DagsHub

APRIL 7, 2024

Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines. These pipelines cover the entire lifecycle of an ML project, from data ingestion and preprocessing, to model training, evaluation, and deployment.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Pickl AI

OCTOBER 19, 2023

Regular Data Audits Conduct regular data audits to identify issues and discrepancies. This proactive approach allows you to detect and address problems before they compromise data quality. Data Governance Framework Implement a robust data governance framework. How Do You Fix Poor Data Quality?

Data Quality 52

Data Quality Data Governance Data Warehouse Machine Learning 52

AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. In this post, we show how to use Lake Formation as a central data governance capability and Amazon EMR as a big data query engine to enable access for SageMaker Data Wrangler.

AWS 93

AWS Data Lakes Clustering Data Preparation 93

phData

APRIL 18, 2023

Snowflake enables organizations to instantaneously scale to meet SLAs with timely delivery of regulatory obligations like SEC Filings, MiFID II, Dodd-Frank, FRTB, or Basel III—all with a single copy of data enabled by data sharing capabilities across various internal departments.

Data Silos 52

Data Silos ETL Clustering Analytics 52

Precisely

JUNE 6, 2023

Salam noted that organizations are offloading computational horsepower and data from on-premises infrastructure to the cloud. This provides developers, engineers, data scientists and leaders with the opportunity to more easily experiment with new data practices such as zero-ETL or technologies like AI/ML.

Data Observability 52

Data Observability Data Lakes Data Quality ETL 52

Pickl AI

DECEMBER 10, 2024

A unified data fabric also enhances data security by enabling centralised governance and compliance management across all platforms. Automated Data Integration and ETL Tools The rise of no-code and low-code tools is transforming data integration and Extract, Transform, and Load (ETL) processes.

Analytics 52

Analytics Analytics Augmented Analytics Machine Learning 52

phData

SEPTEMBER 4, 2024

ThoughSpot can easily connect to top cloud data platforms such as Snowflake AI Data Cloud , Oracle, SAP HANA, and Google BigQuery. In that case, ThoughtSpot also leverages ELT/ETL tools and Mode, a code-first AI-powered data solution that gives data teams everything they need to go from raw data to the modern BI stack.

Analytics 52

Analytics Analytics SQL ETL 52

DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging. This article will discuss managing unstructured data for AI and ML projects. What is Unstructured Data?

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Alation

APRIL 14, 2023

Last week, the Alation team had the privilege of joining IT professionals, business leaders, and data analysts and scientists for the Modern Data Stack Conference in San Francisco. Practitioners and hands-on data users were thrilled to be there, and many connected as they shared their progress on their own data stack journeys.

Data Pipeline 52

Data Pipeline Data Warehouse Cloud Data ETL 52

Alation

JANUARY 25, 2022

This is a key component of active data governance. These capabilities are also key for a robust data fabric. Another key nuance of a data fabric is that it captures social metadata. Social metadata captures the associations that people create with the data they produce and consume. The Power of Social Metadata.

DataOps 52

DataOps SQL ML ML 52

IBM Journey to AI blog

JULY 3, 2024

Multiple data applications and formats make it harder for organizations to access, govern, manage and use all their data for AI effectively. Scaling data and AI with technology, people and processes Enabling data as a differentiator for AI requires a balance of technology, people and processes.

AI 45

AI AI Data Quality Database 45

phData

SEPTEMBER 27, 2024

If the event log is your customer’s diary, think of persistent staging as their scrapbook – a place where raw customer data is collected, organized, and kept for future reference. In traditional ETL (Extract, Transform, Load) processes in CDPs, staging areas were often temporary holding pens for data.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

Pickl AI

JUNE 11, 2024

Let’s delve into the key components that form the backbone of a data warehouse: Source Systems These are the operational databases, CRM systems, and other applications that generate the raw data feeding the data warehouse. Data Extraction, Transformation, and Loading (ETL) This is the workhorse of architecture.

Data Warehouse 52

Data Warehouse ETL Data Mining Data Mining 52

phData

JULY 18, 2023

Why Migrate to a Modern Data Stack? Slow Response to New Information: Legacy data systems often lack the computation power necessary to run efficiently and can be cost-inefficient to scale. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data.

Data Warehouse 52

Data Warehouse Analytics Analytics SQL 52

IBM Journey to AI blog

AUGUST 4, 2023

Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.

Data Lakes 73

Data Lakes AI AI Data Governance 73

Flipboard

NOVEMBER 27, 2024

His mission is to enable customers achieve their business goals and create value with data and AI. He helps architect solutions across AI/ML applications, enterprise data platforms, data governance, and unified search in enterprises.

AWS 149

AWS Database ML ML 149

AWS Machine Learning Blog

NOVEMBER 12, 2024

There are various technologies that help operationalize and optimize the process of field trials, including data management and analytics, IoT, remote sensing, robotics, machine learning (ML), and now generative AI. Multi-source data is initially received and stored in an Amazon Simple Storage Service (Amazon S3) data lake.

AWS 104

AWS AI AI Data Lakes 104