Remove Data Analytics Remove Data Architecture Remove Enterprise Remove Structured Data
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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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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.

IT 106
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The Future Is Hybrid Data, Embrace It

CIO Business Intelligence

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.

IT 92
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What is data governance? Best practices for managing data assets

CIO Business Intelligence

Whereas data governance is about the roles, responsibilities, and processes for ensuring accountability for and ownership of data assets, DAMA defines data management as “an overarching term that describes the processes used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control, and purge data.”

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Snowflake: A New Blueprint for the Modern Data Warehouse

CDW Research Hub

Modern companies are placing data analytics in the center of every activity—from applications to operations—and arming teams with the business intelligence and analytics tools they need to understand their businesses. To do so, these companies need a modern data warehouse, such as Snowflake.

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

It allows users to write data transformation code, run it, and test the output, all within the framework it provides. Use case The Enterprise Data Analytics group of a large jewelry retailer embarked on their cloud journey with AWS in 2021. It’s raw, unprocessed data straight from the source.

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Detect, mask, and redact PII data using AWS Glue before loading into Amazon OpenSearch Service

AWS Big Data

Solution overview The following diagram illustrates the high-level solution architecture. We have defined all layers and components of our design in line with the AWS Well-Architected Framework Data Analytics Lens. For change data capture (CDC) use cases, you can use Kinesis Data Streams as a target for AWS DMS.