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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data Tools
  5. Airflow vs Pachyderm

Airflow vs Pachyderm

OverviewDecisionsComparisonAlternatives

Overview

Pachyderm
Pachyderm
Stacks24
Followers95
Votes5
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Pachyderm: What are the differences?

  1. 1. Task Scheduling and Orchestration: Airflow is primarily used for task scheduling and orchestration, allowing users to define, schedule, and monitor complex workflows as Directed Acyclic Graphs (DAGs). Pachyderm, on the other hand, focuses on data versioning and data lineage, allowing users to track changes to data over time and reproduce data pipelines.
  2. 2. Data Versioning and Lineage: Pachyderm is specifically designed for data versioning and lineage, enabling users to track changes to data, reproduce past results, and debug data pipelines by examining metadata associated with each data version. Airflow does not provide built-in functionality for data versioning and lineage.
  3. 3. Data Storage and Processing: Airflow is agnostic to the backend data storage and processing framework used, allowing users to integrate with various popular storage and processing technologies such as Hadoop, Spark, and SQL databases. Pachyderm, on the other hand, provides its own storage and processing layer built on top of distributed file systems like HDFS or object stores like S3, and is optimized for large-scale data processing tasks.
  4. 4. Plugin Ecosystem: Airflow has a rich ecosystem of plugins that extend its functionality, allowing users to easily integrate with third-party tools and services for various tasks such as data extraction, transformation, loading, and monitoring. Pachyderm, being more focused on data versioning and lineage, does not have as extensive of a plugin ecosystem.
  5. 5. Language Support: Airflow allows users to define tasks using Python, making it highly flexible and customizable. Pachyderm, on the other hand, supports task definition using Docker containers, allowing users to execute tasks in any programming language as long as it is containerized.
  6. 6. Community and Adoption: Airflow has a large and active community of users and contributors, with widespread adoption in the data engineering and data science community. Pachyderm, being a relatively newer project, has a smaller community and adoption compared to Airflow.

In Summary, Airflow is primarily used for task scheduling and orchestration, while Pachyderm focuses on data versioning and lineage. Airflow has a more extensive plugin ecosystem and community adoption, while Pachyderm provides its own storage and processing layer and supports task definition using Docker containers.

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Advice on Pachyderm, Airflow

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.

294k views294k
Comments

Detailed Comparison

Pachyderm
Pachyderm
Airflow
Airflow

Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.

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.

Git-like File System;Dockerized MapReduce;Microservice Architecture;Deployed with CoreOS
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.
Statistics
Stacks
24
Stacks
1.7K
Followers
95
Followers
2.8K
Votes
5
Votes
128
Pros & Cons
Pros
  • 3
    Containers
  • 1
    Can run on GCP or AWS
  • 1
    Versioning
Cons
  • 1
    Recently acquired by HPE, uncertain future.
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 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
Integrations
Docker
Docker
Amazon EC2
Amazon EC2
Google Compute Engine
Google Compute Engine
Vagrant
Vagrant
No integrations available

What are some alternatives to Pachyderm, Airflow?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

GitHub Actions

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

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