erwin

JANUARY 9, 2020

While it’s always been the best way to understand complex data sources and automate design standards and integrity rules, the role of data modeling continues to expand as the fulcrum of collaboration between data generators, stewards and consumers. So here’s why data modeling is so critical to data governance.

Data Governance 144

Data Governance Modeling Metadata Unstructured Data 144

AWS Big Data

FEBRUARY 6, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning (ML), and application development. MongoDB Atlas is a developer data service from AWS technology partner MongoDB, Inc. Choose Create job.

Metadata 98

Metadata Data Lake Machine Learning Big Data 98

AWS Big Data

MARCH 29, 2024

QuickSight makes it straightforward for business users to visualize data in interactive dashboards and reports. You can slice data by different dimensions like job name, see anomalies, and share reports securely across your organization. Looking at the Skewness Job per Job visualization, there was spike on November 1, 2023.

Metrics 128

Metrics Visualization Dashboards Publishing 128

datapine

MAY 29, 2019

BI architecture has emerged to meet those requirements, with data warehousing as the backbone of these processes. One of the BI architecture components is data warehousing. Each of that component has its own purpose that we will discuss in more detail while concentrating on data warehousing. Data integration.

Business Intelligence 196

Business Intelligence Data Warehouse Dashboards Visualization 196

AWS Big Data

OCTOBER 21, 2024

Let’s briefly describe the capabilities of the AWS services we referred above: AWS Glue is a fully managed, serverless, and scalable extract, transform, and load (ETL) service that simplifies the process of discovering, preparing, and loading data for analytics. Amazon Athena is used to query, and explore the data.

Sales 115

Sales Data-driven Data Processing Key Performance Indicator 115

AWS Big Data

DECEMBER 17, 2024

We have enhanced data sharing performance with improved metadata handling, resulting in data sharing first query execution that is up to four times faster when the data sharing producers data is being updated.

Data Lake 98

Data Lake Data Warehouse Data-driven Optimization 98

AWS Big Data

DECEMBER 13, 2023

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

Metadata 111

Metadata Data Lake Visualization Data Quality 111

AWS Big Data

DECEMBER 4, 2024

Collaborate and build faster using familiar AWS tools for model development, generative AI, data processing, and SQL analytics with Amazon Q Developer , the most capable generative AI assistant for software development, helping you along the way. Having confidence in your data is key.

Data Analytics 104

Data Analytics Analytics Data Lake Data Quality 104

erwin

OCTOBER 8, 2020

These tools range from enterprise service bus (ESB) products, data integration tools; extract, transform and load (ETL) tools, procedural code, application program interfaces (APIs), file transfer protocol (FTP) processes, and even business intelligence (BI) reports that further aggregate and transform data.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

erwin

JANUARY 17, 2020

What is Data Modeling? 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.

Data-driven 138

Data-driven Modeling Metadata Data Governance 138

erwin

DECEMBER 9, 2024

And if it isnt changing, its likely not being used within our organizations, so why would we use stagnant data to facilitate our use of AI? The key is understanding not IF, but HOW, our data fluctuates, and data observability can help us do just that. And lets not forget about the controls.

Metadata 52

Metadata Data Quality Sales Modeling 52

erwin

MAY 27, 2020

And it exists across these hybrid architectures in different formats: big and unstructured and traditional structured business data may physically sit in different places. What’s desperately needed is a way to understand the relationships and interconnections between so many entities in data sets in detail. Nine Steps to Data Modeling.

Modeling 98

Modeling Metadata Data Governance Visualization 98

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 63

Data Quality Data Integration Metadata Cost-Benefit 63

AWS Big Data

APRIL 3, 2024

Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time. Apache Iceberg offers integrations with popular data processing frameworks such as Apache Spark, Apache Flink, Apache Hive, Presto, and more.

