AWS Big Data

APRIL 3, 2024

They understand that a one-size-fits-all approach no longer works, and recognize the value in adopting scalable, flexible tools and open data formats to support interoperability in a modern data architecture to accelerate the delivery of new solutions. Snowflake can query across Iceberg and Snowflake table formats.

Data Lake 96

Data Lake Snapshot Metadata Data Architecture 96

Cloudera

FEBRUARY 22, 2022

Over the past decade, the successful deployment of large scale data platforms at our customers has acted as a big data flywheel driving demand to bring in even more data, apply more sophisticated analytics, and on-board many new data practitioners from business analysts to data scientists.

Snapshot 107

Snapshot Metadata Cost-Benefit Data Architecture 107

AWS Big Data

JULY 20, 2023

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. Expiring old snapshots – This operation provides a way to remove outdated snapshots and their associated data files, enabling Orca to maintain low storage costs.

Data Lake 90

Data Lake Analytics Snapshot Optimization 90

DataKitchen

AUGUST 8, 2023

Data Observability leverages five critical technologies to create a data awareness AI engine: data profiling, active metadata analysis, machine learning, data monitoring, and data lineage. This is especially true regarding our one-to-many, producer-to-consumer relationships on our data architecture.

Data Quality 130

Data Quality Testing Snapshot Reporting 130

Cloudera

MARCH 23, 2022

In fact, we recently announced the integration with our cloud ecosystem bringing the benefits of Iceberg to enterprises as they make their journey to the public cloud, and as they adopt more converged architectures like the Lakehouse. Figure 2: Apache Iceberg within Cloudera Data Platform. #5: 1: Multi-function analytics .

Metadata 90

Metadata Data Architecture Machine Learning Cost-Benefit 90

Cloudera

OCTOBER 11, 2022

This leads to the obvious question – how do you do data at scale ? Al needs machine learning (ML), ML needs data science. Data science needs analytics. And they all need lots of data. The challenge for AI is how to do data in all its complexity – volume, variety, velocity.

Snapshot 92

Snapshot Data Science Digital Transformation Metadata 92

AWS Big Data

MARCH 10, 2023

However, as data processing at scale solutions grow, organizations need to build more and more features on top of their data lakes. Additionally, the task of maintaining and managing files in the data lake can be tedious and sometimes complex.

Data Lake 105

Data Lake Sales Data Warehouse Snapshot 105