Data Engineering Podcast

JANUARY 21, 2024

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

Data Lake High Quality Data NoSQL Data Warehouse 147

Towards Data Science

JANUARY 16, 2024

Evolution of the data landscape 1980s — Inception Relational databases came into existence. Databases were overwhelmed with transactional and analytical workloads. Result: Data warehouse was born. Image by the author Early 1990s — Scale Analytical workloads started to get complex. Data volumes started to grow.

Data Warehouse 67

Data Warehouse Data Architecture Relational Database NoSQL 67

DataKitchen

AUGUST 19, 2021

Figure 3 shows an example processing architecture with data flowing in from internal and external sources. Each data source is updated on its own schedule, for example, daily, weekly or monthly. The data scientists and analysts have what they need to build analytics for the user. The new Recipes run, and BOOM!

Pharmaceutical 98

Pharmaceutical Data Lake Data Warehouse Raw Data 98

Edureka

FEBRUARY 7, 2023

One can use polybase: From Azure SQL Database or Azure Synapse Analytics, query data kept in Hadoop, Azure Blob Storage, or Azure Data Lake Store. It does away with the requirement to import data from an outside source. Export information to Azure Data Lake Store, Azure Blob Storage, or Hadoop.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Rockset

MARCH 19, 2020

With all of these stream processing and real-time data store options, though, also comes questions for when each should be used and what their pros and cons are. I hope by the end you find yourself better informed and less confused about the real-time analytics landscape and are ready to dive in to it for yourself.

Kafka 40

Kafka Database Process SQL 40