Precisely

DECEMBER 12, 2023

It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may prompt you to rethink your data observability strategy. Complexity leads to risk. Learn more here.

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

IBM Journey to AI blog

OCTOBER 22, 2024

Data is the differentiator as business leaders look to utilize their competitive edge as they implement generative AI (gen AI). Leaders feel the pressure to infuse their processes with artificial intelligence (AI) and are looking for ways to harness the insights in their data platforms to fuel this movement.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

IBM Data Science in Practice

NOVEMBER 28, 2022

Implementing a data fabric architecture is the answer. What is a data fabric? Data fabric is defined by IBM as “an architecture that facilitates the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems.”

Data Pipeline 130

Data Pipeline Data Quality Data Preparation Data Governance 130

IBM Journey to AI blog

APRIL 10, 2023

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

AI 103

AI AI Data Governance Data Pipeline 103

Precisely

AUGUST 29, 2024

Key Takeaways Data quality ensures your data is accurate, complete, reliable, and up to date – powering AI conclusions that reduce costs and increase revenue and compliance. Data observability continuously monitors data pipelines and alerts you to errors and anomalies. stored: where is it located?

Data Governance 52

Data Governance Data Quality Data Observability AI 52

IBM Journey to AI blog

FEBRUARY 26, 2024

Increased data pipeline observability As discussed above, there are countless threats to your organization’s bottom line. That’s why data pipeline observability is so important. That’s why data pipeline observability is so important.

Data Pipeline 112

Data Pipeline Data Engineer Data Engineering Data Engineering 112

IBM Journey to AI blog

AUGUST 23, 2023

The recent success of artificial intelligence based large language models has pushed the market to think more ambitiously about how AI could transform many enterprise processes. However, consumers and regulators have also become increasingly concerned with the safety of both their data and the AI models themselves.

Data Governance 66

Data Governance AI AI Artificial Intelligence 66

ODSC - Open Data Science

FEBRUARY 14, 2023

Beyond Monitoring: The Rise of Data Observability Shane Murray Field | CTO | Monte Carlo This session addresses the problem of “data downtime” — periods of time when data is partial, erroneous, missing or otherwise inaccurate — and how to eliminate it in your data ecosystem with end-to-end data observability.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

The MLOps Blog

JUNE 27, 2023

With Azure Machine Learning, data scientists can leverage pre-built models, automate machine learning tasks, and seamlessly integrate with other Azure services, making it an efficient and scalable solution for machine learning projects in the cloud. Might be useful Unlike manual, homegrown, or open-source solutions, neptune.ai

Machine Learning 134

Machine Learning Machine Learning ML ML 134

AWS Machine Learning Blog

AUGUST 21, 2024

By using the AWS SDK, you can programmatically access and work with the processed data, observability information, inference parameters, and the summary information from your batch inference jobs, enabling seamless integration with your existing workflows and data pipelines.

AWS 101

AWS Data Preparation ML ML 101

Pickl AI

JUNE 7, 2024

IBM Infosphere DataStage IBM Infosphere DataStage is an enterprise-level ETL tool that enables users to design, develop, and run data pipelines. Key Features: Graphical Framework: Allows users to design data pipelines with ease using a graphical user interface. Read Further: Azure Data Engineer Jobs.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

IBM Journey to AI blog

DECEMBER 16, 2022

Gartner calls out IBM’s innovation in metadata and AI-/ML-driven automation in Watson Knowledge Catalog on Cloud Pak for Data, along with fully integrated quality and governance capabilities, as key differentiators that make IBM a leading vendor in competitive evaluations. Everybody wins with a data catalog.

Data Quality 52

Data Quality Data Governance Data Scientist Data Wrangling 52

DagsHub

AUGUST 23, 2024

Datafold is a tool focused on data observability and quality. It is particularly popular among data engineers as it integrates well with modern data pipelines (e.g., Source: [link] Monte Carlo is a code-free data observability platform that focuses on data reliability across data pipelines.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

IBM Journey to AI blog

JULY 13, 2023

IBM’s data governance solution helps organizations establish an automated, metadata-driven foundation that assigns data quality scores to assets and improves curation via out-of-the-box automation rules to simplify data quality management.

Data Quality 74

Data Quality Data Profiling Data Governance Machine Learning 74

Precisely

JUNE 5, 2023

The Data Quality service of the Precisely Data Integrity Suite can help – not only does it ensure data is complete, deduplicated, properly formatted, and standardized, but it also uses AI-based intelligence to make swift recommendations for areas in need of remediation. Addresses are often culprits here.

Analytics 52

Analytics Analytics Data Quality Data Pipeline 52

Pickl AI

JULY 24, 2024

Bias Systematic errors introduced into the data due to collection methods, sampling techniques, or societal biases. Bias in data can result in unfair and discriminatory outcomes. Read More: Data Observability vs Data Quality Data Cleaning and Preprocessing Techniques This is a critical step in preparing data for analysis.

Data Quality 52

Data Quality Machine Learning Machine Learning Clean Data 52

ODSC - Open Data Science

JANUARY 9, 2024

Data engineers act as gatekeepers that ensure that internal data standards and policies stay consistent. Data Observability and Monitoring Data observability is the ability to monitor and troubleshoot data pipelines. Interested in attending an ODSC event?

Data Engineering 97

Data Engineering Data Engineering Data Engineer Data Engineering 97

Precisely

FEBRUARY 10, 2023

Advanced analytics and AI/ML continue to be hot data trends in 2023. According to a recent IDC study, “executives openly articulate the need for their organizations to be more data-driven, to be ‘data companies,’ and to increase their enterprise intelligence.”

DataOps 52

DataOps Data Observability ML ML 52

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Learning these tools is crucial for building scalable data pipelines. offers Data Science courses covering these tools with a job guarantee for career growth. Below are 20 essential tools every data engineer should know.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52