AWS Database Blog

Category: Amazon Kinesis

Archive data from Amazon DynamoDB to Amazon S3 using TTL and Amazon Kinesis integration

In this post, we share how you can use Amazon Kinesis integration and the Amazon DynamoDB Time to Live (TTL) feature to design data archiving. Archiving old data helps reduce costs and meet regulatory requirements governing data retention or deletion policies. Amazon Kinesis Data Streams for DynamoDB captures item-level modifications in a DynamoDB table and […]

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Store and stream sports data feeds using Amazon DynamoDB and Amazon Kinesis Data Streams

Online bookmakers are innovating to offer their clients continuously updated sports data feeds that allow betting throughout the duration of matches. In this post, we walk through a solution to ingest, store, and stream sports data feeds in near real-time using Amazon API Gateway, Amazon DynamoDB, and Amazon Kinesis Data Streams. In betting, odds represent […]

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Build a fault-tolerant, serverless data aggregation pipeline with exactly-once processing

The business problem of real-time data aggregation is faced by customers in various industries like manufacturing, retail, gaming, utilities, and financial services. In a previous post, we discussed an example from the banking industry: real-time trade risk aggregation. Typically, financial institutions associate every trade that is performed on the trading floor with a risk value […]

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Build a near real-time data aggregation pipeline using a serverless, event-driven architecture

The collection, aggregation, and reporting of large volumes of data in near real time is a challenge faced by customers from many different industries, like manufacturing, retail, gaming, utilities, and financial services. In this post, we present a serverless aggregation pipeline in AWS. We start by defining the business problem, introduce a serverless architecture for […]

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Filter Amazon Aurora database activity stream data for segregation and monitoring

Most organizations need to monitor activity on databases containing sensitive information to ensure security auditing and compliance. Although some security operations teams might be interested in monitoring all activities like read, write, and logons, others might want to restrict monitoring to activities that lead to changes in data and data structures only. In this post, […]

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Near real-time processing with Amazon Kinesis, Amazon Timestream, and Grafana

As organizations adopt and deploy home-connected smart devices, they face challenges utilizing device telemetry data in narrow and broad contexts. Examples of such home-connected devices are smart meters and home sensors that emit telemetry and measurements as time series data. In a narrow context, operational teams use data to understand if devices are operating within […]

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Amazon QLDB data streaming via AWS CDK

Amazon Quantum Ledger Database (Amazon QLDB) is a fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log. You can use Amazon QLDB to track each application data change, and it maintains a complete and verifiable history of changes over time. Because of those key features, banking customers have adopted Amazon QLDB as a database […]

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How Zulily drives discovery shopping using Amazon Kinesis Data Analytics and Amazon DocumentDB

This is a guest post by Sergey Podlazov – Director of Engineering (Shopping Experience) at Zulily, Senthil Kumar, Sr. Solutions Architect, AWS, and Praveen Chamarthi, Sr. Technical Account Manager, AWS Zulily offers a unique ecommerce experience to shoppers by offering amazing deals on products for moms, kids, and babies. We have scaled this model to […]

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Creating Amazon Timestream interpolated views using Amazon Kinesis Data Analytics for Apache Flink

Many organizations have accelerated their adoption of stream data processing technologies in an effort to more quickly derive actionable insights from their data. Frequently, it is required that data from streams be computed into metrics or aggregations and stored in near real-time for analysis. These computed values should be generated and stored as quickly as […]

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Performing analytics on Amazon Managed Blockchain

Managed Blockchain follows an event-driven architecture. We can open up a wide range of analytic approaches by streaming events to Amazon Kinesis. For instance, we could analyze events in near-real time with Kinesis Data Analytics, perform petabyte scale data warehousing with Amazon RedShift, or use the Hadoop ecosystem with Amazon EMR. This allows us to use the right approach for every blockchain analytics use case.
In this post, we show you one approach that uses Amazon Kinesis Data Firehose to capture, monitor, and aggregate events into a dataset, and analyze it with Amazon Athena using standard SQL.

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