Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Key challenges.

Big Data 63

Big Data Storage Benchmarking Hardware 63

The Netflix TechBlog

NOVEMBER 20, 2023

Whether in analyzing A/B tests, optimizing studio production, training algorithms, investing in content acquisition, detecting security breaches, or optimizing payments, well structured and accurate data is foundational. Backfill: Backfilling datasets is a common operation in big data processing. past 3 hours or 10 days).

Processing 210

Processing Big Data Efficiency Engineering 210

The Netflix TechBlog

JULY 21, 2022

We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform. With large data, comes the opportunity to leverage the data for predictive and classification based analysis.

Big Data 179

Big Data Cache Engineering Data Engineering 179

The Netflix TechBlog

MARCH 4, 2024

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. Upon further profiling, we found that most of the latency came from the candidate generated step (i.e.,

Tuning 210

Tuning Efficiency Big Data Engineering 210

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

InfoQ

JULY 3, 2023

The new solution achieved over 99% hit rate, helped reduce tail latencies by more than 60% and costs by 10% annually. LinkedIn introduced Couchbase as a centralized caching tier for scaling member profile reads to handle increasing traffic that has outgrown their existing database cluster. By Rafal Gancarz

Cache 82

Cache Latency Traffic Database 82

Abhishek Tiwari

DECEMBER 27, 2017

A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL. It offers reliability and performance of a data warehouse, real-time and low-latency characteristics of a streaming system, and scale and cost-efficiency of a data lake.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49