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

NOVEMBER 7, 2024

BladeBridge offers a comprehensive suite of tools that automate much of the complex conversion work, allowing organizations to quickly and reliably transition their data analytics capabilities to the scalable Amazon Redshift data warehouse. Amazon Redshift is a fully managed data warehouse service offered by Amazon Web Services (AWS).

Data Warehouse 105

Data Warehouse Reporting Big Data Data Lake 105

AWS Big Data

JUNE 7, 2023

They chose AWS Glue as their preferred data integration tool due to its serverless nature, low maintenance, ability to control compute resources in advance, and scale when needed. To share the datasets, they needed a way to share access to the data and access to catalog metadata in the form of tables and views.

Metadata 59

Metadata Data Lake Machine Learning Big Data 59

AWS Big Data

JUNE 10, 2024

Various data pipelines process these logs, storing petabytes (PBs) of data per month, which after processing data stored on Amazon S3, are then stored in Snowflake Data Cloud. Until recently, this data was mostly prepared by automated processes and aggregated into results tables, used by only a few internal teams.

Data Lake 126

Data Lake Metadata Snapshot Analytics 126

Cloudera

JUNE 7, 2022

Only Cloudera has the power to span multi-cloud and on-premises with a hybrid data platform. We deliver cloud-native data analytics across the full data lifecycle – data distribution, data engineering, data warehousing, transactional data, streaming data, data science, and machine learning – that’s portable across infrastructures.

IT 112

IT Data Architecture Unstructured Data Big Data 112

CIO Business Intelligence

MARCH 9, 2022

They conveniently store data in a flat architecture that can be queried in aggregate and offer the speed and lower cost required for big data analytics. On the other hand, they don’t support transactions or enforce data quality. If only there were a best-of-both-worlds compromise. . Learn more at [link]. .

Data Lake 119

Data Lake Unstructured Data Data Warehouse Big Data 119

CIO Business Intelligence

JUNE 23, 2022

Only Cloudera has the power to span multi-cloud and on-premises with a hybrid data platform. We deliver cloud-native data analytics across the full data lifecycle – data distribution, data engineering, data warehousing, transactional data, streaming data, data science, and machine learning – that’s portable across infrastructures.

IT 97

IT Data Architecture Unstructured Data Big Data 97

Cloudera

SEPTEMBER 28, 2020

The data lifecycle model ingests data using Kafka, enriches that data with Spark-based batch process, performs deep data analytics using Hive and Impala, and finally uses that data for data science using Cloudera Data Science Workbench to get deep insights. on roadmap).

Testing 132

Testing Metadata Risk Data Science 132

IBM Big Data Hub

APRIL 27, 2022

Data fabric and data mesh are emerging data management concepts that are meant to address the organizational change and complexities of understanding, governing and working with enterprise data in a hybrid multicloud ecosystem. The good news is that both data architecture concepts are complimentary.

Management 79

Management Metadata Data Architecture Data Lake 79

Cloudera

OCTOBER 11, 2022

This leads to the obvious question – how do you do data at scale ? Al needs machine learning (ML), ML needs data science. Data science needs analytics. And they all need lots of data. And that data is likely in clouds, in data centers and at the edge.

Data Science 94

Data Science Snapshot Data Warehouse Metadata 94

CIO Business Intelligence

AUGUST 3, 2023

“You had to be an expert in the programming language that interacts with that data, and understand the relationships of each data element within each data source, let alone understand its relation to elements in other data sources,” he says. Without those templates, it’s hard to add such information after the fact.”

Analytics 98

Analytics Data Lake Metadata Cost-Benefit 98

AWS Big Data

JULY 14, 2023

These inputs reinforced the need of a unified data strategy across the FinOps teams. We decided to build a scalable data management product that is based on the best practices of modern data architecture. Our source system and domain teams were mapped as data producers, and they would have ownership of the datasets.

