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

NOVEMBER 21, 2024

In this guide, we’ll explore the patterns that can help you design data pipelines that actually work. Table of Contents Common Data Pipeline Design Patterns Explained 1. Lambda Architecture Pattern 4. Kappa Architecture Pattern 5. Data Mesh Pattern 8. The data lakehouse has got you covered!

Data Pipeline 52

Data Pipeline Designing Lambda Architecture Kafka 52

Cloudera

NOVEMBER 2, 2020

Data streamed in is queryable immediately, in an optimal manner. Data streamed in is queryable in conjunction with historical data, avoiding need for Lambda Architecture. Data Model. Conventional enterprise data types. Figure 1 below shows a standard architecture for a Real-Time Data Warehouse.

Data Warehouse 102

Data Warehouse Kafka Lambda Architecture BI 102

Knowledge Hut

APRIL 25, 2023

You can find a comprehensive guide on how data ingestion impacts a data science project with any Data Science course. Why Data Ingestion is Important? Data ingestion provides certain benefits to the business: The raw data coming from various sources is highly complex. Why Data Ingestion is Important?

Data Ingestion 52

Data Ingestion Lambda Architecture Raw Data Data Science 52

dbt Developer Hub

JUNE 30, 2020

Lambda views are a simple and readily available solution that is tool agnostic and SQL based. What are lambda views? ​ The idea of lambda views comes from lambda architecture. This enables handling a lot of data in a very performant manner. This is what I implemented at JetBlue.

SQL 52

SQL Lambda Architecture Raw Data Architecture 52

Ascend.io

JULY 28, 2023

Ingestion: Your data pipeline architecture should anticipate a wide variety of raw data sources to be incorporated into the pipeline. These include internal sources, operational systems, the databases and files provided by business partners, and third-party sources from regulators, agencies, and data aggregators.

Data Pipeline 52

Data Pipeline Architecture Lambda Architecture Data Architecture 52

Monte Carlo

MARCH 14, 2023

Some data teams will leverage micro-batch strategies for time sensitive use cases. These involve data pipelines that will ingest data every few hours or even minutes. Also worth noting is lambda architecture-based data ingestion which is a hybrid model that combines features of both streaming and batch data ingestion.

Data Ingestion 52

Data Ingestion Data Warehouse Lambda Architecture Raw Data 52