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Apache Flink might be a bit more popular than AWS Elastic Load Balancing. We know about 27 links to it since March 2021 and only 22 links to AWS Elastic Load Balancing. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Use load balancers and distribute load accordingly to your redundant spring boot services. - Source: dev.to / about 1 month ago
• Amazon Elastic Container Service (Amazon ECS) is a fully managed container orchestration service that helps you easily deploy, manage, and scale containerized applications. • AWS Fargate is a serverless, pay-as-you-go compute engine that lets you focus on building applications without managing servers. AWS Fargate is compatible with both Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic... - Source: dev.to / 7 months ago
Elite Dangerous. With proper setup, AWS works great. And I am sure there are hundreds or even thousands of games using them, but not all are willing to share that information. Source: 11 months ago
Terraform templates can be used to automate the deployment and configuration of the Presentation Tier components. This Terraform code snippet can be used to deploy an AWS Elastic Load Balancer (ELB):. - Source: dev.to / 12 months ago
Assuming you already have two LBs (as above), most services will offer auto scaling of some description to handle this (e.g., https://aws.amazon.com/elasticloadbalancing/). Source: 12 months ago
Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / 21 days ago
Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago
Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 4 months ago
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago
Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 5 months ago
nginx - A high performance free open source web server powering busiest sites on the Internet.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Traefik - Load Balancer / Reverse Proxy
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Azure Traffic Manager - Microsoft Azure Traffic Manager allows you to control the distribution of user traffic for service endpoints in different datacenters.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.