Data Lake 127

Data Lake Snapshot Metadata Data Architecture 127

AWS Big Data

FEBRUARY 26, 2025

The next generation of SageMaker also introduces new capabilities, including Amazon SageMaker Unified Studio (preview) , Amazon SageMaker Lakehouse , and Amazon SageMaker Data and AI Governance. These metadata tables are stored in S3 Tables, the new S3 storage offering optimized for tabular data. With AWS Glue 5.0,

Analytics 61

Analytics Data Lake Metadata Data Warehouse 61

CIO Business Intelligence

MARCH 24, 2023

The program must introduce and support standardization of enterprise data. Programs must support proactive and reactive change management activities for reference data values and the structure/use of master data and metadata.

Data Governance 142

Data Governance Management Metadata Data Quality 142

AWS Big Data

SEPTEMBER 7, 2023

We will partition and format the server access logs with Amazon Web Services (AWS) Glue , a serverless data integration service, to generate a catalog for access logs and create dashboards for insights. Both the user data and logs buckets must be in the same AWS Region and owned by the same account.

Metadata 116

Metadata Dashboards Metrics Visualization 116

erwin

MAY 13, 2020

Metadata management is the key to managing and governing your data and drawing intelligence from it. Beyond harvesting and cataloging metadata , it also must be visualized to break down the complexity of how data is organized and what data relationships there are so that meaning is explicit to all stakeholders in the data value chain.

Data Governance 93

Data Governance Enterprise Modeling Management 93

David Menninger's Analyst Perspectives

OCTOBER 8, 2024

As I recently noted , the term “data intelligence” has been used by multiple providers across analytics and data for several years and is becoming more widespread as software providers respond to the need to provide enterprises with a holistic view of data production and consumption.

Data Quality 130

Data Quality Data Governance Enterprise Visualization 130

DataKitchen

SEPTEMBER 21, 2023

There are multiple locations where problems can happen in a data and analytic system. What is Data in Use? Data in Use pertains explicitly to how data is actively employed in business intelligence tools, predictive models, visualization platforms, and even during export or reverse ETL processes.

Testing 173

Testing Data Quality Predictive Modeling Metrics 173

erwin

NOVEMBER 6, 2020

To better explain our vision for automating data governance, let’s look at some of the different aspects of how the erwin Data Intelligence Suite (erwin DI) incorporates automation. Data Cataloging: Catalog and sync metadata with data management and governance artifacts according to business requirements in real time.

Data Governance 97

Data Governance Metadata Data-driven Visualization 97

AWS Big Data

JUNE 7, 2023

This data needs to be ingested into a data lake, transformed, and made available for analytics, machine learning (ML), and visualization. For this, Cargotec built an Amazon Simple Storage Service (Amazon S3) data lake and cataloged the data assets in AWS Glue Data Catalog.

Metadata 59

Metadata Data Lake Machine Learning Big Data 59

erwin

APRIL 29, 2020

Many large organizations, in their desire to modernize with technology, have acquired several different systems with various data entry points and transformation rules for data as it moves into and across the organization. Who are the data owners? Data lineage offers proof that the data provided is reflected accurately.

Key Performance Indicator 111

Key Performance Indicator Metadata Data Governance Data Quality 111

Jen Stirrup

MARCH 29, 2023

However, to turn data into a business problem, organizations need support to move away from technical issues to start getting value as quickly as possible. SAP Datasphere simplifies data integration, cataloging, semantic modeling, warehousing, federation, and virtualization through a unified interface. Why is this interesting?

Data Warehouse 121

Data Warehouse Metadata Data Integration Business Intelligence 121

CIO Business Intelligence

OCTOBER 26, 2023

Business intelligence (BI) analysts transform data into insights that drive business value. This is done by mining complex data using BI software and tools , comparing data to competitors and industry trends, and creating visualizations that communicate findings to others in the organization.

Business Intelligence 126

Business Intelligence Data-driven Statistics Data Warehouse 126

AWS Big Data

OCTOBER 11, 2023

The availability of machine-readable files opens up new possibilities for data analytics, allowing organizations to analyze large amounts of pricing data. Using machine learning (ML) and data visualization tools, these datasets can be transformed into actionable insights that can inform decision-making.

