Remove Data Architecture Remove Metadata Remove Raw Data
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5 Helpful Extract & Load Practices for High-Quality Raw Data

Meltano

ELT is becoming the default choice for data architectures and yet, many best practices focus primarily on “T”: the transformations. But the extract and load phase is where data quality is determined for transformation and beyond. “Raw data” sounds clear.

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1. Streamlining Membership Data Engineering at Netflix with Psyberg

Netflix Tech

The fact tables then feed downstream intraday pipelines that process the data hourly. Raw data for hours 3 and 6 arrive. Hour 6 data flows through the various workflows, while hour 3 triggers a late data audit alert. It leverages Iceberg metadata to facilitate processing incremental and batch-based data pipelines.

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Open, Interoperable Storage with Iceberg Tables, Now Generally Available

Snowflake

Metadata and evolution support : We’ve added structured-type schema evolution for flexibility as source systems or business reporting needs change. Get better Iceberg ecosystem interoperability with Primary Key information added to Iceberg table metadata.

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Data Vault Architecture, Data Quality Challenges, And How To Solve Them

Monte Carlo

Over the past several years, data warehouses have evolved dramatically, but that doesn’t mean the fundamentals underpinning sound data architecture needs to be thrown out the window. Data vault collects and organizes raw data as underlying structure to act as the source to feed Kimball or Inmon dimensional models.

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Data Cloud Deployment Framework: Architecture

Cloudyard

Read Time: 5 Minute, 16 Second As we know Snowflake has introduced latest badge “Data Cloud Deployment Framework” which helps to understand knowledge in designing, deploying, and managing the Snowflake landscape. Secondly, Define Business Rules : Develop the transformation on RAW data and include the Business logic.

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DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

A DataOps architecture is the structural foundation that supports the implementation of DataOps principles within an organization. It encompasses the systems, tools, and processes that enable businesses to manage their data more efficiently and effectively. As a result, they can be slow, inefficient, and prone to errors.

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Watch our video explaining how data engineering works.