Google Cloud Composer Description
The managed features of Cloud Composer, along with its compatibility with Apache Airflow, enable you to concentrate on crafting, scheduling, and overseeing your workflows rather than worrying about resource provisioning. Its seamless integration with various Google Cloud products such as BigQuery, Dataflow, Dataproc, Datastore, Cloud Storage, Pub/Sub, and AI Platform empowers users to orchestrate their data pipelines effectively. You can manage your workflows from a single orchestration tool, regardless of whether your pipeline operates on-premises, in multiple clouds, or entirely within Google Cloud. This solution simplifies your transition to the cloud and supports a hybrid data environment by allowing you to orchestrate workflows that span both on-premises setups and the public cloud. By creating workflows that interconnect data, processing, and services across different cloud platforms, you can establish a cohesive data ecosystem that enhances efficiency and collaboration. Additionally, this unified approach not only streamlines operations but also optimizes resource utilization across various environments.
Google Cloud Composer Alternatives
Windocks
Windocks provides on-demand Oracle, SQL Server, as well as other databases that can be customized for Dev, Test, Reporting, ML, DevOps, and DevOps. Windocks database orchestration allows for code-free end to end automated delivery. This includes masking, synthetic data, Git operations and access controls, as well as secrets management. Databases can be delivered to conventional instances, Kubernetes or Docker containers.
Windocks can be installed on standard Linux or Windows servers in minutes. It can also run on any public cloud infrastructure or on-premise infrastructure. One VM can host up 50 concurrent database environments. When combined with Docker containers, enterprises often see a 5:1 reduction of lower-level database VMs.
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DataBuck
Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
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dbt
Version control, quality assurance, documentation, and modularity enable data teams to work together similarly to software engineering teams. It is crucial to address analytics errors with the same urgency as one would for bugs in a live product. A significant portion of the analytic workflow is still performed manually. Therefore, we advocate for workflows to be designed for execution with a single command. Data teams leverage dbt to encapsulate business logic, making it readily available across the organization for various purposes including reporting, machine learning modeling, and operational tasks. The integration of continuous integration and continuous deployment (CI/CD) ensures that modifications to data models progress smoothly through the development, staging, and production phases. Additionally, dbt Cloud guarantees uptime and offers tailored service level agreements (SLAs) to meet organizational needs. This comprehensive approach fosters a culture of reliability and efficiency within data operations.
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DataBahn
DataBahn is an advanced platform that harnesses the power of AI to manage data pipelines and enhance security, streamlining the processes of data collection, integration, and optimization from a variety of sources to various destinations. Boasting a robust array of over 400 connectors, it simplifies the onboarding process and boosts the efficiency of data flow significantly. The platform automates data collection and ingestion, allowing for smooth integration, even when dealing with disparate security tools. Moreover, it optimizes costs related to SIEM and data storage through intelligent, rule-based filtering, which directs less critical data to more affordable storage options. It also ensures real-time visibility and insights by utilizing telemetry health alerts and implementing failover handling, which guarantees the integrity and completeness of data collection. Comprehensive data governance is further supported by AI-driven tagging, automated quarantining of sensitive information, and mechanisms in place to prevent vendor lock-in. In addition, DataBahn's adaptability allows organizations to stay agile and responsive to evolving data management needs.
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Pricing
Pricing Starts At:
$0.074 per vCPU hour
Integrations
Company Details
Company:
Google
Year Founded:
1998
Headquarters:
United States
Website:
cloud.google.com/composer
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Product Details
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Web-Based
Types of Training
Training Docs
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Business Hours
Online Support
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