3 end-to-end Data Architecture diagrams for you

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Build a Scalable Event-Based GCP Data Pipeline using DataFlow

There is a continual need to give huge volumes of data to teams in a data-driven company. Many tools are available to assist you with your requirements and wants. Choosing the appropriate mechanism may be complicated and overwhelming at times. The key idea to remember is that there is no one-size-fits-all tool or architecture, and it all depends on your needs. Click here to read project details.

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Build an AWS ETL Data Pipeline in Python on YouTube Data

A data pipeline is a technique for transferring data from one system to another. The data may or may not be updated, and it may be handled in real-time (or streaming) rather than in batches. The data pipeline encompasses everything from harvesting or acquiring data using various methods to storing raw data, cleaning, validating, and transforming data into a query-worthy format, displaying KPIs, and managing the above process.Click here to read project details.

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AWS Snowflake Data Pipeline Example using Kinesis and Airflow

The architecture of Snowflake is a mix of classic shared-disk and shared-nothing database designs. Snowflake employs a central data repository for persistent data, like shared-disk architectures, available from all compute nodes in the platform. Snowflake executes queries utilizing MPP (massively parallel processing) compute clusters, wherein each node in the cluster stores a part of the whole data set locally, like shared-nothing architectures. This method combines the simplicity of a shared-disk design with the speed and scale-out advantages of a shared-nothing architecture.Click here to read project details.

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