AWS Big Data

AUGUST 16, 2023

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. Iceberg captures metadata information on the state of datasets as they evolve and change over time. Choose Create.

Data Lake 78

Data Lake Metadata Snapshot Management 78

Alation

FEBRUARY 13, 2020

These are all good questions and a logical place to start your data cataloging journey. This brief definition makes several points about data catalogs—data management, searching, data inventory, and data evaluation—but all depend on the central capability to provide a collection of metadata.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

AWS Big Data

JANUARY 24, 2023

AWS Lake Formation helps with enterprise data governance and is important for a data mesh architecture. It works with the AWS Glue Data Catalog to enforce data access and governance. This solution only replicates metadata in the Data Catalog, not the actual underlying data.

Data Architecture 66

Data Architecture Metadata Data Lake Snapshot 66

Cloudera

AUGUST 3, 2021

Advanced analytics and enterprise data are empowering several overarching initiatives in supply chain risk reduction – improved visibility and transparency into all aspects of the supply chain balanced with data governance and security. . Improve Visibility within Supply Chains.

Analytics 111

Analytics Digital Transformation Forecasting Risk 111

Domino Data Lab

APRIL 3, 2019

Paco Nathan ‘s latest column dives into data governance. This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. In particular, here’s my Strata SF talk “Overview of Data Governance” presented in article form.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Cloudera

MARCH 6, 2018

Instead, they have separate data stores and inconsistent (if any) frameworks for data governance, management, and security. If catalog metadata and business definitions live with transient compute resources, they will be lost, requiring work to recreate later and making auditing impossible.

Analytics 40

Analytics Metadata Recreation/Entertainment Big Data 40

Octopai

JANUARY 31, 2021

The particular episode we recommend looks at how WeWork struggled with understanding their data lineage so they created a metadata repository to increase visibility. Agile Data. Another podcast we think is worth a listen is Agile Data. In terms of data lineage, they offer an excellent definition here. .

Data Governance 57

Data Governance Data Processing Data Quality Metadata 57

AWS Big Data

AUGUST 14, 2023

In the same way, overly restrictive data governance practices that either prevent data products from taking root at all, or pare them back too aggressively (deforestation), can over time create “data deserts” that drive both the producers and consumers of data within an organization to look elsewhere for their data needs.

Management 79

Management Risk Measurement Optimization 79

erwin

FEBRUARY 19, 2021

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. What Is Metadata? Harvest data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

Jet Global

JULY 2, 2019

HDInsight: When it comes to Big Data analysis, the open-source Hadoop framework remains one of the most popular options. Just like actual lakes, the long-term health of your organizational data lake depends on defending it from ‘pollution’—data governance is critical to ensure your data lake doesn’t become a data swamp.

Data Lake 73

Data Lake Data Warehouse Big Data Machine Learning 73

AWS Big Data

DECEMBER 21, 2023

To ingest the data, smava uses a set of popular third-party customer data platforms complemented by custom scripts. After the data lands in Amazon S3, smava uses the AWS Glue Data Catalog and crawlers to automatically catalog the available data, capture the metadata, and provide an interface that allows querying all data assets.

Data Lake 89

Data Lake Data Warehouse Data-driven B2B 89

Domino Data Lab

AUGUST 8, 2019

In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. First and foremost: there’s substantial overlap between what the scientific community is working toward for scholarly infrastructure and some of the current needs of data governance in industry.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

BI-Survey

FEBRUARY 6, 2020

In fact, most companies struggle with data-related problems , organizational issues or outdated business intelligence and data analytics infrastructures which ignore more current usage scenarios such as explorative analytics on big data sources. Fact-Based Decision-Making Requires Trusted And Managed Data.

Reporting 59

Reporting Optimization Enterprise Data Quality 59

erwin

OCTOBER 24, 2019

Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata , or the data about the data. Metadata Is the Heart of Data Intelligence.

Metadata 104

Metadata Management Data-driven Data Architecture 104

TDAN

JUNE 30, 2020

About a week ago, I was teaching a data modeling class, and an attendee asked me to explain the concept of a data catalog. Like a lot of hype-related terms in IT, there is more than one definition. However, I had recently read the book, The Data Catalog: Sherlock Holmes Data Sleuthing for Analytics by […].

Modeling 52

Modeling Analytics Data Architecture IT 52

erwin

MAY 27, 2020

What’s desperately needed is a way to understand the relationships and interconnections between so many entities in data sets in detail. Visualizing data from anywhere defined by its context and definition in a central model repository, as well as the rules for governing the use of those data elements, unifies enterprise data management.

Modeling 98

Modeling Metadata Data Governance Visualization 98

Alation

APRIL 14, 2022

Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Alation

MARCH 21, 2023

To fully realize data’s value, organizations in the travel industry need to dismantle data silos so that they can securely and efficiently leverage analytics across their organizations. What is big data in the travel and tourism industry? What are common data challenges for the travel industry?

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

Alation

JANUARY 25, 2022

Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. As a reminder, here’s Gartner’s definition of data fabric: “A design concept that serves as an integrated layer (fabric) of data and connecting processes. The data catalog is a foundational layer of the data fabric.

Metadata 52

Metadata IT Metrics Data-driven 52

IBM Big Data Hub

APRIL 11, 2024

In the context of AI, it can refer to the safety and ethics guardrails of AI tools and systems, policies concerning data access and model usage or the government-mandated regulation itself. Therefore, we see national and international guidelines address these overlapping and intersecting definitions in a variety of ways.

Risk 74

Risk Consulting Modeling Data Processing 74

erwin

SEPTEMBER 25, 2020

Therefore, the visual representation provided by a data model gives organizations the confidence to design their proposed systems and take them live. Data modeling is a critical component of metadata management , data governance and data intelligence. Increase agility in application development.

Modeling 111

Modeling Cost-Benefit Visualization Metadata 111

Alation

JANUARY 17, 2023

Data ingestion/integration services. Data orchestration tools. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means. How Did the Modern Data Stack Get Started? What Are the Benefits of a Modern Data Stack?

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

erwin

JUNE 27, 2019

From a business-outcome perspective, a data modeling tool is used to help organizations: Effectively manage and govern massive volumes of data. Consolidate and build applications with hybrid architectures, including traditional, Big Data, cloud and on premise.

Measurement 81

Measurement Modeling Unstructured Data Metadata 81

Alation

JULY 6, 2021

Common Data Governance Challenges. Every enterprise runs into data governance challenges eventually. Issues like data visibility, quality, and security are common and complex. Data governance is often introduced as a potential solution. And one enterprise alone can generate a world of data.

Data Governance 52

Data Governance Metadata Data Quality Risk 52

erwin

AUGUST 31, 2023

It delivers the ability to capture and unify the business and technical perspectives of data assets, enables effective collaboration between a variety of stakeholders, and delivers metadata-driven automation to accelerate the creation and maintenance of data sources on virtually any data management platform.

Data-driven 98

Data-driven Modeling Enterprise Structured Data 98

IBM Big Data Hub

JULY 5, 2023

This was, without a question, a significant departure from traditional analytic environments, which often meant vendor-lock in and the inability to work with data at scale. Another unexpected challenge was the introduction of Spark as a processing framework for big data. Comprehensive data security and data governance (i.e.

Data Lake 60

Data Lake Metadata Cost-Benefit Data Warehouse 60