Precisely

MAY 3, 2023

Modern Data Architectures Panel To discuss the importance of data quality, governance, and observability for digital transformation success, Precisely COO Eric Yau is joined by panelists Sanjeev Mohan, former Gartner Research VP, Atif Salam, CxO Advisor & Enterprise Technologist at AWS, and Tendü Yogurtçu, PhD, CTO at Precisely.

Data Governance 52

Data Governance AWS Data Quality Analytics 52

Precisely

MARCH 14, 2023

First, private cloud infrastructure providers like Amazon (AWS), Microsoft (Azure), and Google (GCP) began by offering more cost-effective and elastic resources for fast access to infrastructure. But early adopters realized that the expertise and hardware needed to manage these systems properly were complex and expensive.

Data Quality 52

Data Quality Cloud Data Data Pipeline Data Observability 52

Pickl AI

MARCH 19, 2025

Apache Nifi Apache Nifi is a tool for automating the movement of data between systems. It allows users to design, automate, and monitor data flows, making it easier to handle complex data pipelines. Monte Carlo Monte Carlo is a data observability platform that helps engineers detect and resolve data quality issues.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Precisely

FEBRUARY 9, 2024

They’re where the world’s transactional data originates – and because that essential data can’t remain siloed, organizations are undertaking modernization initiatives to provide access to mainframe data in the cloud.

Data Quality 69

Data Quality DataOps Data Silos Artificial Intelligence 69

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. AWS Glue AWS Glue is Amazon’s serverless ETL tool.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Alation

FEBRUARY 8, 2022

Signals around the quality and integrity of the data are essential if people are to understand and trust it. Data provenance and lineage, for example, clarify an asset’s origin and past usages, important details for a newcomer to understand and trust that asset. Examples include public cloud vendors like AWS, Azure, and GCP.

Data Governance 52

Data Governance Data Engineering Data Engineer Data Engineering 52

The MLOps Blog

AUGUST 3, 2023

For example, let’s take Airflow , AWS SageMaker pipelines. We’re building on top of Hamilton, which is an open-source framework for describing data flows. Like they didn’t have to think about, you know, data observability, but look, if you provided those data, we captured things about it.

ML 52

ML ML Data Scientist Machine Learning 52