Finance 98

Finance Metadata Big Data Recreation/Entertainment 98

AWS Big Data

APRIL 19, 2023

Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights. A modern data architecture is critical in order to become a data-driven organization.

Technology 105

Technology Data-driven Machine Learning Sales 105

AWS Big Data

DECEMBER 19, 2024

The business end-users were given a tool to discover data assets produced within the mesh and seamlessly self-serve on their data sharing needs. The integration of Databricks Delta tables into Amazon DataZone is done using the AWS Glue Data Catalog. Oghosa Omorisiagbon is a Senior Data Engineer at HEMA.

Data Governance 52

Data Governance Publishing Data-driven Metadata 52

AWS Big Data

MARCH 10, 2023

However, as data processing at scale solutions grow, organizations need to build more and more features on top of their data lakes. Additionally, the task of maintaining and managing files in the data lake can be tedious and sometimes complex. The Iceberg table is synced with the AWS Glue Data Catalog.

Data Lake 122

Data Lake Sales Data Warehouse Snapshot 122

CIO Business Intelligence

JUNE 23, 2022

In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake. In a rush to own this term, many vendors have lost sight of the fact that the openness of a data architecture is what guarantees its durability and longevity.

Data Lake 98

Data Lake Data Warehouse Machine Learning Data-driven 98

Cloudera

JUNE 18, 2022

In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake. In a rush to own this term, many vendors have lost sight of the fact that the openness of a data architecture is what guarantees its durability and longevity.

Data Lake 94

Data Lake Data Warehouse Machine Learning Data-driven 94

Alation

JULY 7, 2022

In 2013 I joined American Family Insurance as a metadata analyst. I was changing careers and had just completed a degree in Library and Information Science. I had always been fascinated by how people find, organize, and access information, so a metadata management role after school was a natural choice.

Metadata 52

Metadata Data-driven Insurance Statistics 52

AWS Big Data

MARCH 26, 2024

Profile aggregation – When you’ve uniquely identified a customer, you can build applications in Managed Service for Apache Flink to consolidate all their metadata, from name to interaction history. Then, you transform this data into a concise format.

Data Strategy 98

Data Strategy Strategy Data Warehouse Prescriptive Analytics 98

Cloudera

MAY 22, 2019

Cloudera’s data superheroes design modern data architectures that work across hybrid and multi-cloud and solve complex data management and analytic use cases spanning from the Edge to AI. DATA SECURITY AND GOVERNANCE.

Machine Learning 56

Machine Learning Data Architecture IoT Data Warehouse 56

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. Pushing data to a data lake and assuming it is ready for use is shortsighted.

Metadata 59

Metadata Data Governance Data-driven Modeling 59

IBM Big Data Hub

JANUARY 17, 2023

IBM Cloud Pak for Data Express solutions offer clients a simple on ramp to start realizing the business value of a modern architecture. Data governance. The data governance capability of a data fabric focuses on the collection, management and automation of an organization’s data. Data science and MLOps.

Data-driven 56

Data-driven Enterprise Data Governance Data Science 56

IBM Big Data Hub

JULY 5, 2023

Several items quickly raised to the top as table stakes: Resilient and scalable storage that could satisfy the demand of an ever-increasing data scale. Open data formats that kept the data accessible by all but optimized for high performance and with a well-defined structure. Comprehensive data security and data governance (i.e.

Data Lake 60

Data Lake Metadata Cost-Benefit Data Warehouse 60

Ontotext

MARCH 27, 2024

For example, GPS, social media, cell phone handoffs are modeled as graphs while data catalogs, data lineage and MDM tools leverage knowledge graphs for linking metadata with semantics. RDF uses unique identifiers (IRIs) to build reusable data models and standardized mapping languages to integrate external data sources.

Modeling 53

Modeling Metadata Data Quality Enterprise 53

IBM Big Data Hub

JULY 13, 2023

Data science tasks such as machine learning also greatly benefit from good data integrity. When an underlying machine learning model is being trained on data records that are trustworthy and accurate, the better that model will be at making business predictions or automating tasks. appeared first on IBM Blog.

