ODSC - Open Data Science

JANUARY 28, 2025

While traditional roles like data scientists and machine learning engineers remain essential, new positions like large language model (LLM) engineers and prompt engineers have gained traction. LLM Engineers: With job postings far exceeding the current talent pool, this role has become one of the hottest inAI.

AI 40

AI AI Machine Learning Machine Learning 40

phData

MARCH 20, 2024

Jinja’s usage will significantly empower you to build dynamic and reusable data pipelines , especially when dealing with conditional logic and templatization within dbt. Conclusion Jinja offers a dynamic toolkit that enhances your dbt models and elevates our data-wrangling skills. What is Jinja?

SQL 52

SQL Data Wrangling Database Python 52

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

phData

FEBRUARY 14, 2023

A traditional approach requires massive efforts and a long lead time in sourcing from various data providers, data pipelining, and integrating into data marts. Also today’s volume, variety, and velocity of data, only intensify the data-sharing issues.

Data Governance 52

Data Governance Data Silos Predictive Analytics Data Scientist 52

IBM Journey to AI blog

DECEMBER 16, 2022

Let’s look at five benefits of an enterprise data catalog and how they make Alex’s workflow more efficient and her data-driven analysis more informed and relevant. A data catalog replaces tedious request and data-wrangling processes with a fast and seamless user experience to manage and access data products.

Data Quality 52

Data Quality Data Governance Data Scientist Data Wrangling 52

Pickl AI

JULY 18, 2023

Solution Design Creating a high-level architectural design that encompasses data pipelines, model training, deployment strategies, and integration with existing systems.

AI 52

AI AI Machine Learning Machine Learning 52

Iguazio

OCTOBER 20, 2024

This starts from data wrangling and constructing data pipelines all the way to monitoring models and conducting risk reviews using "policy as code". It's not just about the pieces - it's how they fit together - Scale relies on effective orchestration of the many interactions through end-to-end automation.

AI 95

AI AI Database Data Wrangling 95