Big Data Data Ingestion ETL 
AI News
SEPTEMBER 29, 2024
Ahead of AI & Big Data Expo Europe , Han Heloir, EMEA gen AI senior solutions architect at MongoDB , discusses the future of AI-powered applications and the role of scalable databases in supporting generative AI and enhancing business processes. Check out AI & Big Data Expo taking place in Amsterdam, California, and London.
Marktechpost
JUNE 13, 2024
ELT Pipelines: Typically used for big data, these pipelines extract data, load it into data warehouses or lakes, and then transform it. It is suitable for distributed and scalable large-scale data processing, providing quick big-data query and analysis capabilities.
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Marktechpost
APRIL 1, 2024
Existing research emphasizes the significance of distributed processing and data quality control for enhancing LLMs. Utilizing frameworks like Slurm and Spark enables efficient big data management, while data quality improvements through deduplication, decontamination, and sentence length adjustments refine training datasets.
AI News
AUGUST 29, 2023
If you think about building a data pipeline, whether you’re doing a simple BI project or a complex AI or machine learning project, you’ve got data ingestion, data storage and processing, and data insight – and underneath all of those four stages, there’s a variety of different technologies being used,” explains Faruqui.
Pickl AI
JULY 30, 2024
Summary: Apache NiFi is a powerful open-source data ingestion platform design to automate data flow management between systems. Its architecture includes FlowFiles, repositories, and processors, enabling efficient data processing and transformation.
Unite.AI
DECEMBER 5, 2023
In this digital economy, data is paramount. Today, all sectors, from private enterprises to public entities, use big data to make critical business decisions. However, the data ecosystem faces numerous challenges regarding large data volume, variety, and velocity. Enter data warehousing!
IBM Journey to AI blog
JANUARY 5, 2023
The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.
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