Remove 2011 Remove Data Warehouse Remove Visualization
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Google BigQuery Architecture for Data Engineers

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction Google’s BigQuery is an enterprise-grade cloud-native data warehouse. BigQuery was first launched as a service in 2010, with general availability in November 2011.

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Is Google BigQuery The Future Of Big Data Analytics?

Smart Data Collective

Google BigQuery is a service (within the Google Cloud platform (GCP)) implemented to collect and analyze big data (also known as a data warehouse). It was released in 2011 and praised for its serverless architecture that enables highly scalable and fast-provided structured query language (SQL) analytics. What is Big Data?”

Big Data 137
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A blazingly fast database in a data-driven world

IBM Big Data Hub

We founded MemSQL (the original name of SingleStore) in 2011. Around 2011, we worked with a hot gaming company with a real-time analytics use case to understand what their users were doing in the moment to optimize the gaming experience by monitoring how users interacted with the game.

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The Top Three Entangled Trends in Data Architectures: Data Mesh, Data Fabric, and Hybrid Architectures

Cloudera

In the data fabric implementation, the concepts in data mesh map to real-world artifacts in a data architecture. Corresponding to the data mesh example in Figure 4, D1, D2 are tables in a data warehouse. A2 is an app built as a spark job orchestrated to run when some data shows up.

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How The Cloud Made ‘Data-Driven Culture’ Possible | Part 1

BizAcuity

2011: IBM enters the cloud market with IBM SmartCloud. 2012: Amazon Redshift, the first of its kind cloud-based data warehouse service comes into existence. Fact: IBM built the world’s first data warehouse in the 1980’s. Microsoft also releases Power BI, a data visualization and business intelligence tool.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Most of the data management moved to back-end servers, e.g., databases. So we had three tiers providing a separation of concerns: presentation, logic, data. Note that data warehouse (DW) and business intelligence (BI) practices both emerged circa 1990. WhereHows is a DG project from LinkedIn, focused on big data.

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Data trust and the evolution of enterprise analytics in the age of AI

CIO Business Intelligence

For example, an analytics dashboard that correlates shipping data gaps in a logistics view could be correlated to quantities released for distribution in a warehouse. Data do not understand causes and effects; humans do. Still, the correlated relationship is not necessarily causal.