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  1. Stackups
  2. Application & Data
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  4. Big Data As A Service
  5. Airflow vs Xplenty

Airflow vs Xplenty

OverviewDecisionsComparisonAlternatives

Overview

Xplenty
Xplenty
Stacks12
Followers26
Votes2
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Xplenty: What are the differences?

<Write Introduction here>
  1. Scalability: Airflow is highly scalable as it allows users to easily scale their workflows by adding more worker nodes, whereas Xplenty has limitations on workflow scalability due to its cloud-based execution model.
  2. Customization: Airflow provides a higher level of customization with the ability to write custom plugins and operators, while Xplenty has a more restricted set of connectors and transformations for data processing.
  3. Community Support: Airflow has a large and active community offering extensive documentation, tutorials, and support forums, whereas Xplenty has a smaller community and limited resources for troubleshooting and assistance.
  4. Ecosystem Integration: Airflow seamlessly integrates with various external services and tools such as Kubernetes, AWS, and Slack, enabling users to build versatile data pipelines, while Xplenty has limited integrations and dependencies on its own platform for data processing.
  5. Dynamic Task Dependency: Airflow allows for dynamic task dependency configuration based on task outcomes and runtime conditions, providing more flexibility in workflow design, whereas Xplenty has a more static task dependency model with limited options for dynamic scheduling.
  6. Cost: Airflow is an open-source project with no licensing fees, making it cost-effective for organizations, whereas Xplenty is a paid service with subscription-based pricing, adding to the operational costs for data processing needs.

In Summary, Airflow and Xplenty differ in terms of scalability, customization, community support, ecosystem integration, dynamic task dependency, and cost.

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Advice on Xplenty, 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.

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Comments

Detailed Comparison

Xplenty
Xplenty
Airflow
Airflow

Read and process data from cloud storage sources such as Amazon S3, Rackspace Cloud Files and IBM SoftLayer Object Storage. Once done processing, Xplenty allows you to connect with Amazon Redshift, SAP HANA and Google BigQuery. You can also store processed data back in your favorite relational database, cloud storage or key-value store.

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.

Xplenty provides you with an visual, intuitive interface to design your ETL data flows; Xplenty lets you integrate data from a variety of data stores, such as Amazon RDS, MySQL, PostgreSQL, Microsoft SQL Server and MongoDB.; Read and process data from cloud storage sources such as Amazon S3, Rackspace Cloud Files and IBM SoftLayer Object Storage; Once done processing, Xplenty allows you to connect with Amazon Redshift, SAP HANA and Google BigQuery. You can also store processed data back in your favorite relational database, cloud storage or key-value store;Integrate semi-structured data with structured data. Our package designer makes it a snap for every data and BI user to write complex data flows for your flat files and JSON files on top of Hadoop without writing a single line of code.
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
12
Stacks
1.7K
Followers
26
Followers
2.8K
Votes
2
Votes
128
Pros & Cons
Pros
  • 2
    Simple, easy to integrate/process data without coding
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
Integrations
Amazon S3
Amazon S3
Compose
Compose
Rackspace Cloud Files
Rackspace Cloud Files
MongoLab
MongoLab
MongoSoup
MongoSoup
Heroku
Heroku
Amazon Redshift
Amazon Redshift
Amazon RDS
Amazon RDS
Google Cloud SQL
Google Cloud SQL
ClearDB
ClearDB
No integrations available

What are some alternatives to Xplenty, Airflow?

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

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.

Snowflake

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Apache Beam

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Stitch

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Zenaton

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

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