Here’s the difference between Redshift and Google Cloud Dataflow. The comparison is based on pricing, deployment, business model, and other important factors.
Amazon Redshift is a fully managed petabyte scale data warehouse service. Redshift is designed for analytic workloads and connects to standard SQL based clients and business intelligence tools. Redshift delivers fast query and I/O performance for virtually any size dataset by using columnar storage technology and parallelizing and distributing queries across multiple nodes. Most common administrative tasks associated with provisioning, configuring, monitoring, backing up, and securing a data warehouse are automated.
Google Cloud Dataflow is a cloud-based data processing service for both batch and real-time data streaming applications. It enables developers to set up processing pipelines for integrating, preparing and analyzing large data sets, such as those found in Web analytics or big data analytics applications. The Cloud Dataflow software expands on earlier Google parallel processing projects, including MapReduce, which originated at the company. Cloud Dataflow is designed to bring to entire analytics pipelines the style of fast parallel execution that MapReduce brought to a single type of computational sort for batch processing jobs.
Overview | ||
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Categories | Data Warehouses | Data Streaming |
Stage | Late Stage | Late Stage |
Target Segment | Enterprise | Enterprise, Mid size |
Deployment | SaaS | SaaS |
Business Model | Commercial | Commercial |
Pricing | Freemium | Freemium |
Location | Seattle,WA, US | US |
Companies using it | ||
Contact info |