Knowledge Hut

DECEMBER 26, 2023

Data Engineering is typically a software engineering role that focuses deeply on data – namely, data workflows, data pipelines, and the ETL (Extract, Transform, Load) process. A simple usage of Business Intelligence (BI) would be enough to analyze such datasets. What is the need for Data Science?

Data Engineering 98

Data Engineering Data Engineer Engineering Pipeline-centric 98

Ascend.io

JUNE 8, 2023

Treating data as a product is more than a concept; it’s a paradigm shift that can significantly elevate the value that business intelligence and data-centric decision-making have on the business. Data Pipelines Data pipelines are the indispensable backbone for the creation and operation of every data product.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Knowledge Hut

FEBRUARY 27, 2023

Data engineering builds data pipelines for core professionals like data scientists, consumers, and data-centric applications. A data engineer can be a generalist, pipeline-centric, or database-centric. They manage data considering trends and discrepancies that impact business goals.

Data Engineering 52

Data Engineering Data Engineer Database-centric Pipeline-centric 52

Knowledge Hut

JUNE 20, 2023

In addition, they are responsible for developing pipelines that turn raw data into formats that data consumers can use easily. He researches, develops, and implements artificial intelligence (AI) systems to automate predictive models. This profile is more in demand in midsize and big businesses.

Machine Learning 52

Machine Learning Data Engineering Data Engineer Engineering 52

Monte Carlo

JANUARY 23, 2024

Gen AI can whip up serviceable code in moments — making it much faster to build and test data pipelines. This means that data scientists and engineers will be able to spend less time on painstakingly preparing datasets for analytics or writing complex code. Can I see the pipeline? Those who don’t embrace it will be left behind.

Pipeline-centric 52

Pipeline-centric Database-centric Metadata Unstructured Data 52

Knowledge Hut

JANUARY 30, 2024

The purpose of data extraction is to transform large, unwieldy datasets into a usable and actionable format. Data extraction serves as a means for businesses to harness the potential hidden within these otherwise challenging datasets, often extending their utility beyond their original intended purpose.

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

Knowledge Hut

SEPTEMBER 26, 2023

This cloud-centric approach ensures scalability, flexibility, and cost-efficiency for your data workloads. Third-Party Integrations: Databricks offers connectors and integrations with popular third-party tools and services, including business intelligence (BI) platforms, data visualization tools, and machine learning frameworks.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52