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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Airflow vs Treasure Data

Airflow vs Treasure Data

OverviewDecisionsComparisonAlternatives

Overview

Treasure Data
Treasure Data
Stacks28
Followers44
Votes5
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Treasure Data: What are the differences?

Introduction:

Key differences between Airflow and Treasure Data:

  1. Architecture: Airflow is an open-source workflow automation and scheduling system that uses Directed Acyclic Graphs (DAGs) to define workflows, while Treasure Data is a cloud-based data platform that focuses on data collection, storage, and analysis. Airflow provides a centralized platform for workflow management, while Treasure Data offers a fully managed service for data processing and analytics.

  2. Use Cases: Airflow is commonly used for ETL (Extract, Transform, Load) processes, data pipeline management, and workflow automation, making it ideal for data engineering tasks. On the other hand, Treasure Data is more tailored towards data ingestion, storage, and analysis, making it suitable for organizations looking for a comprehensive data platform with built-in analytics capabilities.

  3. Scalability: Airflow can be scaled horizontally by adding more worker nodes to handle larger workloads and increased data processing requirements. In contrast, Treasure Data's cloud infrastructure allows for automatic scaling based on the volume of data being processed, ensuring that resources are efficiently utilized without manual intervention.

  4. Integration: Airflow has a robust ecosystem of integrations with various data sources, databases, and cloud services, making it easy to connect with existing tools and systems. Treasure Data also offers a wide range of integrations with data sources, analytics tools, and visualization platforms, enabling seamless data flow and analysis across different systems and applications.

  5. Monitoring and Alerts: Airflow provides built-in monitoring and alerting capabilities, allowing users to track the progress of workflows, get notified of failures, and troubleshoot issues in real-time. Treasure Data also offers monitoring and alerting features to track data ingestion, storage performance, and query execution, ensuring data reliability and operational efficiency.

  6. Cost Efficiency: Airflow is an open-source tool that can be deployed on-premises or in the cloud, providing cost-effective workflow management solutions for organizations of all sizes. Treasure Data, being a cloud-based platform, offers a pay-as-you-go pricing model based on data usage, making it a flexible and economical choice for companies looking to scale their data operations efficiently.

In Summary, Airflow and Treasure Data differ in terms of architecture, use cases, scalability, integration capabilities, monitoring/alerting features, and cost efficiency, catering to distinct needs in data workflow management and analytics.

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Advice on Treasure Data, 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

Treasure Data
Treasure Data
Airflow
Airflow

Treasure Data's Big Data as-a-Service cloud platform enables data-driven businesses to focus their precious development resources on their applications, not on mundane, time-consuming integration and operational tasks. The Treasure Data Cloud Data Warehouse service offers an affordable, quick-to-implement and easy-to-use big data option that does not require specialized IT resources, making big data analytics available to the mass market.

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.

Instant Integration- Using td-agent, you can start importing your data from existing log files, web and packaged applications right away.;Streaming or Batch?- You choose! Our data collection tool, td-agent, enables you to stream or batch your data to the cloud in JSON format.;Secure Upload- The connection between td-agent and the cloud is SSL-encrypted, ensuring secure transfer of your data.;Availability- Our best-in-class, multi-tenant architecture uses Amazon S3 to ensure 24x7 availability and automatic replication.;Columnar Database- Our columnar database not only delivers blinding performance, it also compresses data to 5 to 10 percent of its original size.;Schema Free- Unlike traditional databases – even cloud databases – Treasure Data allows you to change your data schema anytime.;SQL-like Query Language- Query your data using our SQL-like language.;BI Tools Connectivity- Treasure Data allows you to use your existing BI/visualization tools (e.g. JasperSoft, Pentaho, Talend, Indicee, Metric Insights) using our JDBC driver.;Enterprise-level Service and Support;No Lock-in- We provide a one-line command to let you export your data anywhere you choose, whenever you choose.
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
28
Stacks
1.7K
Followers
44
Followers
2.8K
Votes
5
Votes
128
Pros & Cons
Pros
  • 2
    Scaleability, less overhead
  • 2
    Makes it easy to ingest all data from different inputs
  • 1
    Responsive to our business requirements, great support
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
Integrations
Amazon EC2
Amazon EC2
G Suite
G Suite
Heroku
Heroku
Engine Yard Cloud
Engine Yard Cloud
Red Hat OpenShift
Red Hat OpenShift
cloudControl
cloudControl
No integrations available

What are some alternatives to Treasure Data, 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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