All Things Distributed

NOVEMBER 15, 2016

Today, I am very happy to announce that QuickSight is now generally available in the N. When we announced QuickSight last year, we set out to help all customers—regardless of their technical skills—make sense out of their ever-growing data. Put simply, data is not always readily available and accessible to organizational end users.

Analytics 152

Analytics Availability Media Social Media 152

InfoQ

JULY 14, 2023

Mei-Chin Tsai, Vinod discuss the internal architecture of Azure Cosmos DB and how it achieves high availability, low latency, and scalability. By Mei-Chin Tsai, Vinod Sridharan

Latency 52

Latency Azure Availability Scalability 52

The Netflix TechBlog

DECEMBER 3, 2022

Optionally, this step can use the Write-Audit-Publish pattern to ensure that data is correct before it is made available to the rest of the company. See example below: - template: id: wap type: wap tables: - ${CATALOG}/${DATABASE}/${TABLE} write_jobs: - job: id: write type: Spark spark: script: $S3{./src/sparksql_write.sql}

Best Practices 358

Best Practices Code Testing Data Engineering 358

The Netflix TechBlog

MARCH 25, 2019

We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management.

Infrastructure 219

Infrastructure Big Data Transportation Architecture 219

O'Reilly

FEBRUARY 18, 2020

The results for data-related topics are both predictable and—there’s no other way to put it—confusing. Starting with data engineering, the backbone of all data work (the category includes titles covering data management, i.e., relational databases, Spark, Hadoop, SQL, NoSQL, etc.).

Software Architecture 132

Software Architecture DevOps Data Engineering Architecture 132

The Netflix TechBlog

MARCH 5, 2019

While our engineering teams have and continue to build solutions to lighten this cognitive load (better guardrails, improved tooling, …), data and its derived products are critical elements to understanding, optimizing and abstracting our infrastructure. In the Efficiency space, our data teams focus on transparency and optimization.

Infrastructure 182

Infrastructure Cloud Scalability AWS 182

The Netflix TechBlog

DECEMBER 21, 2020

Some of the optimizations are prerequisites for a high-performance data warehouse. Sometimes Data Engineers write downstream ETLs on ingested data to optimize the data/metadata layouts to make other ETL processes cheaper and faster. Both automatic (event-driven) as well as manual (ad-hoc) optimization.

Storage 203

Storage Latency Efficiency Data Engineering 203