Gcp Data Lake Reference Architecture

Google cloud s data lake powers any analysis on any type of data.

Gcp data lake reference architecture.

It offers high data quantity to increase analytic performance and native integration. The following diagram depicts a typical migrate for compute engine deployment with google cloud. You can ingest data from sources such as crm or point of sale pos. In this post we first discuss a layered component oriented logical architecture of modern analytics platforms and then present a reference architecture for building a serverless data platform that includes a data lake data processing pipelines and a consumption layer that enables several ways to analyze the data in the data lake without.

Data lake solution architecture on aws the solution uses aws cloudformation to deploy the infrastructure components supporting this data lake reference implementation. A cloud vpn or cloud interconnect connecting to a google cloud virtual private cloud. In most cases you do this data ingestion offline by using the bq command line tool api or web ui. Google cloud solutions architecture reference infrastructure modernization.

Smart analytics text text cloud solutions architecture reference. The following diagram shows a reference architecture for s 4hana in a centralized deployment model. It is a place to store every type of data in its native format with no fixed limits on account size or file. You can load data locally or from cloud storage.

On a shared virtual private cloud vpc and on a single project vpc. In a distributed deployment you can install the different components on different compute engine instances. A data lake architecture must be able to ingest varying volumes of data from different sources such as internet of things iot sensors clickstream activity on websites online transaction. This topic describes two reference architectures for installing pivotal platform on google cloud platform gcp.

Note that sap ascs pas wd and hana are all installed on the same instance. This empowers your teams to securely and cost effectively ingest store and analyze large volumes of diverse full fidelity data. This topic also outlines multiple networking variants for vpc deployment. Cloud storage is the recommended approach for big datasets or when you are considering building a data lake.

At its core this solution implements a data lake api which leverages amazon api gateway to provide access to data lake microservices aws lambda functions.