS DMS). In Part 2, we provided some examples of how we […]

In Part 1 of this series, we discussed the architecture of multi-threaded full load and change data capture (CDC) settings, and considerations and best practices for configuring various parameters when replicating data using AWS Database Migration Service (AWS DMS) from a relational database system to Amazon Kinesis Data Streams. In this post, we demonstrate the […]

AWS Database Migration Service (AWS DMS) makes it possible to replicate to Amazon Kinesis Data Streams from relational databases, data warehouses, NoSQL databases, and other types of data stores. You can use Kinesis data streams to collect and process large streams of data records in real time. Replicating data changes to a Kinesis data stream […]

At AWS, we have been making steady progress towards bringing our zero-ETL vision to life. With Amazon Aurora zero-ETL integration to Amazon Redshift, you can bring together the transactional data of Amazon Aurora with the analytics capabilities of Amazon Redshift. The integration helps you derive holistic insights across many applications, break data silos in your […]

At Amazon Web Services (AWS), we have been making steady progress towards bringing our zero-extract, transform, and load (ETL) vision to life. With Amazon Aurora zero-ETL integration to Amazon Redshift, you can bring together the transactional data of Amazon Aurora with the analytics capabilities of Amazon Redshift. The integration helps you derive holistic insights across […]