CIO Business Intelligence

DECEMBER 11, 2024

Furthermore, generally speaking, data should not be split across multiple databases on different cloud providers to achieve cloud neutrality. Not my original quote, but a cardinal sin of cloud-native data architecture is copying data from one location to another.

Management 111

Management Data Governance Data Science Reporting 111

erwin

JUNE 19, 2020

Despite the similarities in name, there are a number of key differences between an enterprise architecture and solutions architecture. Much like the differences between enterprise architecture (EA) and data architecture, EA’s holistic view of the enterprise will often see enterprise and solution architects collaborate.

Enterprise 130

Enterprise Data Architecture Strategy Data Governance 130

DataKitchen

DECEMBER 20, 2020

The agile alliance definition of business agility consists of two parts. The DataOps Engineering skillset includes hybrid and cloud platforms, orchestration, data architecture, data integration, data transformation, CI/CD, real-time messaging, and containers. Companies that move slowly get left behind. .

Data-driven 147

Data-driven Manufacturing Data Architecture Data Analytics 147

AWS Big Data

NOVEMBER 26, 2024

This post was co-written with Dipankar Mazumdar, Staff Data Engineering Advocate with AWS Partner OneHouse. Data architecture has evolved significantly to handle growing data volumes and diverse workloads. detector = _lambda.DockerImageFunction( scope=self, id="Converter", # Dockerfile in.

Metadata 105

Metadata Data Lake Snapshot Data Warehouse 105

erwin

JUNE 25, 2020

The Difference Between Technical Architecture and Enterprise Architecture. We previously have discussed the difference between data architecture and EA plus the difference between solutions architecture and EA. Organizations can try erwin Evolve for free and keep any content you produce should you decide to buy.

Enterprise 144

Enterprise Data Architecture Data-driven Cost-Benefit 144

CIO Business Intelligence

MARCH 24, 2023

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

Data Governance 142

Data Governance Management Metadata Data Quality 142

TDAN

MAY 4, 2021

When you’ve been involved in data management for as long as I have, things are definitely bound to change. And things have changed, quite a lot, in fact. Back when I started in IT, IMS was the primary database system used at most big enterprises and most of the computing was done on mainframe systems. […].

Enterprise 139

Enterprise Management Data Architecture IT 139

DataKitchen

OCTOBER 11, 2021

First, you must understand the existing challenges of the data team, including the data architecture and end-to-end toolchain. Second, you must establish a definition of “done.” In DataOps, the definition of done includes more than just some working code. Definition of Done. When can you declare it done?

Testing 154

Testing Metadata Dashboards Statistics 154

erwin

JANUARY 9, 2020

From regulatory compliance and business intelligence to target marketing, data modeling maintains an automated connection back to the source. Building a more agile and governable data architecture: Create and implement common data design standards from the start. erwin Data Modeler: Where the Magic Happens.

Data Governance 144

Data Governance Modeling Metadata Unstructured Data 144

AWS Big Data

NOVEMBER 7, 2024

He has over 13 years of professional experience building and optimizing enterprise data warehouses and is passionate about enabling customers to realize the power of their data. He specializes in migrating enterprise data warehouses to AWS Modern Data Architecture.

Data Warehouse 105

Data Warehouse Reporting Big Data Data Lake 105

Peter James Thomas

AUGUST 2, 2019

It includes 30 new definitions, some of which have been contributed by people like Tenny Thomas Soman, George Firican, Scott Taylor and and Taru Väre. Data Architecture – Definition (2). Data Catalogue. Data Sourcing. Geospatial Data. Reference Data (contributor: George Firican ).

Analytics 69

Analytics Data Analytics Data Architecture Statistics 69

Ontotext

JULY 9, 2020

There is no easy answer to these questions but we still need to make sense of the data around us and figure out ways to manage and transfer knowledge with the finest granularity of detail. Knowledge graphs, the ones with semantically modeled data even more so , allow for such a granularity of detail.

Data Architecture 128

Data Architecture Unstructured Data Enterprise Technology 128

Ontotext

JULY 24, 2023

A Few Cautions LLM references a huge amount of data to become truly functional, making it a quite expensive and time consuming effort to train the model. Supercomputers (and other components of infrastructure) along with new approaches to data architecture (with billions of parameters) are needed.

