erwin

MAY 27, 2020

What, then, should users look for in a data modeling product to support their governance/intelligence requirements in the data-driven enterprise? Nine Steps to Data Modeling. Provide metadata and schema visualization regardless of where data is stored.

Modeling 98

Modeling Metadata Data Governance Visualization 98

erwin

JANUARY 24, 2019

In most companies, an incredible amount of data flows from multiple sources in a variety of formats and is constantly being moved and federated across a changing system landscape. They need their data mappings to fall under governance and audit controls, with instant access to dynamic impact analysis and lineage. Governing metadata.

Data Governance 71

Data Governance Metadata Data-driven Cost-Benefit 71

AWS Big Data

MARCH 29, 2024

However, to analyze trends over time, aggregate from different dimensions, and share insights across the organization, a purpose-built business intelligence (BI) tool like Amazon QuickSight may be more effective for your business. With these insights, teams have the visibility to make data integration pipelines more efficient.

Metrics 103

Metrics Visualization Dashboards Interactive 103

IBM Big Data Hub

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.

Data Quality 52

Data Quality Data Architecture Strategy Data Lake 52

erwin

JANUARY 17, 2020

Data modeling is a process that enables organizations to discover, design, visualize, standardize and deploy high-quality data assets through an intuitive, graphical interface. Data models provide visualization, create additional metadata and standardize data design across the enterprise. SQL or NoSQL?

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

AWS Big Data

AUGUST 31, 2023

Amazon Redshift is a fast, fully managed petabyte-scale cloud data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Iceberg stores the metadata pointer for all the metadata files.

Data Lake 91

Data Lake Data Warehouse Metadata Data Architecture 91

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

AWS Big Data

NOVEMBER 29, 2023

Of course, the value of a strong data foundation is not new and the need for one spans well beyond generative AI. Amazon DataZone is being used by companies like Guardant Health and Bristol Meyers Squibb to catalog, discover, share, and govern data across their organization. We look forward to seeing what you create!

Data Warehouse 109

Data Warehouse Data Lake Data Integration Dashboards 109

IBM Big Data Hub

NOVEMBER 4, 2022

Increasingly enterprise data is spread across multiple environments which contributes to inconsistent data silos that complicate data governance initiatives and create data integrity issues that could impact Business Intelligence and analytics applications. IBM’s holistic approach to Data Quality.

Data Quality 57

Data Quality Metadata Data Governance Data-driven 57

AWS Big Data

JULY 20, 2023

The Orca Platform is powered by a state-of-the-art anomaly detection system that uses cutting-edge ML algorithms and big data capabilities to detect potential security threats and alert customers in real time, ensuring maximum security for their cloud environment. Why did Orca choose Apache Iceberg?

Data Lake 86

Data Lake Analytics Snapshot Optimization 86

AWS Big Data

NOVEMBER 29, 2023

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

Data Warehouse 99

Data Warehouse Data Lake Analytics Machine Learning 99

AWS Big Data

FEBRUARY 21, 2023

Loading complex multi-point datasets into a dimensional model, identifying issues, and validating data integrity of the aggregated and merged data points are the biggest challenges that clinical quality management systems face. It is a data modeling methodology designed for large-scale data warehouse platforms.

Data Warehouse 64

Data Warehouse Data Lake Cost-Benefit Modeling 64

AWS Big Data

AUGUST 15, 2023

Some examples include AWS data analytics services such as AWS Glue for data integration, Amazon QuickSight for business intelligence (BI), as well as third-party software and services from AWS Marketplace. This post demonstrates how to use Athena to run queries on Parquet or CSV files in a GCS bucket.

Recreation/Entertainment 86

Recreation/Entertainment Unstructured Data Business Intelligence Data-driven 86

erwin

APRIL 27, 2022

Best Data Modeling Solution (erwin Data Modeler). Read more about erwin® Data Modeler by Quest.

Data Governance 52

Data Governance Digital Transformation Metadata Data Warehouse 52

Cloudera

AUGUST 31, 2021

Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis. Consideration of both data & metadata in the migration.

Data Warehouse 82

Data Warehouse Cost-Benefit Metadata Data-driven 82

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. Techcopedia follows the latest trends in data and provides comprehensive tutorials.

Data Governance 57

Data Governance Data Processing Data Quality Metadata 57

IBM Big Data Hub

JULY 13, 2023

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.

Data Quality 66

Data Quality Data Integration Metadata Cost-Benefit 66

AWS Big Data

NOVEMBER 29, 2023

The dbt-glue adapter democratized access for dbt users to data lakes, and enabled many users to effortlessly run their transformation workloads on the cloud with the serverless data integration capability of AWS Glue. The team uses dbt-glue to build a transformed gold model optimized for business intelligence (BI).

Data Lake 101

Data Lake Management Metrics Data Warehouse 101

Domino Data Lab

APRIL 3, 2019

It includes perspectives about current issues, themes, vendors, and products for data governance. My interest in data governance (DG) began with the recent industry surveys by O’Reilly Media about enterprise adoption of “ABC” (AI, Big Data, Cloud). Most of the data management moved to back-end servers, e.g., databases.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Data Virtualization

MARCH 3, 2021

Data is the fuel of the digital economy, so data-centric organizations have a distinct advantage. To remain competitive, organizations must have a data management strategy in place to effectively ingest, store, organize, and analyze data while ensuring that it is.

Enterprise 75

Enterprise Management Strategy Data Lake 75

Alation

JANUARY 17, 2023

A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Data orchestration tools. Business intelligence (BI) platforms. How Did the Modern Data Stack Get Started? Today, data integration platforms like Rivery do just that.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Business Intelligence 52

IBM Big Data Hub

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Sometimes, these outputs are biased because the data used to train the model was incomplete or inaccurate in some way.

Cost-Benefit 81

Cost-Benefit Metadata Data Governance Modeling 81

Data Virtualization

MARCH 3, 2021

Data is the fuel of the digital economy, so data-centric organizations have a distinct advantage. To remain competitive, organizations must have a data management strategy in place to effectively ingest, store, organize, and analyze data while ensuring that it is.

Management 59

Management Enterprise Strategy IT 59

Data Virtualization

MARCH 3, 2021

Data is the fuel of the digital economy, so data-centric organizations have a distinct advantage. To remain competitive, organizations must have a data management strategy in place to effectively ingest, store, organize, and analyze data while ensuring that it is.

Management 52

Management Enterprise Strategy IT 52

O'Reilly on Data

MAY 21, 2019

We found that companies that have successfully adopted machine learning do so either by building on existing data products and services, or by modernizing existing models and algorithms. Use ML to unlock new data types—e.g., Thus, many developers will need to curate data, train models, and analyze the results of models.

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238

IBM Big Data Hub

AUGUST 4, 2023

By leveraging data services and APIs, a data fabric can also pull together data from legacy systems, data lakes, data warehouses and SQL databases, providing a holistic view into business performance. It uses knowledge graphs, semantics and AI/ML technology to discover patterns in various types of metadata.

Data Architecture 62

Data Architecture Data Lake Machine Learning Data Governance 62

Jet Global

MAY 1, 2023

Learn how embedded analytics are different from traditional business intelligence and what analytics users expect. Embedded Analytics Definition Embedded analytics are the integration of analytics content and capabilities within applications, such as business process applications (e.g., CRM, ERP, EHR/EMR) or portals (e.g.,

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52