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. This is where data ingestion comes in.

Data Ingestion 52

Data Ingestion ETL Data Quality Data Integration 52

IBM Journey to AI blog

JUNE 27, 2024

This requires traditional capabilities like encryption, anonymization and tokenization, but also creating capabilities to automatically classify data (sensitivity, taxonomy alignment) by using machine learning. We approach this problem with data management in mind, preparing data for governance, risk and compliance from the outset.

Generative AI 321

Generative AI Data Ingestion Large Language Models AI 321

IBM Journey to AI blog

JANUARY 5, 2023

Next generation of big data platforms and long running batch jobs operated by a central team of data engineers have often led to data lake swamps. Both approaches were typically monolithic and centralized architectures organized around mechanical functions of data ingestion, processing, cleansing, aggregation, and serving.

Data Quality 130

Data Quality Metadata ETL Big Data 130

Pickl AI

JULY 6, 2023

Thus, making it easier for analysts and data scientists to leverage their SQL skills for Big Data analysis. It applies the data structure during querying rather than data ingestion. How Data Flows in Hive In Hive, data flows through several steps to enable querying and analysis.

Big Data 52

Big Data ETL Data Analysis Metadata 52