Remove Data Architecture Remove Metadata Remove Structured Data
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How Apache Iceberg Is Changing the Face of Data Lakes

Snowflake

Data storage has been evolving, from databases to data warehouses and expansive data lakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew.

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The Future Is Hybrid Data, Embrace It

Cloudera

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

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Announcing New Innovations for Data Warehouse, Data Lake, and Data Lakehouse in the Data Cloud 

Snowflake

And, since historically tools and commercial platforms were often designed to align with one specific architecture pattern, organizations struggled to adapt to changing business needs – which of course has implications on data architecture. The schema of semi-structured data tends to evolve over time.

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How Cloudera Data Flow Enables Successful Data Mesh Architectures

Cloudera

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

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Hands-On Introduction to Delta Lake with (py)Spark

Towards Data Science

In this context, data management in an organization is a key point for the success of its projects involving data. One of the main aspects of correct data management is the definition of a data architecture. show() The history object is a Spark Data Frame. delta_table.history().select("version",

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The Symbiotic Relationship Between AI and Data Engineering

Ascend.io

Read More: AI Data Platform: Key Requirements for Fueling AI Initiatives How Data Engineering Enables AI Data engineering is the backbone of AI’s potential to transform industries , offering the essential infrastructure that powers AI algorithms.

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5 Reasons Data Discovery Platforms Are Best For Data Lakes

Monte Carlo

Data Catalogs Can Drown in a Data Lake Although exceptionally flexible and scalable, data lakes lack the organization necessary to facilitate proper metadata management and data governance. Data discovery tools and platforms can help. Interested in learning how to scale data discovery across your data lake?