Remove Analytics Architecture Remove Data Management Remove Data Warehouse
article thumbnail

Modern Customer Data Platform Principles

Data Engineering Podcast

Summary Databases and analytics architectures have gone through several generational shifts. A substantial amount of the data that is being managed in these systems is related to customers and their interactions with an organization. How has that changed the architectural approach to CDPs?

Data Lake 147
article thumbnail

An Exploration Of The Expectations, Ecosystem, and Realities Of Real-Time Data Applications

Data Engineering Podcast

In this episode Shruti Bhat gives her view on the state of the ecosystem for real-time data and the work that she and her team at Rockset is doing to make it easier for engineers to build those experiences. Just connect it to your database/data warehouse/data lakehouse/whatever you’re using and let them do the rest.

Insiders

Sign Up for our Newsletter

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

article thumbnail

Data Engineering Weekly #107

Data Engineering Weekly

Streaming and batch unified in a single platform No Airflow - orchestration inferred from the data $99 / TB of data ingested | transformations free Start Your 30 Day Trial Wealthfront: Event Tracking System at Wealthfront A robust event-tracking system is critical for an efficient data management platform.

article thumbnail

Top-Paying Data Engineer Jobs in Singapore [2023 Updated]

Knowledge Hut

Data Engineers indulge in the whole data process, from data management to analysis. Engineers work with Data Scientists to help make the most of the data they collect and have deep knowledge of distributed systems and computer science. Data engineering is not necessarily an entry-level role.

article thumbnail

Snowflake Data Mesh: Ensure Reliable Data with Data Observability

Monte Carlo

There’s a lot of content out there about why a data mesh is (or isn’t) the best thing since sliced bread. But one thing’s for sure: if you can’t trust the data powering your analytics architecture, it’s hard to justify the investment. More sources and more consumers meant more pipelines and more challenges.