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Description
Managed Service for Apache Airflow is a cloud-based workflow orchestration service that simplifies the creation and management of complex data pipelines. Built on the open-source Apache Airflow framework, it allows users to define workflows using Python-based DAGs. The platform is fully managed, removing the need to provision or maintain infrastructure, which helps teams focus on pipeline development and execution. It integrates with a wide range of Google Cloud services, including BigQuery, Dataflow, Cloud Storage, and Managed Service for Apache Spark. The service supports hybrid and multi-cloud environments, enabling organizations to orchestrate workflows across different platforms. It offers advanced monitoring and troubleshooting tools, including visual workflow representations and logs. New features such as DAG versioning and improved scheduling enhance reliability and control. The platform also supports CI/CD pipelines and DevOps automation use cases. Its open-source foundation ensures flexibility and avoids vendor lock-in. Overall, it provides a powerful and scalable solution for managing data workflows and automation processes.
Description
Discover the transformative capabilities of large language models as they redefine Natural Language Processing (NLP) through Spark NLP, an open-source library that empowers users with scalable LLMs. The complete codebase is accessible under the Apache 2.0 license, featuring pre-trained models and comprehensive pipelines. As the sole NLP library designed specifically for Apache Spark, it stands out as the most widely adopted solution in enterprise settings. Spark ML encompasses a variety of machine learning applications that leverage two primary components: estimators and transformers. Estimators possess a method that ensures data is secured and trained for specific applications, while transformers typically result from the fitting process, enabling modifications to the target dataset. These essential components are intricately integrated within Spark NLP, facilitating seamless functionality. Pipelines serve as a powerful mechanism that unites multiple estimators and transformers into a cohesive workflow, enabling a series of interconnected transformations throughout the machine-learning process. This integration not only enhances the efficiency of NLP tasks but also simplifies the overall development experience.
API Access
Has API
API Access
Has API
Integrations
Python
Apache Airflow
Apache Spark
Conda
Databricks
Facebook
Google Cloud AI Infrastructure
Google Cloud BigQuery
Google Cloud Dataflow
Google Cloud Pub/Sub
Integrations
Python
Apache Airflow
Apache Spark
Conda
Databricks
Facebook
Google Cloud AI Infrastructure
Google Cloud BigQuery
Google Cloud Dataflow
Google Cloud Pub/Sub
Pricing Details
$0.074 per vCPU hour
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Founded
1998
Country
United States
Website
cloud.google.com/products/managed-service-for-apache-airflow
Vendor Details
Company Name
John Snow Labs
Country
United States
Website
sparknlp.org
Product Features
Product Features
Natural Language Processing
Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization