AWS Big Data

OCTOBER 19, 2023

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure.

Data Lake 113

Data Lake Data Warehouse Visualization Snapshot 113

Domino Data Lab

JANUARY 11, 2021

Data Science works best with a high degree of data granularity when the data offers the closest possible representation of what happened during actual events – as in financial transactions, medical consultations or marketing campaign results. About Domino Data Lab. Integration Features.

Recreation/Entertainment 103

Recreation/Entertainment Data Science Data Warehouse Modeling 103

AWS Big Data

MAY 24, 2023

When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. You can use either the AWS Glue Data Catalog (recommended) or a Hive catalog for Iceberg tables.

Data Lake 85

Data Lake Snapshot Metadata Optimization 85

AWS Big Data

FEBRUARY 12, 2024

About Redshift and some relevant features for the use case Amazon Redshift is a fully managed, petabyte-scale, massively parallel data warehouse that offers simple operations and high performance. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools.

Data Warehouse 101

Data Warehouse Analytics Snapshot Cost-Benefit 101

AWS Big Data

JANUARY 24, 2023

The utility for cloning and experimentation is available in the open-sourced GitHub repository. This solution only replicates metadata in the Data Catalog, not the actual underlying data. This ensures that the data lake will still be functional in another Region if Lake Formation has an availability issue.

Data Architecture 67

Data Architecture Metadata Data Lake Snapshot 67

O'Reilly on Data

OCTOBER 19, 2021

It has far-reaching implications as to how such applications should be developed and by whom: ML applications are directly exposed to the constantly changing real world through data, whereas traditional software operates in a simplified, static, abstract world which is directly constructed by the developer. This approach is not novel.

IT 351

IT Testing Experimentation Software 351

Corinium

APRIL 25, 2019

Then when there is a breach, it comes as a shock, “wow, I didn’t even know that application had access to so much sensitive data”. Step One in any data security program should first be to discover and classify datasets that are sensitive, and know where that data is, and understand who really needs it to do their jobs.

Insurance 150

Insurance Risk IoT Cost-Benefit 150