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. Performance.

Big Data 63

Big Data Storage Benchmarking Hardware 63

Dynatrace

JANUARY 16, 2023

Accordingly, the remaining 27% of clusters are self-managed by the customer on cloud virtual machines. Kubernetes hosting decisions are guided by a set of parameters, including cost, ease of provisioning and scaling, data security, and regulatory compliance. Java Virtual Machine (JVM)-based languages are predominant.

Open Source 337

Open Source Java Operating System Programming 337

ACM Sigarch

MAY 31, 2023

Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications. The memory bandwidth will be a key player because the traditional method to add memory bandwidth by adding memory channels is not scalable.

Latency 52

Latency Hardware Cache Architecture 52

Scalegrid

FEBRUARY 8, 2024

Key Takeaways Distributed storage systems benefit organizations by enhancing data availability, fault tolerance, and system scalability, leading to cost savings from reduced hardware needs, energy consumption, and personnel. Variations within these storage systems are called distributed file systems.

Storage 130

Storage Systems Big Data Azure 130

ACM Sigarch

NOVEMBER 1, 2019

This blog post gives a glimpse of the computer systems research papers presented at the USENIX Annual Technical Conference (ATC) 2019, with an emphasis on systems that use new hardware architectures. The second work presented a novel scalable distributed capability mechanism for security and protection in such systems.

Architecture 12

Architecture Hardware Cache Storage 12