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.

Data Quality 130

Data Quality Metadata ETL Big Data 130

Pickl AI

AUGUST 1, 2023

The key sectors where Data Engineering has a major contribution include IT, Internet/eCommerce, and Banking & Insurance. Salary of a Data Engineer ranges between ₹ 3.1 Data Storage: Storing the collected data in various storage systems, such as relational databases, NoSQL databases, data lakes, or data warehouses.

Big Data 52

Big Data Data Analysis Data Scientist Data Ingestion 52

Pickl AI

APRIL 14, 2023

In addition, it also defines the framework wherein it is decided what action needs to be taken on certain data. And so, a company dealing in Big Data Analysis needs to follow stringent Data Governance policies. The same applies to data. What is Data Management? Wrapping it up !!!

Data Platform 52

Data Platform Data Integration Automation Data Ingestion 52

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!

Metadata 162

Metadata Big Data ETL Data Ingestion 162

The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

ETL 59

ETL Categorization Automation Data Integration 59

The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134