AWS Big Data

MARCH 7, 2024

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 115

Data Lake Analytics Dashboards Metrics 115

IBM Big Data Hub

FEBRUARY 2, 2023

Misconception 3: All data warehouse migrations are the same, irrespective of vendors While migrating to the cloud, CTOs often feel the need to revamp and “modernize” their entire technology stack – including moving to a new cloud data warehouse vendor. This enabled data-driven analytics at scale across the organization 4.

Data Warehouse 57

Data Warehouse Cost-Benefit Unstructured Data Data Architecture 57

Ontotext

DECEMBER 1, 2023

Most organisations are missing this ability to connect all the data together. from Q&A with Tim Berners-Lee ) Finally, Sumit highlighted the importance of knowledge graphs to advance semantic data architecture models that allow unified data access and empower flexible data integration.

Metadata 78

Metadata Sales Consulting Enterprise 78

AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 105

Data Lake Sales Data Warehouse Snapshot 105

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. To overcome these issues, Orca decided to build a data lake. By decoupling storage and compute, data lakes promote cost-effective storage and processing of big data.

Data Lake 86

Data Lake Analytics Snapshot Optimization 86

Cloudera

OCTOBER 7, 2021

Those decentralization efforts appeared under different monikers through time, e.g., data marts versus data warehousing implementations (a popular architectural debate in the era of structured data) then enterprise-wide data lakes versus smaller, typically BU-Specific, “data ponds”.

Metadata 126

Metadata Cost-Benefit Enterprise Interactive 126