AWS Big Data

MARCH 7, 2023

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

Analytics 72

Analytics Data Warehouse Data Lake Metadata 72

AWS Big Data

JUNE 19, 2023

Security Lake automatically centralizes security data from cloud, on-premises, and custom sources into a purpose-built data lake stored in your account. With Security Lake, you can get a more complete understanding of your security data across your entire organization.

Publishing 87

Publishing Dashboards Visualization Management 87

AWS Big Data

JULY 28, 2023

To successfully respond to a data subject’s requests, organizations should have a clear strategy to determine how data is forgotten, flagged, anonymized, or deleted, and they should have clear guidelines in place for data audits. Data mapping involves identifying and documenting the flow of personal data in an organization.

Snapshot 84

Snapshot Metadata Measurement Data Warehouse 84

AWS Big Data

NOVEMBER 16, 2023

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

Enterprise 86

Enterprise Data Warehouse Snapshot Cost-Benefit 86

AWS Big Data

AUGUST 28, 2023

OpenSearch Ingestion reads Parquet formatted security data from the Security Lake managed Amazon S3 bucket and transforms the security logs into JSON documents. OpenSearch Ingestion ingests this OCSF compliant data into OpenSearch Service. For more information, refer to Lifecycle management in Security Lake.

Dashboards 52

Dashboards Visualization Metadata Management 52

Alation

FEBRUARY 20, 2020

The result, as many industry observers have put it, is that many data lakes become data swamps. New data visualization user interfaces from Tableau and Qlik proved that any business user can analyze their own data. Disruptive Trend #2: Self-Service Analytics.

Data Lake 52

Data Lake Metadata Structured Data Big Data 52

Ontotext

MARCH 8, 2023

Rich metadata and semantic modeling continue to drive the matching of 50K training materials to specific curricula, leading new, data-driven, audience-based marketing efforts that demonstrate how the recommender service is achieving increased engagement and performance from over 2.3 million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Domino Data Lab

APRIL 3, 2019

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. in lieu of simply landing in a data lake.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

AWS Big Data

OCTOBER 25, 2023

Text, images, audio, and videos are common examples of unstructured data. Most companies produce and consume unstructured data such as documents, emails, web pages, engagement center phone calls, and social media. Therefore, there is a need to being able to analyze and extract value from the data economically and flexibly.

Unstructured Data 121

Unstructured Data Metadata Management Analytics 121

Domino Data Lab

AUGUST 8, 2019

The second rule of Foo Camp concerns a practice called “FrieNDA” where people speak candidly and others respect that without either (1) “Oh gosh I must live tweet stream this amazing insight” or (2) anybody having to sign legal documents. The gist is, leveraging metadata about research datasets, projects, publications, etc.,

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Alation

JULY 19, 2022

Get the data. Explore the data. Model the data. Communicate and visualize the results. A data catalog can assist directly with every step, but model development. How Data Catalogs Help Data Scientists Ask Better Questions. Communicate and Visualize Results.

Metadata 59

Metadata Data Quality Statistics Data Science 59

Alation

APRIL 14, 2022

In today’s AI/ML-driven world of data analytics, explainability needs a repository just as much as those doing the explaining need access to metadata, EG, information about the data being used. The Cloud Data Migration Challenge. A useful feature for exposing patterns in the data. Visual Profiling.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Cloudera

JANUARY 21, 2021

With CDW, as an integrated service of CDP, your line of business gets immediate resources needed for faster application launches and expedited data access, all while protecting the company’s multi-year investment in centralized data management, security, and governance. Proprietary file formats mean no one else is invited in!

Data Lake 93

Data Lake Data Warehouse IT Analytics 93

IBM Big Data Hub

OCTOBER 12, 2023

Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.)

Cost-Benefit 85

Cost-Benefit Metadata Data Governance Modeling 85

IBM Big Data Hub

JULY 17, 2023

By supporting open-source frameworks and tools for code-based, automated and visual data science capabilities — all in a secure, trusted studio environment — we’re already seeing excitement from companies ready to use both foundation models and machine learning to accomplish key tasks.

Machine Learning 69

Machine Learning Data Warehouse Modeling Cost-Benefit 69

Alation

SEPTEMBER 23, 2021

What Is a Data Catalog? A data catalog is a centralized storage bank of metadata on information sources from across the enterprise, such as: Datasets. Visualizations. The data catalog also stores metadata (data about data, like a conversation), which gives users context on how to use each asset.

Enterprise 52

Enterprise Metadata Data Governance Visualization 52

Alation

FEBRUARY 17, 2023

Reichental describes data governance as the overarching layer that empowers people to manage data well ; as such, it is focused on roles & responsibilities, policies, definitions, metrics, and the lifecycle of the data. In this way, data governance is the business or process side. Seeing is believing!

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

Jet Global

FEBRUARY 23, 2024

Why Data Mapping is Important Data mapping is a critical element of any data management initiative, such as data integration, data migration, data transformation, data warehousing, or automation. Data mapping helps standardize, visualize, and understand data across different systems and applications.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52