Visualization 119

Visualization Dashboards Data-driven Gap analysis 119

Ontotext

NOVEMBER 4, 2021

As we’ve said again and again, we believe that knowledge graphs are the next generation tool for helping businesses make critical decisions, based on harmonized knowledge models and data derived from siloed source systems. But these tasks are only part of the story. Now, let’s dive in and look into each of these webinars.

Metadata 98

Metadata Visualization Modeling Enterprise 98

IBM Big Data Hub

APRIL 27, 2022

Gartner defines a data fabric as “a design concept that serves as an integrated layer of data and connecting processes. The data fabric architectural approach can simplify data access in an organization and facilitate self-service data consumption at scale. 11 May 2021. . 3 March 2022.

Management 79

Management Metadata Data Architecture Data Lake 79

AWS Big Data

MARCH 26, 2024

Data ingestion You have to build ingestion pipelines based on factors like types of data sources (on-premises data stores, files, SaaS applications, third-party data), and flow of data (unbounded streams or batch data). Then, you transform this data into a concise format.

Data Strategy 99

Data Strategy Strategy Data Warehouse Prescriptive Analytics 99

AWS Big Data

FEBRUARY 29, 2024

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. Data discoverability Unlike structured data, which is managed in well-defined rows and columns, unstructured data is stored as objects.

Data Governance 124

Data Governance Unstructured Data Metadata Data Lake 124

AWS Big Data

NOVEMBER 22, 2023

Many AWS customers adopted Apache Hudi on their data lakes built on top of Amazon S3 using AWS Glue , a serverless data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning (ML), and application development.

Data Lake 116

Data Lake Snapshot Metadata Optimization 116

AWS Big Data

NOVEMBER 29, 2023

Maximize value with comprehensive analytics and ML capabilities “Amazon Redshift is one of the most important tools we had in growing Jobcase as a company.” – Ajay Joshi, Distinguished Engineer, Jobcase With all your data integrated and available, you can easily build and run near real-time analytics to AI/ML/Generative AI applications.

Data Warehouse 130

Data Warehouse Analytics Data Lake Machine Learning 130

AWS Big Data

NOVEMBER 29, 2023

With Amazon Bedrock , you can privately customize FMs for your specific use case using a small set of your own labeled data through a visual interface without writing any code. Amazon DataZone uses ML to automatically add metadata to your data catalog, making all of your data more discoverable.

Data Warehouse 116

Data Warehouse Data Lake Dashboards Data Integration 116

Ontotext

SEPTEMBER 15, 2020

Added to this is the increasing demands being made on our data from event-driven and real-time requirements, the rise of business-led use and understanding of data, and the move toward automation of data integration, data and service-level management. Knowledge Graphs are the Warp and Weft of a Data Fabric.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

Ontotext

JUNE 3, 2020

Knowledge graph technology can walk us out of the lack of context (which is basically absence of proper interlinking) and towards enriching digital representation of collection with semantic data and further interlinking it into a meaningful constellation of items.

Contextual Data 98

Contextual Data Metadata Digital Transformation Visualization 98

Smarten

JANUARY 30, 2024

Business users cannot even hope to prepare data for analytics – at least not without the right tools. Gartner predicts that, ‘data preparation will be utilized in more than 70% of new data integration projects for analytics and data science.’ So, why is there so much attention paid to the task of data preparation?

Analytics 67

Analytics Visualization Data Quality Metadata 67

Cloudera

MARCH 23, 2022

Figure 1: Apache Iceberg fits the next generation data architecture by abstracting storage layer from analytics layer while introducing net new capabilities like time-travel and partition evolution. #1: Apache Iceberg enables seamless integration between different streaming and processing engines while maintaining data integrity between them.

Metadata 92

Metadata Data Architecture Machine Learning Cost-Benefit 92