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  1. Stackups
  2. Utilities
  3. Task Scheduling
  4. Workflow Manager
  5. Airflow vs Atlas-DB

Airflow vs Atlas-DB

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
Atlas-DB
Atlas-DB
Stacks6
Followers77
Votes0
GitHub Stars3.5K
Forks324

Airflow vs Atlas-DB: What are the differences?

# Introduction

1. **Architecture**:
   Airflow is a workflow management system that focuses on orchestrating and scheduling complex workflows, utilizing Directed Acyclic Graphs (DAGs) for task dependencies, while Atlas-DB is a distributed key-value store designed for scalability, with a focus on data storage and retrieval.

2. **Functionality**:
   Airflow offers features for workflow creation, automation, monitoring, and logging of tasks, providing a user-friendly interface, whereas Atlas-DB is optimized for read-heavy workloads and provides high availability, scalability, and low-latency access to data.

3. **Community Support**:
   Airflow has a larger and more active open-source community, offering a wide range of plugins, integrations, and continuous updates, while Atlas-DB, being more specialized, has a smaller community but provides robust support for its key-value storage capabilities.

4. **Use case**:
   Airflow is commonly used in data engineering and ETL (Extract, Transform, Load) pipelines, providing flexibility and extensibility for various data processing tasks, whereas Atlas-DB is ideal for applications requiring fast and reliable key-value lookups, such as caching layers or storing metadata.

5. **Scalability**:
   Airflow can be scaled horizontally by adding more worker nodes to handle increased workload and larger DAGs, while Atlas-DB offers horizontal scalability by allowing the addition of more nodes to distribute data and queries, maintaining consistent performance and availability.

6. **Data Model**:
   Airflow primarily focuses on orchestrating workflows and managing task dependencies through DAGs, while Atlas-DB is designed for storing and retrieving key-value pairs, with support for secondary indexes and strong consistency guarantees for data operations.

In Summary, Airflow and Atlas-DB differ in architecture, functionality, community support, use cases, scalability, and data model focus.

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Advice on Airflow, Atlas-DB

Anonymous
Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Comments

Detailed Comparison

Airflow
Airflow
Atlas-DB
Atlas-DB

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

Atlas was developed by Netflix to manage dimensional time series data for near real-time operational insight. Atlas features in-memory data storage, allowing it to gather and report very large numbers of metrics, very quickly.

Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Manages dimensional time series data; In-memory data storage; Captures operational intelligence
Statistics
GitHub Stars
-
GitHub Stars
3.5K
GitHub Forks
-
GitHub Forks
324
Stacks
1.7K
Stacks
6
Followers
2.8K
Followers
77
Votes
128
Votes
0
Pros & Cons
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 1
    Logical separation of DAGs is not straight forward
No community feedback yet

What are some alternatives to Airflow, Atlas-DB?

dbForge Studio for MySQL

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

dbForge Studio for Oracle

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

dbForge Studio for SQL Server

dbForge Studio for SQL Server

It is a powerful IDE for SQL Server management, administration, development, data reporting and analysis. The tool will help SQL developers to manage databases, version-control database changes in popular source control systems, speed up routine tasks, as well, as to make complex database changes.

Liquibase

Liquibase

Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.

Sequel Pro

Sequel Pro

Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

DBeaver

DBeaver

It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.

dbForge SQL Complete

dbForge SQL Complete

It is an IntelliSense add-in for SQL Server Management Studio, designed to provide the fastest T-SQL query typing ever possible.

Knex.js

Knex.js

Knex.js is a "batteries included" SQL query builder for Postgres, MySQL, MariaDB, SQLite3, and Oracle designed to be flexible, portable, and fun to use. It features both traditional node style callbacks as well as a promise interface for cleaner async flow control, a stream interface, full featured query and schema builders, transaction support (with savepoints), connection pooling and standardized responses between different query clients and dialects.

Flyway

Flyway

It lets you regain control of your database migrations with pleasure and plain sql. Solves only one problem and solves it well. It migrates your database, so you don't have to worry about it anymore.

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