Data Quality 63

Data Quality Data Integration Metadata Cost-Benefit 63

Data Virtualization

DECEMBER 21, 2023

Reading Time: 2 minutes Data mesh is a modern, distributed data architecture in which different domain based data products are owned by different groups within an organization. And data fabric is a self-service data layer that is supported in an orchestrated fashion to serve.

Data Governance 59

Data Governance Data Architecture Data Integration Management 59

Alation

JULY 28, 2022

What Are the Biggest Drivers of Cloud Data Warehousing? It’s costly and time-consuming to manage on-premises data warehouses — and modern cloud data architectures can deliver business agility and innovation. Lift and shift perpetuates the same data problems, albeit in a new location.

Data Warehouse 69

Data Warehouse Cost-Benefit Data-driven Data Governance 69

Data Virtualization

AUGUST 23, 2024

The post Querying Minds Want to Know: Can a Data Fabric and RAG Clean up LLMs? – Part 4 : Intelligent Autonomous Agents appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. In previous posts, I spoke.

Data Integration 98

Data Integration Modeling Management Data Architecture 98

Data Virtualization

DECEMBER 20, 2024

The post My Reflections on the Gartner Hype Cycle for Data Management, 2024 appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. Gartner Hype Cycle methodology provides a view of how.

Management 52

Management Data Integration Technology Data Architecture 52

Sisense

SEPTEMBER 3, 2019

Modeling Your Data for Performance. Data architecture. The data landscape has changed significantly over the last two decades. The volume of data being created has increased, and the storage and computational resources needed to store and analyze that data has become cheaper and more widely available.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78

Andrew White

JANUARY 9, 2022

This research does not tell you where to do the work; it is meant to provide the questions to ask in order to work out where to target the work, spanning reporting/analytics (classic), advanced analytics and data science (lab), data management and infrastructure, and D&A governance. We write about data and analytics.

Analytics 64

Analytics Measurement Data-driven Modeling 64

Data Virtualization

JUNE 19, 2024

Reading Time: 5 minutes The data landscape has evolved and become more complex as organizations recognize the need to leverage data and analytics. Generative artificial intelligence has further put pressure on organizations to manage this complexity. At TDWI, we see companies collecting traditional structured.

Data Integration 59

Data Integration Management Analytics Data Architecture 59

Data Virtualization

NOVEMBER 17, 2022

Reading Time: 5 minutes Today, many applications call themselves “data catalogs.” The idea seems, on the face of it, easy to understand: a data catalog is simply a centralized inventory of the data assets within an organization. Data catalogs also seek to be the.

Data Integration 59

Data Integration Management Data Lake IT 59

Data Virtualization

MAY 19, 2023

Reading Time: 11 minutes The post Data Strategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

Data Strategy 52

Data Strategy Strategy Data Integration Management 52

Peter James Thomas

AUGUST 18, 2018

Even back then, these were used for activities such as Analytics , Dashboards , Statistical Modelling , Data Mining and Advanced Visualisation. Of course some architectures featured both paradigms as well. This required additional investments in metadata.

Data Lake 63

Data Lake Data Warehouse Data mining Statistics 63

Ontotext

APRIL 15, 2020

Ontotext’s knowledge graph technology is at the core of Cochrane’s data architecture developed by our partners from Data Language. Here, GraphDB is used for storing the ontology models, the vocabulary, the content metadata and the graphs from the PICO ontology.

Publishing 105

Publishing Metadata Data mining Data Architecture 105

AWS Big Data

DECEMBER 20, 2024

Solution overview The basic concept of the modernization project is to create metadata-driven frameworks, which are reusable, scalable, and able to respond to the different phases of the modernization process. These phases are: data orchestration, data migration, data ingestion, data processing, and data maintenance.

Metadata 119

Metadata Data-driven Snapshot Data Lake 119