Alation

JANUARY 27, 2022

People might not understand the data, the data they chose might not be ideal for their application, or there might be better, more current, or more accurate data available. An effective data governance program ensures data consistency and trustworthiness. It can also help prevent data misuse.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

This post, part of the Governing the ML lifecycle at scale series ( Part 1 , Part 2 , Part 3 ), explains how to set up and govern a multi-account ML platform that addresses these challenges. An enterprise might have the following roles involved in the ML lifecycles. This ML platform provides several key benefits.

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AWS Machine Learning Blog

JUNE 5, 2023

Data is the foundation for machine learning (ML) algorithms. One of the most common formats for storing large amounts of data is Apache Parquet due to its compact and highly efficient format. Athena allows applications to use standard SQL to query massive amounts of data on an S3 data lake.

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Flipboard

MAY 30, 2025

For the preceding techniques, the foundation should provide scalable infrastructure for data storage and training, a mechanism to orchestrate tuning and training pipelines, a model registry to centrally register and govern the model, and infrastructure to host the model. Model versions should be managed centrally in a model registry.

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Alation

FEBRUARY 1, 2022

A new research report by Ventana Research, Embracing Modern Data Governance , shows that modern data governance programs can drive a significantly higher ROI in a much shorter time span. Historically, data governance has been a manual and restrictive process, making it almost impossible for these programs to succeed.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts.

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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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. Data is frequently kept in data lakes that can be managed by AWS Lake Formation , giving you the ability to implement fine-grained access control using a straightforward grant or revoke procedure.

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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.

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AWS Machine Learning Blog

APRIL 30, 2025

ENGIEs One Data team partnered with AWS Professional Services to develop an AI-powered chatbot that enables natural language conversation search within ENGIEs Common Data Hub data lake, over 3 petabytes of data. Daniel Zagyva is a Senior ML Engineer at AWS Professional Services.

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Alation

APRIL 14, 2022

This is why, when data moves, it’s imperative for organizations to prioritize data discovery. Data discovery is also critical for data governance , which, when ineffective, can actually hinder organizational growth. The Cloud Data Migration Challenge. Data pipeline orchestration. Cloud governance.

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Precisely

JULY 18, 2024

Data management recommendations and data products emerge dynamically from the fabric through automation, activation, and AI/ML analysis of metadata. As data grows exponentially, so do the complexities of managing and leveraging it to fuel AI and analytics. Increase metadata maturity.

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phData

AUGUST 5, 2024

Who should have access to sensitive data? How can my analysts discover where data is located? All of these questions describe a concept known as data governance. The Snowflake AI Data Cloud has built an entire blanket of features called Horizon, which tackles all of these questions and more.

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Alation

APRIL 19, 2022

In that sense, data modernization is synonymous with cloud migration. Modern data architectures, like cloud data warehouses and cloud data lakes , empower more people to leverage analytics for insights more efficiently. What Is the Role of the Cloud in Data Modernization? How to Modernize Data with Alation.

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Precisely

JUNE 6, 2023

The group kicked off the session by exchanging ideas about what it means to have a modern data architecture. Atif Salam noted that as recently as a year ago, the primary focus in many organizations was on ingesting data and building data lakes.

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Alation

JUNE 21, 2022

Over time, we called the “thing” a data catalog , blending the Google-style, AI/ML-based relevancy with more Yahoo-style manual curation and wikis. Thus was born the data catalog. In our early days, “people” largely meant data analysts and business analysts. ML and DataOps teams).

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IBM Journey to AI blog

MARCH 14, 2024

Try Db2 Warehouse SaaS on AWS for free   Netezza SaaS on AWS IBM® Netezza® Performance Server is a cloud-native data warehouse designed to operationalize deep analytics, data mining and BI by unifying, accessing and scaling all types of data across the hybrid cloud. Netezza

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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?

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IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. This is especially helpful when handling massive amounts of big data.

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Alation

SEPTEMBER 23, 2021

The data catalog also stores metadata (data about data, like a conversation), which gives users context on how to use each asset. It offers a broad range of data intelligence solutions, including analytics, data governance, privacy, and cloud transformation. Data Catalog by Type.

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Alation

MARCH 21, 2023

What are common data challenges for the travel industry? Some companies struggle to optimize their data’s value and leverage analytics effectively. When companies lack a data governance strategy , they may struggle to identify all consumer data or flag personal data as subject to compliance audits.

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DagsHub

FEBRUARY 29, 2024

Typically, flashy new algorithms or state-of-the-art models capture both public imagination and the interest of data scientists, but messy data can undermine even the most sophisticated model. For instance, bad data is reported to cost the US $3 Trillion per year and poor quality data costs organizations an average of $12.9

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IBM Journey to AI blog

APRIL 25, 2023

To effectively use raw data, it often needs to be curated within a data warehouse. Semi-structured data needs to be reformatted and transformed to be loaded into tables. And ML processes consume an abundance of capacity to build models. Some use case examples will help.

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phData

MAY 13, 2024

Difficulty in moving non-SAP data into SAP for analytics which encourages data silos and shadow IT practices as business users search for ways to extract the data (which has data governance implications).

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Pickl AI

JUNE 11, 2024

Self-Service Analytics User-friendly interfaces and self-service analytics tools empower business users to explore data independently without relying on IT departments. Best Practices for Maximizing Data Warehouse Functionality A data warehouse, brimming with historical data, holds immense potential for unlocking valuable insights.

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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.

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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. Machine Learning Integration Opportunities Organizations harness machine learning (ML) algorithms to make forecasts on the data.

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phData

SEPTEMBER 27, 2024

Both persistent staging and data lakes involve storing large amounts of raw data. But persistent staging is typically more structured and integrated into your overall customer data pipeline. Building a composable CDP requires some serious data engineering chops. Looking for purchase data? New user sign-up?

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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.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Data lineage and auditing – Metadata can provide information about the provenance and lineage of documents, such as the source system, data ingestion pipeline, or other transformations applied to the data. This information can be valuable for data governance, auditing, and compliance purposes.

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