Snowflake

NOVEMBER 29, 2023

Next-generation sequencing (NGS) technology has dramatically dropped the price of genomic sequencing, from about $1 million in 2007 to $600 today per whole genome sequencing (WGS). Flexibility to ensure that the data itself is interoperable and reusable across varied research, clinical and real-world use cases.

Metadata 88

Metadata Healthcare Medical Data Storage 88

ProjectPro

AUGUST 26, 2015

"Big data is at the foundation of all of the megatrends that are happening today, from social to mobile to the cloud to gaming."- ”- Atul Butte, Stanford With the big data hype all around, it is the fuel of the 21 st century that is driving all that we do. .”- 1960 - Data warehousing became cheaper.

Big Data 40

Big Data Unstructured Data Hadoop NoSQL 40

Knowledge Hut

JUNE 30, 2023

Key Benefits and Takeaways: Understand data intake strategies and data transformation procedures by learning data engineering principles with Python. Investigate alternative data storage solutions, such as databases and data lakes. Key Benefits and Takeaways: Learn the core concepts of big data systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

ProjectPro

JULY 21, 2021

As a big data architect or a big data developer, when working with Microservices-based systems, you might often end up in a dilemma whether to use Apache Kafka or RabbitMQ for messaging. Rabbit MQ vs. Kafka - Which one is a better message broker? Table of Contents Kafka vs. RabbitMQ - An Overview What is RabbitMQ?

Kafka 52

Kafka Big Data Java Architecture 52

Towards Data Science

FEBRUARY 15, 2023

Concepts, theory, and functionalities of this modern data storage framework Photo by Nick Fewings on Unsplash Introduction I think it’s now perfectly clear to everybody the value data can have. To use a hyped example, models like ChatGPT could only be built on a huge mountain of data, produced and collected over years.

Data Lake 75

Data Lake Data Warehouse Hadoop Architecture 75

Rockset

JANUARY 23, 2020

While traditional RDBMS databases served well the data storage and data processing needs of the enterprise world from their commercial inception in the late 1970s until the dotcom era, the large amounts of data processed by the new applications—and the speed at which this data needs to be processed—required a new approach.

Database 40

Database MySQL Kafka NoSQL 40