ODSC - Open Data Science

DECEMBER 5, 2023

Some of our most popular in-person sessions were: MLOps: Monitoring and Managing Drift: Oliver Zeigermann | Machine Learning Architect ODSC Keynote: Human-Centered AI: Peter Norvig, PhD | Engineering Director, Education Fellow | Google, Stanford Institute for Human-Centered Artificial Intelligence (HAI) The Cost of AI Compute and Why AI Clouds Will (..)

Data Science 98

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 98

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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

APRIL 21, 2025

Real-World Example: Healthcare systems manage a huge variety of data: structured patient demographics, semi-structured lab reports, and unstructured doctor’s notes, medical images (X-rays, MRIs), and even data from wearable health monitors. Ensuring data quality and accuracy is a major challenge.

Big Data 52

Big Data Big Data Data Science Machine Learning 52

Pickl AI

OCTOBER 17, 2024

Best Practices for ETL Efficiency Maximising efficiency in ETL (Extract, Transform, Load) processes is crucial for organisations seeking to harness the power of data. Implementing best practices can improve performance, reduce costs, and improve data quality.

ETL 52

ETL Data Warehouse Data Quality Data Governance 52

Pickl AI

OCTOBER 15, 2024

Talend Talend is a leading open-source ETL platform that offers comprehensive solutions for data integration, data quality , and cloud data management. It supports both batch and real-time data processing , making it highly versatile. It is well known for its data provenance and seamless data routing capabilities.

ETL 52

ETL Azure AWS Data Governance 52

Pickl AI

JULY 25, 2024

Summary: Data ingestion is the process of collecting, importing, and processing data from diverse sources into a centralised system for analysis. This crucial step enhances data quality, enables real-time insights, and supports informed decision-making. What are the Common Challenges in Data Ingestion?

Apache Kafka 52

Apache Kafka Data Lakes Data Warehouse Data Quality 52

phData

OCTOBER 11, 2024

To help, phData designed and implemented AI-powered data pipelines built on the Snowflake AI Data Cloud , Fivetran, and Azure to automate invoice processing. Implementation of metadata-driven data pipelines for governance and reporting. This is where AI truly shines.

AI 52

AI AI Data Pipeline ML 52

Precisely

MAY 23, 2024

Whatever your approach may be, enterprise data integration has taken on strategic importance. It synthesizes all the metadata around your organization’s data assets and arranges the information into a simple, easy-to-understand format. Deployment should be resource-efficient and easily targeted to fit your use cases.

Data Governance 63

Data Governance ETL Data Pipeline Azure 63

Pickl AI

APRIL 9, 2025

Integration : Can it connect with existing systems like AWS, Azure, or Google Cloud? Informatica PowerCenter Informatica PowerCenter is a leading enterprise-grade ETL tool known for its robust data integration capabilities. PowerCenter is particularly favored by large organizations with extensive data integration needs.

ETL 52

ETL Data Warehouse AWS Business Intelligence 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

FEBRUARY 21, 2024

Matillion’s Data Productivity Cloud is a versatile platform designed to increase the productivity of data teams. It provides a unified platform for creating and managing data pipelines that are effective for both coders and non-coders. Please contact our team for assistance in accomplishing this goal.

Data Pipeline 52

Data Pipeline Azure Cloud Data Data Quality 52

Precisely

MAY 23, 2024

Whatever your approach may be, enterprise data integration has taken on strategic importance. It synthesizes all the metadata around your organization’s data assets and arranges the information into a simple, easy-to-understand format. Deployment should be resource-efficient and easily targeted to fit your use cases.

Data Governance 52

Data Governance ETL Data Pipeline Azure 52

phData

JULY 18, 2023

Key Advantages of Governance Simplified Change Managment: The complexity of the underlying systems is abstracted away from the user, allowing them to simply and declaratively build and change data pipelines. Enhance data quality by rebuilding and documenting data transformations starting from the operational data sources.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

The MLOps Blog

MARCH 21, 2023

Olalekan said that most of the random people they talked to initially wanted a platform to handle data quality better, but after the survey, he found out that this was the fifth most crucial need. And when the platform automates the entire process, it’ll likely produce and deploy a bad-quality model.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

phData

AUGUST 17, 2023

Data pipeline orchestration tools are designed to automate and manage the execution of data pipelines. These tools help streamline and schedule data movement and processing tasks, ensuring efficient and reliable data flow. What are Orchestration Tools?

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

phData

SEPTEMBER 27, 2024

Data Quality Management : Persistent staging provides a clear demarcation between raw and processed customer data. This makes it easier to implement and manage data quality processes, ensuring your marketing efforts are based on clean, reliable data. Your customer data game will never be the same.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52