AWS Big Data

NOVEMBER 9, 2023

A modern data platform entails maintaining data across multiple layers, targeting diverse platform capabilities like high performance, ease of development, cost-effectiveness, and DataOps features such as CI/CD, lineage, and unit testing. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.

Data Warehouse 104

Data Warehouse Testing Data Quality Reporting 104

Cloudera

DECEMBER 21, 2021

Cloudera’s Shared Data Experience (SDX) provides all these capabilities allowing seamless data sharing across all the Data Services including CDE. We are excited to offer in Tech Preview this born-in-the-cloud table format that will help future proof data architectures at many of our public cloud customers.

Snapshot 115

Snapshot Data-driven Optimization Management 115

Peter James Thomas

OCTOBER 30, 2018

It may well be that one thing that a CDO needs to get going is a data transformation programme. This may purely be focused on cultural aspects of how an organisation records, shares and otherwise uses data. It may be to build a new (or a first) Data Architecture. It may be to introduce or expand Data Governance.

Marketing 51

Marketing Strategy Data Architecture Data Strategy 51

AWS Big Data

NOVEMBER 15, 2023

Each CDH dataset has three processing layers: source (raw data), prepared (transformed data in Parquet), and semantic (combined datasets). It is possible to define stages (DEV, INT, PROD) in each layer to allow structured release and test without affecting PROD.

Dashboards 94

Dashboards Analytics Metadata Data Warehouse 94

AWS Big Data

NOVEMBER 16, 2023

As with all AWS services, Amazon Redshift is a customer-obsessed service that recognizes there isn’t a one-size-fits-all for customers when it comes to data models, which is why Amazon Redshift supports multiple data models such as Star Schemas, Snowflake Schemas and Data Vault. Data Vault 2.0

Enterprise 83

Enterprise Data Warehouse Data Lake Optimization 83

Cloudera

JUNE 30, 2022

These tools empower analysts and data scientists to easily collaborate on the same data, with their choice of tools and analytic engines. No more lock-in, unnecessary data transformations, or data movement across tools and clouds just to extract insights out of the data.

Data Lake 84

Data Lake Data Architecture Metadata Data Warehouse 84

IBM Big Data Hub

JANUARY 18, 2023

Furthermore, data warehouse storage cannot support workloads like Artificial Intelligence (AI) or Machine Learning (ML), which require huge amounts of data for model training. For these workloads, data lake vendors usually recommend extracting data into flat files to be used solely for model training and testing purposes.

Data Lake 52

Data Lake Data Warehouse Data-driven Metadata 52