AWS Partner Network (APN) Blog

Category: Amazon Kinesis

Building a Streaming Pipeline with Minimal Effort Using Amazon Kinesis and Qlik Talend

To collect massive amounts of time-critical data, setting up data streaming pipelines is essential. The combination of Qlik Talend data integration and Amazon Kinesis provides a complete solution for easily building, running, and maintaining streaming data pipelines with low operational overhead. Learn how Qlik Talend with Amazon Kinesis supporting Spark streaming enables an accessible, no-code methodology for building Spark streaming pipelines leveraging the power of AWS.

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Strategies, Patterns, and Security Measures for Integrating Infor CloudSuite with AWS

Infor OS provides deep integration capabilities and includes Intelligent Open Network (ION), which is an interoperability and business process management platform designed to integrate applications, processes, people, and data to run your business. Infor ION enables you to easily integrate your Infor and non-Infor enterprise systems, whether they’re on-premises, in the cloud, or both. In this post, we discuss general scenarios and integration patterns while using ION.

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Best Practices from Rackspace for Modernizing a Legacy HBase/Solr Architecture Using AWS Services

As technology advances and business requirements change, organizations may find themselves needing to migrate away from legacy data processing systems like HBase, Solr, and HBase Indexer. Explore the advantages of migrating from HBase, Solr, and HBase indexer to a modern data ecosystem based on AWS, and dive deep on the discuss architecture, design, and pathways for implementation. This post offers insights and guidance from Rackspace for those looking to embark on this intricate migration journey.

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Building a Serverless Stream Analytics Platform with Amazon Kinesis Data Firehose and MongoDB Realm

A serverless architecture strategy reduces complexity and provides more flexibility in adopting the features and non-functional requirements needed to support market agility. In this post, walk through an example of an IoT use case and build a serverless scalable platform using Amazon Kinesis Data Firehose, Amazon Managed Service for Apache Flink, and MongoDB Realm. You’ll learn how easy it is to develop mobile and desktop applications on top of the data platform for different personas.

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How Thundra Decreased Data Processing Pipeline Delay By 3x on Average and 6x on P99

It can be complicated to maintain a robust, scalable, and reliable monitoring system that inputs terabytes of data under heavy traffic. Learn how Thundra has delivered 99.9 percent availability to customers since incorporating AWS services into its product. Thundra’s platform can handle scalability and availability challenges, recover both from partial failures and major outages, and support point-in-time recovery in case of disaster.

Monitoring Your Palo Alto Networks VM-Series Firewall with a Syslog Sidecar

By hosting a Palo Alto Networks VM-Series firewall in an Amazon VPC, you can use AWS native cloud services—such as Amazon CloudWatch, Amazon Kinesis Data Streams, and AWS Lambda—to monitor your firewall for changes in configuration. This post explains why that’s desirable and walks you through the steps required to do it. You now have a way to monitor your Palo Alto Networks firewall that is very similar to how you monitor your AWS environment with AWS Config.

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How to Analyze and Action Device Data Using AWS IoT and Splunk

Splunk software collects, analyzes, and visualizes real-time and historical machine data from any source, enabling you to improve operations, ensure safety and compliance, perform predictive maintenance, and better manage the uptime and availability of industrial assets. Learn how to collect data from Internet of Things (IoT) devices using AWS IoT services and ingest that data into Splunk for meaningful and action-oriented analytics using Amazon Kinesis Data Firehose.

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How to Migrate Mainframe Batch to Cloud Microservices with AWS Blu Age

While modernizing customer mainframes, the team at AWS Blu Age discovered that Batch can be a complex aspect of a mainframe migration to AWS. It’s critical to design your AWS architecture to account for the key Batch stringent performance requirements such as intensive I/Os, large datasets, and short durations. Let’s explore how to migrate mainframe Batch to AWS microservices using AWS Blu Age automated transformation technology.