article thumbnail

The Modern Data Stack: What It Is, How It Works, Use Cases, and Ways to Implement

AltexSoft

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59
article thumbnail

Hadoop vs Spark: Main Big Data Tools Explained

AltexSoft

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Obviously, Big Data processing involves hundreds of computing units.

Insiders

Sign Up for our Newsletter

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

article thumbnail

How to Use DBT to Get Actionable Insights from Data?

Workfall

Reading Time: 8 minutes In the world of data engineering, a mighty tool called DBT (Data Build Tool) comes to the rescue of modern data workflows. Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights.

article thumbnail

Data Pipeline Architecture Explained: 6 Diagrams and Best Practices

Monte Carlo

5 Data pipeline architecture designs and their evolution The Hadoop era , roughly 2011 to 2017, arguably ushered in big data processing capabilities to mainstream organizations. Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

article thumbnail

How to Become a Data Engineer in 2024?

Knowledge Hut

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. They are also accountable for communicating data trends. Let us now look at the three major roles of data engineers.