Alation

JUNE 2, 2022

In part one of this series, I discussed how data management challenges have evolved and how data governance and security have to play in such challenges, with an eye to cloud migration and drift over time. Data management is not yet a solved problem, but modern data management is leagues ahead of prior approaches.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

Corinium

APRIL 25, 2019

It definitely depends on the type of data, no one method is always better than the other. For a large volume of structured data, for example, a customer master or data warehouse, where there are many stakeholders in your organization who need to see different subsets, tokenization is generally better. Governance 101.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

AWS Big Data

MARCH 7, 2023

Analytics reference architecture for gaming organizations In this section, we discuss how gaming organizations can use a data hub architecture to address the analytical needs of an enterprise, which requires the same data at multiple levels of granularity and different formats, and is standardized for faster consumption.

Analytics 73

Analytics Data Warehouse Data Lake Metadata 73

Ontotext

MARCH 18, 2020

According to an article in Harvard Business Review , cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data. Finally, they combine classical technologies like data governance and data management with modern analytics.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

AWS Big Data

JULY 19, 2023

We’ve seen that there is a demand to design applications that enable data to be portable across cloud environments and give you the ability to derive insights from one or more data sources. With this connector, you can bring the data from Google Cloud Storage to Amazon S3.

Big Data 85

Big Data Software Consulting Unstructured Data 85

AWS Big Data

OCTOBER 20, 2023

We’ve seen a demand to design applications that enable data to be portable across cloud environments and give you the ability to derive insights from one or more data sources. With these connectors, you can bring the data from Azure Blob Storage and Azure Data Lake Storage separately to Amazon S3. Learn more in README.

Data Lake 86

Data Lake Big Data Consulting Data Warehouse 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 124

Metadata Cost-Benefit Enterprise Interactive 124

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. Data mapping involves identifying and documenting the flow of personal data in an organization.

Snapshot 85

Snapshot Metadata Measurement Data Warehouse 85

AWS Big Data

JANUARY 22, 2024

Discussions with users showed they were happier to have faster access to data in a simpler way, a more structured data organization, and a clear mapping of who the producer is. A lot of progress has been made to advance their data-driven culture (data literacy, data sharing, and collaboration across business units).

Data-driven 87

Data-driven Advertising Metadata Data Architecture 87

O'Reilly on Data

DECEMBER 19, 2018

For example, Jacek Laskowski describes how to extract a resilient distributed data set (RDD) lineage graph that describes a series of Spark transformations. This graph could be committed to a lineage tracking system, or even a more traditional version-control system, to document transformations that have been applied to the data.

Machine Learning 258

Machine Learning Software Metadata Testing 258

erwin

OCTOBER 24, 2019

Organizations are dealing with numerous data types and data sources that were never designed to work together and data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and with little thought for downstream integration.

Metadata 104

Metadata Management Data-driven Data Architecture 104

Ontotext

MARCH 8, 2023

Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs. We get this question regularly.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52