Modeling 98

Modeling Management Structured Data Data Architecture 98

Octopai

APRIL 20, 2023

Data mesh is an approach to data architecture that is intentionally distributed, where data is owned and governed by domain-specific teams who treat the data as a product to be consumed by other domain-specific teams. What are the principles behind data mesh architecture?

Data-driven 97

Data-driven Data Architecture Sales Interactive 97

Sisense

JULY 6, 2020

EDM covers the entire organization’s data lifecycle: It designs and describes data pipelines for each enterprise data type: metadata, reference data, master data, transactional data, and reporting data.

Enterprise 98

Enterprise Management Data Architecture Data-driven 98

AWS Big Data

MAY 22, 2024

Blueprint discovery Rather than create a pipeline definition from scratch, you can use configuration blueprints, which are preconfigured templates for common ingestion scenarios such as trace analytics or Apache logs. He is deeply passionate about Data Architecture and helps customers build analytics solutions at scale on AWS.

Data Architecture 121

Data Architecture Visualization Data Transformation Management 121

erwin

OCTOBER 24, 2019

. • Harvesting data – Automate the collection of metadata from various data management silos and consolidate it into a single source. Structuring and deploying data sources – Connect physical metadata to specific data models, business terms, definitions and reusable design standards.

Metadata 104

Metadata Management Data-driven Data Architecture 104

CIO Business Intelligence

APRIL 24, 2025

By definition, these are large projects with very specific milestones, he adds. Were constantly working across borders, and that means its a good product that comes out. The basis is test, measure, and learn. But there are times when theres project work, like when a new train is purchased.

IT 72

IT Consulting Optimization IoT 72

erwin

JUNE 19, 2020

Despite the similarities in name, there are a number of key differences between an enterprise architecture and solutions architecture. Much like the differences between enterprise architecture (EA) and data architecture, EA’s holistic view of the enterprise will often see enterprise and solution architects collaborate.

Enterprise 94

Enterprise Data Architecture Strategy Data Governance 94

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

JANUARY 6, 2025

Additionally, for the Amazon Redshift Data API, choose the IAM role appflow-redshift-access-role created in the previous section and then choose Set up a table and permission in Amazon Redshift To set up table and permission in Amazon Redshift, follow these steps: On the Amazon Redshift console, choose Query editor v2 in Explorer.

Analytics 98

Analytics Data Warehouse Big Data Metrics 98

Cloudera

SEPTEMBER 15, 2022

The consumption of the data should be supported through an elastic delivery layer that aligns with demand, but also provides the flexibility to present the data in a physical format that aligns with the analytic application, ranging from the more traditional data warehouse view to a graph view in support of relationship analysis.

Data Lake 83

Data Lake Data Architecture Data-driven Data Warehouse 83

CIO Business Intelligence

NOVEMBER 1, 2023

SAP doesn’t want to build those tools from scratch itself: “We definitely want to leverage what’s already out there,” Sun said, noting there are already many large language models (LLMs) it can build on, adding its own prompting, fine tuning, and data embedding to get those models to business customers quickly.

Enterprise 132

Enterprise Cost-Benefit Unstructured Data Software 132

CIO Business Intelligence

JUNE 27, 2022

By now, most enterprises have reached data maturity. “If If your company has data, you’re definitely leveraging it and trying to use insights from analytics to drive positive business outcomes,” says John Loury, president and CEO of Cause + Effect Strategy, a business intelligence consulting firm.

Metrics 133

Metrics KPI IT Consulting 133

TDAN

JUNE 18, 2019

Most organizations agree that they have data issues, categorized as data quality. Organizations typically define the scope of their data problems by their current (known) data quality issues (symptoms). However, this definition is […].

Data Quality 90

Data Quality Data Architecture IT Data Governance 90

AWS Big Data

AUGUST 31, 2023

This enables you to easily manage and maintain your tables on transactional data lakes. This provides the flexibility to manage analytics at scale and find the most cost-effective architecture solution. The following example demonstrates this. We hope this gives you a great starting point for querying Iceberg tables in Amazon Redshift.

