Flipboard

MAY 30, 2025

Governance The two key areas of governance are model and data: Model governance Monitor model for performance, robustness, and fairness. Model versions should be managed centrally in a model registry. Appropriate permissions and policies should be in place for model deployments.

AWS 141

AWS AI AI Database 141

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Big Data Processing: Apache Hadoop, Apache Spark, etc.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Alation

AUGUST 3, 2021

It brings together business users, data scientists , data analysts, IT, and application developers to fulfill the business need for insights. DataOps then works to continuously improve and adjust data models, visualizations, reports, and dashboards to achieve business goals. The Agile Connection.

DataOps 59

DataOps Data Pipeline Data Analyst Analytics 59

phData

AUGUST 10, 2023

In data vault implementations, critical components encompass the storage layer, ELT technology, integration platforms, data observability tools, Business Intelligence and Analytics tools, Data Governance , and Metadata Management solutions. The most important reason for using DBT in Data Vault 2.0

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Tableau

OCTOBER 8, 2021

Leveraging Looker’s semantic layer will provide Tableau customers with trusted, governed data at every stage of their analytics journey. With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics.

Tableau 98

Tableau Analytics Analytics Machine Learning 98

The MLOps Blog

JUNE 27, 2023

Model versioning, lineage, and packaging : Can you version and reproduce models and experiments? Can you see the complete model lineage with data/models/experiments used downstream? It sits between the data lake and cloud object storage, allowing you to version and control changes to data lakes at scale.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Iguazio

FEBRUARY 20, 2024

This includes management vision and strategy, resource commitment, data and tech and operating model alignment, robust risk management and change management. The required architecture includes a data pipeline, ML pipeline, application pipeline and a multi-stage pipeline. Read more here.

AI 52

AI AI Data Pipeline Analytics 52

Pickl AI

JULY 3, 2023

It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

IBM Journey to AI blog

JANUARY 5, 2023

What does a modern data architecture do for your business? A modern data architecture like Data Mesh and Data Fabric aims to easily connect new data sources and accelerate development of use case specific data pipelines across on-premises, hybrid and multicloud environments.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

ODSC - Open Data Science

APRIL 26, 2023

Data Engineer Data engineers are the authors of the infrastructure that stores, processes, and manages the large volumes of data an organization has. The main aspect of their profession is the building and maintenance of data pipelines, which allow for data to move between sources.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

phData

JULY 18, 2023

Enter dbt dbt provides SQL-centric transformations for your data modeling and transformations, which is efficient for scrubbing and transforming your data while being an easy skill set to hire for and develop within your teams. However, merely knowing what it consists of isn’t enough.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Data Security and Governance Maintaining data security is crucial for any company.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

Dataversity

SEPTEMBER 28, 2022

Data engineering is a fascinating and fulfilling career – you are at the helm of every business operation that requires data, and as long as users generate data, businesses will always need data engineers. The journey to becoming a successful data engineer […]. In other words, job security is guaranteed.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Snorkel AI

MAY 12, 2023

The reason is that most teams do not have access to a robust data ecosystem for ML development. billion is lost by Fortune 500 companies because of broken data pipelines and communications. Publishing standards for data and governance of that data is either missing or very widely far from an ideal.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

The reason is that most teams do not have access to a robust data ecosystem for ML development. billion is lost by Fortune 500 companies because of broken data pipelines and communications. Publishing standards for data and governance of that data is either missing or very widely far from an ideal.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

Data Science Dojo

APRIL 3, 2025

A 5-Step Framework for Secure Enterprise AI Deployment Building a secure and compliant enterprise AI system requires more than just deploying AI models. A robust infrastructure, strong data governance, and proactive security measures are some key requirements for the process.

AI 195

AI AI Data Pipeline Data Preparation 195