article thumbnail

The power of remote engine execution for ETL/ELT data pipelines

IBM Journey to AI blog

Business leaders risk compromising their competitive edge if they do not proactively implement generative AI (gen AI). However, businesses scaling AI face entry barriers. The explosion of data volume in different formats and locations and the pressure to scale AI looms as a daunting task for those responsible for deploying AI.

ETL 213
article thumbnail

30% Off ODSC East, Fan-Favorite Speakers, Foundation Models for Times Series, and ETL Pipeline…

ODSC - Open Data Science

30% Off ODSC East, Fan-Favorite Speakers, Foundation Models for Times Series, and ETL Pipeline Orchestration The ODSC East 2025 Schedule isLIVE! Explore the must-attend sessions and cutting-edge tracks designed to equip AI practitioners, data scientists, and engineers with the latest advancements in AI and machine learning.

ETL 52
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Ivo Everts, Databricks: Enhancing open-source AI and improving data governance

AI News

. “From a quality standpoint, we believe that DBRX is one of the best open-source models out there and when we refer to ‘best’ this means a wide range of industry benchmarks, including language understanding (MMLU), Programming (HumanEval), and Math (GSM8K).”

article thumbnail

Han Heloir, MongoDB: The role of scalable databases in AI-powered apps

AI News

Selecting a database that can manage such variety without complex ETL processes is important. AI models often need access to real-time data for training and inference, so the database must offer low latency to enable real-time decision-making and responsiveness.

Big Data 311
article thumbnail

Tackling AI’s data challenges with IBM databases on AWS

IBM Journey to AI blog

Db2 Warehouse fully supports open formats such as Parquet, Avro, ORC and Iceberg table format to share data and extract new insights across teams without duplication or additional extract, transform, load (ETL). This allows you to scale all analytics and AI workloads across the enterprise with trusted data. 

ETL 234
article thumbnail

Supercharge your data strategy: Integrate and innovate today leveraging data integration

IBM Journey to AI blog

The ability to effectively deploy AI into production rests upon the strength of an organization’s data strategy because AI is only as strong as the data that underpins it. Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models.

article thumbnail

The Rise and Fall of Data Science Trends: A 2018–2024 Conference Perspective

ODSC - Open Data Science

Data Engineerings SteadyGrowth 20182021: Data engineering was often mentioned but overshadowed by modeling advancements. 20222024: As AI models required larger and cleaner datasets, interest in data pipelines, ETL frameworks, and real-time data processing surged.