AWS Big Data

NOVEMBER 26, 2024

This post was co-written with Dipankar Mazumdar, Staff Data Engineering Advocate with AWS Partner OneHouse. Data architecture has evolved significantly to handle growing data volumes and diverse workloads. The synchronization process in XTable works by translating table metadata using the existing APIs of these table formats.

Metadata 105

Metadata Data Lake Snapshot Data Warehouse 105

AWS Big Data

JULY 28, 2023

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It is designed for analyzing large volumes of data and performing complex queries on structured and semi-structured data. Tags provide metadata about resources at a glance. However, this is beyond the scope of this post.

Snapshot 98

Snapshot Metadata Measurement Data Warehouse 98

Cloudera

NOVEMBER 15, 2024

With flexible schema and partitioning, Iceberg tables can scale to handle petabytes of data while compressing logs to save on storage costs. The metadata-driven approach ensures quick query planning so defenders don’t have to deal with slow processes when they need fast answers.

Analytics 59

Analytics Metadata Data-driven Snapshot 59

AWS Big Data

MARCH 25, 2024

Furthermore, data events are filtered, enriched, and transformed to a consumable format using a stream processor. The result is made available to the application by querying the latest snapshot. This allows the model to adapt to the latest changes in price and availability. versions).

Data Lake 121

Data Lake Unstructured Data Management Snapshot 121

Wayne Yaddow

MARCH 14, 2025

Snapshot testing augments debugging capabilities by recording past table states, facilitating the identification of unforeseen spikes, declines, or abnormalities before their effect on production systems. Data freshness propagation: No automatic tracking of data propagation delays across multiplemodels.

Data Transformation 52

Data Transformation Testing Unstructured Data Data Quality 52

AWS Big Data

AUGUST 16, 2023

We fetch the metadata of the users_xxxxxx table from Athena. The following are a few important considerations regarding how the Lambda function handles Iceberg table metadata changes: In this approach, target metadata takes precedence during DML operations. It’s imperative that the source and target metadata match.

Data Lake 96

Data Lake Metadata Testing Snapshot 96

Cloudera

NOVEMBER 15, 2024

With flexible schema and partitioning, Iceberg tables can scale to handle petabytes of data while compressing logs to save on storage costs. The metadata-driven approach ensures quick query planning so defenders don’t have to deal with slow processes when they need fast answers.

Analytics 84

Analytics Metadata Data-driven Snapshot 84