Data Lake 118

Data Lake Data Warehouse Metadata Data Architecture 118

AWS Big Data

JULY 2, 2024

On the Actions dropdown menu, choose Import definition to import the workflow definition of the state machine. He has worked with building databases and data warehouse solutions for over 15 years. He is deeply passionate about Data Architecture and helps customers build analytics solutions at scale on AWS.

Data Warehouse 118

Data Warehouse Sales Testing Big Data 118

CIO Business Intelligence

APRIL 29, 2022

Data has rights and sovereignty. Data Security: Achieving authentication, access control, and encryption without negatively impacting productivity. Data Ingestion and Processing: Ensuring that data quality, streaming, and transformation capabilities support your decision-making needs.

Digital Transformation 132

Digital Transformation Data-driven Strategy Data Strategy 132

Cloudera

NOVEMBER 18, 2020

The technological linchpin of its digital transformation has been its Enterprise Data Architecture & Governance platform. It hosts over 150 big data analytics sandboxes across the region with over 200 users utilizing the sandbox for data discovery.

Internet Publishing and Broadcasting 123

Internet Publishing and Broadcasting Data-driven Broadcasting Digital Transformation 123

Jen Stirrup

MARCH 29, 2023

SAP helps to solve this search problem by offering ways to simplify business data with a solid data foundation that powers SAP Datasphere. It fits neatly with the renewed interest in data architecture, particularly data fabric architecture. They fail to get a grip on their data.

Data Warehouse 121

Data Warehouse Metadata Data Integration Business Intelligence 121

AWS Big Data

SEPTEMBER 13, 2023

The Analytics specialty practice of AWS Professional Services (AWS ProServe) helps customers across the globe with modern data architecture implementations on the AWS Cloud. Use the odpf_setup_test_data_glue_job_s3_policy.json policy definition. Use the odpf_setup_test_data_glue_job_ddb_policy.json policy definition.

Data Lake 110

Data Lake Data Processing Metadata Snapshot 110

Sisense

SEPTEMBER 3, 2020

This includes database modeling, metrics definition, dashboard design , and creating and publishing executive reports. ROI (return on investment) is also a key concern, as business analysts apply their data-related activities to finance, marketing, and risk management, for instance. See an example: Explore Dashboard.

Statistics 97

Statistics Metrics Visualization Finance 97

AWS Big Data

JUNE 6, 2023

On the Crawlers page, select data-quality-result-crawler and choose Run. When the crawler is complete, you can see the AWS Glue Data Catalog table definition. After you create the table definition on the AWS Glue Data Catalog, you can use Athena to query the Data Catalog table. Choose Create crawler.

Data Quality 97

Data Quality Metrics Visualization Dashboards 97

Cloudera

JULY 29, 2024

In a recent paper , Harvard Business School professor and technology researcher Marco Iansiti collaborated with Economist Ruiging Cao to model “Data architecture coherence” and the cascading benefit of sustained innovation speed across an enterprise.

Enterprise 69

Enterprise Data Architecture Manufacturing Key Performance Indicator 69

AWS Big Data

DECEMBER 21, 2023

Overview of solution As a data-driven company, smava relies on the AWS Cloud to power their analytics use cases. smava ingests data from various external and internal data sources into a landing stage on the data lake based on Amazon Simple Storage Service (Amazon S3).

Data Lake 115

Data Lake Data Warehouse Data-driven B2B 115

CIO Business Intelligence

FEBRUARY 28, 2024

And not only do companies have to get all the basics in place to build for analytics and MLOps, but they also need to build new data structures and pipelines specifically for gen AI. All of that is fascinating to us and my team is definitely working on this,” she adds. This is imperative for us to do.”

IT 131

IT Consulting Modeling Enterprise 131

Ontotext

MAY 30, 2019

There are two reasons for this: First, Linked Data, or, to put it in Plain English, the practice of explaining the meaning of content to machines, is essentially about linking content to semantically modeled data. Second, Linked Data is creating highly connected, computer-processable definitions of entities.

Cost-Benefit 87

Cost-Benefit Big Data Technology Metadata 87