containers-roadmap VS aws-lambda-java-libs

Compare containers-roadmap vs aws-lambda-java-libs and see what are their differences.


This is the public roadmap for AWS container services (ECS, ECR, Fargate, and EKS). (by aws)


Official mirror for interface definitions and helper classes for Java code running on the AWS Lambda platform. (by aws)
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containers-roadmap aws-lambda-java-libs
30 82
4,074 374
3.3% 2.4%
2.7 6.6
5 months ago 11 days ago
Shell C++
GNU General Public License v3.0 or later Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of containers-roadmap. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-04.


Posts with mentions or reviews of aws-lambda-java-libs. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-26.
  • Trigger Lambda Functions with event filtering
    1 project | | 28 Nov 2021
    AWS Lambda functions recently announced an enhancement with event-triggers for DynamoDB, Amazon SQS, Amazon Kinesis as event sources which makes it easier for event based Lambda function triggers to get invoked only based on the filter expression. You can read about the official announcement from AWS Blog post.
  • The Ultimate Guide to TypeScript Monorepos
    17 projects | | 26 Nov 2021
    AWS Lambda for backend development
  • AWS Lambda pricing explained with examples
    2 projects | | 22 Nov 2021
    AWS Lambda is charging its users by the number of requests for their functions and by the duration, which is the time the code needs to execute. When your code starts running in response to an event, AWS Lambda counts a request. It will charge the total number of requests across all of the functions used. Duration is calculated by when your code started executing until it returns or is terminated, rounded up to the closest 1 millisecond. The AWS Lambda pricing depends on the amount of memory the user used to allocate to the function.
  • Creating Serverless Websites with AWS, Bref, and PHP
    3 projects | | 19 Nov 2021
  • Using Lumigo to debug AWS Lambda timeouts
    2 projects | | 19 Nov 2021
    Most AWS Lambda functions don't perform CPU-intensive tasks that take a long time to complete. However, they often have to perform multiple input/output (I/O) operations in a single invocation. An invocation occurs when your application invokes a Lambda function. For example, they fetch data from an Amazon DynamoDB table, talk to third-party APIs such as Stripe, or communicate with other internal APIs in your application. Sometimes these I/O operations don't complete quickly, and your function times out while waiting for a response.
  • The Developer's Guide TO Building Notification Systems: Part 3 - Routing & Preferences
    1 project | | 18 Nov 2021
    At Courier, we use AWS Lambda. Since our usage tends to come in bursts, serverless technology allows us to adjust and scale for changes in demand throughout each day as well as handle asynchronous operations efficiently.
  • Creating an Async API using Postgres - Building a Chess Analysis App (Part 3)
    4 projects | | 17 Nov 2021
    There are a lot of ways to actually implement queues. There are products like Kafka or RabbitMQ. There are managed services like AWS SQS or AWS Kinesis. There are libraries like Celery which can be used with Redis or RabbitMQ. For the workers, you can use serverless functions like AWS Lambda or you can have a pool of servers waiting for work.
  • MLOps on AWS
    2 projects | | 16 Nov 2021
    Setting up a MLOps Pipeline Data science and analytics teams are often squeezed between increasing business expectations and sandbox environments evolving into complex solutions. This makes it challenging to transform data into solid answers for stakeholders consistently. The ML-based workloads should support the reproducibility in any machine learning pipeline, which is central to any MLOps solution. The ML-based workload implementation choice can directly impact the design and implementation of any MLOps solution. If the ML capabilities required by your use cases can be implemented using the AI services, then an MLOps solution is not required. For instance the business use case to track the sentiment of users from their social media content (tweets, facebook posts) which leverages AWS AI services like Comprehend and Translate to extract insights can be well supported with a minimal solution where an listener running on a Amazon EC2 instance ingesting tweets / posts and delivering them via Kinesis Data Firehose and storing the raw content in S3 bucket. Amazon S3 invokes an AWS Lambda function to analyze the raw tweets using Amazon Translate to translate non-English tweets into English, and Amazon Comprehend to use natural-language-processing (NLP) to perform entity extraction and sentiment analysis. A second Kinesis Data Firehose delivery stream loads the translated tweets and sentiment values into the sentiment prefix in the Amazon S3 bucket. A third delivery stream loads entities in the entities prefix using in the Amazon S3 bucket. This solution uses a data lake leveraging AWS Glue for data transformation, Amazon Athena for data analysis, and Amazon QuickSight for data visualization. AWS Glue Data Catalog contains a logical database which is used to organize the tables for the data on Amazon S3. Athena uses these table definitions to query the data stored on Amazon S3 and return the information to an Amazon QuickSight dashboard. On the other hand, if you use either the ML services or ML frameworks and infrastructure, we recommend you implement an MLOps solution regardless of the use case. The ML services stack’s ease of use and support for various use cases makes it desirable for implementing any ML-based pipeline. Also, since different model training and serving algorithms can alter aspects of the MLOps solution, there are three main options Amazon SageMaker provides when it comes to choosing your training algorithm:
  • Getting started with SNS and SQS
    1 project | | 14 Nov 2021
    SNS facilitates with publishers from external or triggered from AWS Services such as EventBridge or triggers from S3 object events, Lambda fns and many more or it could be involved programmatically with sns:publish which publish to a designated topic and all the subscribers to that specific topic receives the message.
  • Getting the most of AWS Lambda free compute - wrapper scripts
    3 projects | | 14 Nov 2021
    Since the service's inception, the AWS Lambda quickly rose to the status of the go-to tool in many developers' toolbelt. This course of events is entirely understandable. A versatile compute platform with per millisecond billing is a dream come true for developers alike.

What are some alternatives?

When comparing containers-roadmap and aws-lambda-java-libs you can also consider the following projects:

netshoot - a Docker + Kubernetes network trouble-shooting swiss-army container

kraken - P2P Docker registry capable of distributing TBs of data in seconds


promise.lua - Promises/A+ 1.1 implementation in Lua

juicefs - JuiceFS is a distributed POSIX file system built on top of Redis and S3.

eks-nvme-ssd-provisioner - EKS NVMe SSD provisioner for Amazon EC2 Instance Stores

ra-data-hasura - react-admin data provider for Hasura GraphQL Engine

k2tf - Kubernetes YAML to Terraform HCL converter

mongoose-json-patch - A utility for applying RFC6902 JSONPatch operations to mongoose models

visualjavascript - Visual Basic + MS Access + Javascript = Visual Javascript

amazon-ecs-agent - Amazon Elastic Container Service Agent

mdx - Markdown for the component era