AWS Big Data

APRIL 17, 2024

In this post, we discuss how the reimagined data flow works with OR1 instances and how it can provide high indexing throughput and durability using a new physical replication protocol. We also dive deep into some of the challenges we solved to maintain correctness and data integrity.

Optimization 118

Optimization Snapshot Metadata Cost-Benefit 118

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

Ontotext

JANUARY 25, 2024

In the current data management landscape, enterprises have to deal with diverse and dispersed data at unimaginable volumes. Among this complexity of siloed data and content, valuable business insights and opportunities get lost. This is a core component of most data fabric based implementations.

Enterprise 64

Enterprise Management Cost-Benefit Metadata 64

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. Knowledge graphs model knowledge of a domain as a graph with a network of entities and relationships.

Modeling 69

Modeling Metadata Data Quality Enterprise 69

Ontotext

JANUARY 10, 2024

In Computer Science, we are trained to use the Okham razor – the simplest model of reality that can get the job done is the best one. Next, I will explain how knowledge graphs help them to get a unified view to data derived from multiple sources and get richer insights in less time. We call this the Bad Data Tax.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

Ontotext

MARCH 23, 2023

Data management is becoming increasingly challenging for organizations. With an unprecedented amount and diversity of data coming from various sources, it’s like trying to put together a picture with pieces from different puzzles. Can data fabrics save your day?

Management 52

Management Metadata Unstructured Data Data Integration 52

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

Ontotext

APRIL 10, 2024

Data ecosystems have become jungles and in spite of all the technology, data teams are struggling to create a modern data experience. In most enterprises data teams lack a data map and data asset inventory and are often unaware of data that exists across the organization, its associated profile, quality and associated metadata.

Metadata 52

Metadata Data Lake Data Warehouse Data Quality 52

AWS Big Data

FEBRUARY 29, 2024

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

Data Governance 100

Data Governance Unstructured Data Metadata Data Lake 100

Ontotext

JUNE 27, 2019

Ontotext’s GraphDB is an enterprise-ready semantic graph database (also called RDF triplestore as it stores data in RDF triples). It provides the core infrastructure for solutions where modeling agility, data integration, relationship exploration, cross-enterprise data publishing and consumption are critical.

Metadata 40

Metadata Management Enterprise Optimization 40

IBM Big Data Hub

MAY 9, 2022

A confluence of events in the data management and AI landscape is bearing down on companies, no matter their size, industry or geographical location. Some of these, such as the continued sprawl of data across multicloud environments have been looming for years, if not decades. Multicloud data integration.

Management 57

Management Data Quality Metadata Data Integration 57

Ontotext

AUGUST 22, 2023

Poor data management, data silos, and a lack of a common understanding across systems and/or teams are the root cause that prohibits an organization from scaling the business in a dynamic environment. Beyond that, and without a way to visualize, connect, and utilize the data, it’s still just a bunch of random information.

Metadata 52

Metadata Knowledge Discovery Big Data Enterprise 52

Rocket-Powered Data Science

JULY 6, 2021

The key to success is to start enhancing and augmenting content management systems (CMS) with additional features: semantic content and context. This is accomplished through tags, annotations, and metadata (TAM). TAM management, like content management, begins with business strategy. Collect, curate, and catalog (i.e.,

Strategy 267

Strategy Machine Learning Metadata Knowledge Discovery 267

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 108

Data Lake Management Metrics Data Warehouse 108

Ontotext

AUGUST 4, 2023

Working across data islands leads to siloed thinking and the inability to implement critical business initiatives such as Customer, Product, or Asset 360. As data is generated, stored, and used across data centers, edge, and cloud providers, managing a distributed storage environment is complex with no map to guide technology professionals.

Metadata 52

Metadata Data-driven Data Architecture Data Quality 52

Alation

JUNE 2, 2022

In part one of this series, I discussed how data management challenges have evolved and how data governance and security have to play in such challenges, with an eye to cloud migration and drift over time. Data management is not yet a solved problem, but modern data management is leagues ahead of prior approaches.

Modeling 52

Modeling Data Governance Statistics Unstructured Data 52

Jen Stirrup

MARCH 29, 2023

Data as a product as well as a foundation The data foundation is critical for all types of reporting and analytics. Data needs to be respected in the same way as building a product. There’s a new trend for people to talk about data products, bringing a product management mindset to the production of data assets.

Metadata 121

Metadata Data Warehouse Data Integration Business Intelligence 121

Ontotext

FEBRUARY 7, 2024

However, what we usually don’t talk about when generating an asset, are the huge invisible or unplanned costs occurring at a later stage when the data needs to be made available for analysis or secondary usage. As a result, a big portion of the IT capacity in Pharma is bound by data integration. So, where do we go wrong?

Metadata 52

Metadata Data Integration Measurement Data-driven 52

erwin

OCTOBER 8, 2020

So if you’re going to move from your data from on-premise legacy data stores and warehouse systems to the cloud, you should do it right the first time. But even with the “need for speed” to market, new applications must be modeled and documented for compliance, transparency and stakeholder literacy.

