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  5. Airflow vs Cloudera Enterprise

Airflow vs Cloudera Enterprise

OverviewDecisionsComparisonAlternatives

Overview

Cloudera Enterprise
Cloudera Enterprise
Stacks126
Followers172
Votes5
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Cloudera Enterprise: What are the differences?

Introduction:

Airflow and Cloudera Enterprise are both popular tools used in the field of data engineering and data management. While they share some similarities, there are several key differences between the two.

  1. Scalability: Airflow is designed to be highly scalable and can handle large-scale data pipelines with ease. It allows for the execution of tasks in parallel, making it suitable for processing massive amounts of data. On the other hand, Cloudera Enterprise is more focused on providing a comprehensive data management platform that includes storage, processing, and analytics capabilities. While it can handle large amounts of data, its scalability may be limited compared to Airflow.

  2. Workflow orchestration: Airflow is primarily used for orchestrating and managing workflows. It allows users to define complex workflows and dependencies between tasks in a clear and intuitive way. Cloudera Enterprise, on the other hand, is not specifically designed for workflow orchestration and may require additional tools or configurations to achieve similar functionality.

  3. DAG visualization: Airflow provides a built-in graphical user interface (GUI) for visualizing and monitoring Directed Acyclic Graphs (DAGs), which are used to represent workflows. This GUI allows users to easily understand the structure of their workflows and monitor the progress of each task. Cloudera Enterprise may not offer the same level of DAG visualization out of the box and may require additional tools or customizations.

  4. Ecosystem and integrations: Airflow has a vibrant and active open-source community, which has contributed to a rich ecosystem of plugins and integrations with various technologies and platforms. This allows users to easily integrate Airflow with their existing systems and leverage the capabilities of different tools. Cloudera Enterprise, on the other hand, is tightly integrated with Cloudera's own ecosystem of products and may not have the same level of flexibility when it comes to integrations with other technologies.

  5. Data processing capabilities: While both Airflow and Cloudera Enterprise provide capabilities for data processing, they have different approaches. Airflow focuses on the orchestration and management of data processing workflows, allowing users to schedule and monitor the execution of tasks. Cloudera Enterprise, on the other hand, provides a more comprehensive data processing platform with built-in tools and frameworks like Apache Hadoop, Spark, and Hive. It offers a wider range of data processing capabilities but may require more setup and configuration compared to Airflow.

  6. Community and support: Airflow benefits from a strong and active open-source community, which means users have access to a wealth of resources, documentation, and community support. Cloudera Enterprise, being a commercially-backed platform, provides dedicated support and consulting services, which can be beneficial for organizations that require enterprise-level support.

In summary, Airflow and Cloudera Enterprise differ in terms of scalability, workflow orchestration capabilities, DAG visualization, ecosystem and integrations, data processing approaches, and community/support options.

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Advice on Cloudera Enterprise, 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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Detailed Comparison

Cloudera Enterprise
Cloudera Enterprise
Airflow
Airflow

Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.

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.

Unified – one integrated system, bringing diverse users and application workloads to one pool of data on common infrastructure; no data movement required;Secure – perimeter security, authentication, granular authorization, and data protection;Governed – enterprise-grade data auditing, data lineage, and data discovery;Managed – native high-availability, fault-tolerance and self-healing storage, automated backup and disaster recovery, and advanced system and data management;Open – Apache-licensed open source to ensure your data and applications remain yours, and an open platform to connect with all of your existing investments in technology and skills
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
126
Stacks
1.7K
Followers
172
Followers
2.8K
Votes
5
Votes
128
Pros & Cons
Pros
  • 1
    Hybrid cloud
  • 1
    Cheeper
  • 1
    Easily management
  • 1
    Scalability
  • 1
    Multicloud
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward

What are some alternatives to Cloudera Enterprise, 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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