Monte Carlo

NOVEMBER 21, 2024

Whether it’s customer transactions, IoT sensor readings, or just an endless stream of social media hot takes, you need a reliable way to get that data from point A to point B while doing something clever with it along the way. That’s where data pipeline design patterns come in. Lambda Architecture Pattern 4.

Data Pipeline 52

Data Pipeline Designing Lambda Architecture Kafka 52

Data Engineering Podcast

NOVEMBER 20, 2021

Summary One of the perennial challenges posed by data lakes is how to keep them up to date as new data is collected. In this episode Ori Rafael shares his experiences from Upsolver and building scalable stream processing for integrating and analyzing data, and what the tradeoffs are when coming from a batch oriented mindset.

Data Lake 100

Data Lake Data Integration Lambda Architecture Process 100

Data Engineering Weekly

FEBRUARY 18, 2025

The fourth difference is the Lakehouse Architecture. Fluss embraces the Lakehouse Architecture. Fluss uses Lakehouse as a tiered storage, and data will be converted and tiered into data lakes periodically; Fluss only retains a small portion of recent data. Fluss is tailored for real-time analytics.

Kafka 73

Kafka Lambda Architecture SQL Architecture 73

Rockset

FEBRUARY 6, 2019

That meant a system that was sufficiently nimble and powerful to execute fast SQL queries on raw data, essentially performing any needed transformations as part of the query step, and not as part of a complex data pipeline. Most processing in the Lambda architecture happens in the pipeline and not at query time.

Lambda Architecture 40

Lambda Architecture Architecture MongoDB Kafka 40

Monte Carlo

MARCH 14, 2023

Data from these sources are often ingested into a cloud-based data warehouse or data lake , where they can then be mined for information and insights. Source : Fundamentals of Data Engineering by Joe Reis and Matt Housley. Some data teams will leverage micro-batch strategies for time sensitive use cases.

Data Ingestion 52

Data Ingestion Data Warehouse Lambda Architecture Raw Data 52

ProjectPro

AUGUST 24, 2021

Data Sourcing: Building pipelines to source data from different company data warehouses is fundamental to the responsibilities of a data engineer. So, work on projects that guide you on how to build end-to-end ETL/ELT data pipelines. Upload it to Azure Data lake storage manually.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52