Teradata

MARCH 11, 2021

Getting your Cloud data architecture right starts with understanding which data products you need, the roles they perform, & the functional & non-functional characteristics that those roles demand.

Analytics Architecture 52

Analytics Architecture Architecture Cloud Data Architecture 52

Data Engineering Podcast

MAY 29, 2022

By supporting fast, in-memory row-based queries and columnar on-disk representation, it lets your transactional and analytical workloads run in the same database. report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. In fact, while only 3.5% In fact, while only 3.5%

Architecture 100

Architecture Database Data Architecture PostgreSQL 100

Towards Data Science

JANUARY 16, 2024

When I heard the words ‘decentralised data architecture’, I was left utterly confused at first! In my then limited experience as a Data Engineer, I had only come across centralised data architectures and they seemed to be working very well. Data lakes were introduced to store the new data formats.

Data Warehouse 67

Data Warehouse Data Architecture Relational Database NoSQL 67

Rockset

JULY 8, 2021

Shrey Batra comes from LinkedIn and Innovaccer, two companies with particularly data-intensive products, so I'm really interested to hear what type of real-time analytics architectures he's had experience with that employ MongoDB Change Streams. Besides, who can say no to a puppet?

MongoDB 40

MongoDB Manufacturing Analytics Architecture Architecture 40

Monte Carlo

MAY 17, 2023

System Modernization and Optimization The only constant in data engineering is change. This applies especially to your data architecture. Luckily, data observability can help with migrations, refactoring pipelines, and more. ” 36. For these scenarios, it can make sense to adapt the underlying infrastructure.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

System Modernization and Optimization The only constant in data engineering is change. This applies especially to your data architecture. Luckily, data observability can help with migrations, refactoring pipelines, and more. ” 36. For these scenarios, it can make sense to adapt the underlying infrastructure.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52