Data Governance 145

Data Governance Metadata Testing Data Lake 145

Octopai

SEPTEMBER 27, 2023

Octopai’s real-time capabilities provide a transparent, up-to-the-moment view of data integrations across platforms like Airflow, Azure Data Factory, Snowflake, Redshift, and Azure Synapse. Instead, it’s an intuitive journey where every step of data is transparent and trustworthy.

Metadata 52

Metadata Visualization Data Integration Data-driven 52

AWS Big Data

MAY 21, 2024

LINQ , AVB’s proprietary product information management system, empowers their appliance, consumer electronics, and furniture retailer members to streamline the management of their product catalog. Floor sales use AVB’s Hub , a custom in-store customer relationship management (CRM) product, which relies on LINQ.

Manufacturing 100

Manufacturing Sales Optimization Data Processing 100

Ontotext

MAY 16, 2024

There is a confluence of activity – including generative AI models, digital twins, and shared ledger capabilities – that are having a profound impact on enterprises. This is designed to identify business priorities and must be aligned with the data from initial use cases. What is Graph COE?

Enterprise 52

Enterprise Data-driven Cost-Benefit Digital Transformation 52

Ontotext

DECEMBER 1, 2023

It enriched their understanding of the full spectrum of knowledge graph business applications and the technology partner ecosystem needed to turn data into a competitive advantage. Content and data management solutions based on knowledge graphs are becoming increasingly important across enterprises.

Metadata 78

Metadata Sales Consulting Enterprise 78

AWS Big Data

MARCH 26, 2024

AWS provides different services for building data ingestion pipelines: AWS Glue is a serverless data integration service that ingests data in batches from on-premises databases and data stores in the cloud. Streaming data with low latency needs is stored in Amazon Kinesis Data Streams for real-time consumption.

Data Strategy 71

Data Strategy Strategy Data Warehouse Prescriptive Analytics 71

CIO Business Intelligence

OCTOBER 26, 2023

Business intelligence (BI) analysts transform data into insights that drive business value. Business intelligence analyst job requirements BI analysts typically handle analysis and data modeling design using data collected in a centralized data warehouse or multiple databases throughout the organization.

Business Intelligence 127

Business Intelligence Data-driven Statistics Data Warehouse 127

Ontotext

JULY 29, 2021

The Semantic Web, both as a research field and a technology stack, is seeing mainstream industry interest, especially with the knowledge graph concept emerging as a pillar for data well and efficiently managed. And what are the commercial implications of semantic technologies for enterprise data?

Enterprise 124

Enterprise Metadata Knowledge Discovery Management 124

Data Virtualization

MAY 23, 2024

The post Improving the Accuracy of LLM-Based Text-to-SQL Generation with a Semantic Layer in the Denodo Platform appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. Three core ideas, working together, enable that scenario: The.

Data Integration 52

Data Integration Management Enterprise Metadata 52

Ontotext

NOVEMBER 4, 2021

Given all the advantages of the RDF model regarding enterprise data management, one no longer has good arguments why to bother with LPG at all. On the other hand, LPGs lack many features that are an absolute must for enterprise data management, e.g., standard schema language. The Background Story. In two of its 9.x

Metadata 98

Metadata Visualization Modeling Enterprise 98

CIO Business Intelligence

APRIL 29, 2022

Forward-thinking transformation leaders have realised that more focus needs to be placed on ‘data-centric value creation’ and have made this the pre-eminent organising principle in their organisations. Many organisations focus too heavily on fine tuning their computational models in their pursuit of ‘quick-wins.’

Data Governance 145

Data Governance IT Risk Data Lake 145

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 261

Machine Learning Software Metadata Testing 261

erwin

JANUARY 30, 2020

The results of our new research show that organizations are still trying to master data governance, including adjusting their strategies to address changing priorities and overcoming challenges related to data discovery, preparation, quality and traceability. And close to 50 percent have deployed data catalogs and business glossaries.

Data Governance 145

Data Governance Metadata Cost-Benefit Digital Transformation 145

AWS Big Data

NOVEMBER 29, 2023

This includes tools to help you customize your foundation models, and new services and features to build a strong data foundation to fuel your generative AI applications. Customizing foundation models The need for data is quite obvious if you are building your own foundation models (FMs).

Data Warehouse 111

Data Warehouse Data Lake Data Integration Dashboards 111

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in London earlier this year. A typical data pipeline for machine learning.

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238

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. 10 Steps toward a Data Fabric with Knowledge Graphs.

Metadata 69

Metadata Knowledge Discovery Data Quality Strategy 69

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. Creating a High-Quality Data Pipeline.

Data Governance 40

Data Governance Risk Metadata Management 40

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 105

Data Warehouse Data Lake Analytics Machine Learning 105