Based on our record, DynamoDB should be more popular than Apache Airflow. It has been mentiond 104 times since March 2021. 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.
DynamoDB is a powerful NoSQL database provided by AWS, designed to handle large amounts of data efficiently. However, for newcomers, understanding the nuances of querying DynamoDB tables can be challenging, particularly when it comes to the differences between KeyConditionExpression and FilterExpression. This blog post aims to clarify these concepts and provide practical examples of their usage. - Source: dev.to / about 1 month ago
Event Producers: Generate streams of events, which can be implemented using straightforward microservices with AWS Lambda (for serverless computing), Amazon DynamoDB Streams (to captures changes to DynamoDB tables in real-time), Amazon S3 Event Notifications (Notify when certain events occur in S3 buckets) or AWS Fargate (a serverless compute engine for containers). - Source: dev.to / about 2 months ago
The first is AWS DynamoDB which is going to act as our NoSQL database for our project which we’re also going to pair with a Single-Table design architecture. - Source: dev.to / about 2 months ago
DynamoDB - 25GB NoSQL DB EC2 - 750 hours per month of t2.micro or t3.micro(12mo). 100GB egress per month. - Source: dev.to / 5 months ago
After two years, I moved to a Web3 startup where I was given a lead software engineer role. This new role gave me more hands-on experience with AWS, where I've learned to implement serverless technologies like Lambda and DynamoDB. - Source: dev.to / 6 months ago
Instead of the custom orchestrator I used, a proper orchestration tool should replace it like Apache Airflow, Dagster, ..., etc. - Source: dev.to / 4 days ago
An integral part of an ML project is data acquisition and data transformation into the required format. This involves creating ETL (extract, transform, load) pipelines and running them periodically. Airflow is an open source platform that helps engineers create and manage complex data pipelines. Furthermore, the support for Python programming language makes it easy for ML teams to adopt Airflow. - Source: dev.to / 15 days ago
Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities. - Source: dev.to / about 1 month ago
For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules. - Source: dev.to / 4 months ago
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows. Source: 8 months ago
AWS Lambda - Automatic, event-driven compute service
ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Microsoft Power Automate - Microsoft Power Automate is an automation platform that integrates DPA, RPA, and process mining. It lets you automate your organization at scale using low-code and AI.
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Make.com - Tool for workflow automation (Former Integromat)