The Netflix TechBlog

DECEMBER 21, 2020

At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions) as the data lands into our warehouse. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 203

Storage Latency Efficiency Data Engineering 203

Abhishek Tiwari

DECEMBER 22, 2017

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. Then we perform frequent batch ETL from application databases to a data warehouse. Classic ETL. Late transformation.

Big Data 40

Big Data Retail Storage Google 40

All Things Distributed

OCTOBER 7, 2015

However, the data infrastructure to collect, store and process data is geared toward developers (e.g., In AWS’ quest to enable the best data storage options for engineers, we have built several innovative database solutions like Amazon RDS, Amazon RDS for Aurora, Amazon DynamoDB, and Amazon Redshift.

Cloud 138

Cloud Big Data AWS Analytics 138

Abhishek Tiwari

DECEMBER 27, 2017

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. ETL is a product of the relational database era and it has not evolved much in last decade. There are several emerging data trends that will define the future of ETL in 2018. Machine learning meets data integration.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49