AWS Big Data

OCTOBER 3, 2023

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights.

Data Lake 115

Data Lake Metadata Snapshot Recreation/Entertainment 115

AWS Big Data

DECEMBER 12, 2024

In the context of comprehensive data governance, Amazon DataZone offers organization-wide data lineage visualization using Amazon Web Services (AWS) services, while dbt provides project-level lineage through model analysis and supports cross-project integration between data lakes and warehouses.

Snapshot 78

Snapshot Recreation/Entertainment Experimentation Data Lake 78

AWS Big Data

JUNE 9, 2023

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

Data Lake 125

Data Lake Metadata Statistics Optimization 125

AWS Big Data

MARCH 4, 2024

As enterprises collect increasing amounts of data from various sources, the structure and organization of that data often need to change over time to meet evolving analytical needs. Schema evolution enables adding, deleting, renaming, or modifying columns without needing to rewrite existing data.

Snapshot 123

Snapshot Data Lake Metadata Recreation/Entertainment 123

AWS Big Data

JUNE 27, 2024

With built-in features such as automated snapshots and cross-Region replication, you can enhance your disaster resilience with Amazon Redshift. Amazon Redshift supports two kinds of snapshots: automatic and manual, which can be used to recover data. Snapshots are point-in-time backups of the Redshift data warehouse.

Snapshot 80

Snapshot Data Warehouse Data Processing Strategy 80

Domino Data Lab

JANUARY 11, 2021

With SQLAlchemy we must specify that we wish to either append results (as in write more results to the bottom of the file) or overwrite results (as in drop the table and recreate). data = pd.read_csv('/mnt/data/modelOut.csv') data.to_sql('modelOutput', engine, index = False, if_exists='append'). About Domino Data Lab.

Data Science 103

Data Science Recreation/Entertainment Data Warehouse Publishing 103

Cloudera

MARCH 23, 2022

Apache Iceberg snapshot and time-travel features can help analysts and auditors to easily look back in time and analyze the data with the simplicity of SQL. . Let’s say new data sources are identified for your project, and as a result new attributes need to be introduced into your existing data model.

Metadata 92

Metadata Data Architecture Machine Learning Cost-Benefit 92