Highly Scalable

AUGUST 20, 2013

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. This system has been designed to supplement and succeed the existing Hadoop-based system that had too high latency of data processing and too high maintenance costs.

Big Data 154

Big Data Processing Lambda Database 154

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., Seer uses a lightweight RPC-level tracing system to collect request traces and aggregate them in a Cassandra database. on end-to-end latency) and less than 0.15% on throughput. ASPLOS’19.

Big Data 45

Big Data Cloud Performance Hardware 45

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. By implementing data replication strategies, distributed storage systems achieve greater.

Storage 130

Storage Systems Big Data Azure 130

All Things Distributed

JULY 13, 2010

In particular this has been true for applications based on algorithms - often MPI-based - that depend on frequent low-latency communication and/or require significant cross sectional bandwidth. Given the specialized nature of these platforms, they require dedicated resources to maintain and operate and put a big burden on the IT organization.

Cloud 119

Cloud AWS Automotive Latency 119

All Things Distributed

APRIL 4, 2017

It will also give customers another region where they can store their data with the knowledge that it will not leave the EU unless they move it. This enables customers to serve content to their end users with low latency, giving them the best application experience. That’s 100% faster.

AWS 117

AWS Airlines Latency Games 117

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

All Things Distributed

NOVEMBER 15, 2010

For example, the most fundamental abstraction trade-off has always been latency versus throughput. Modern CPUs strongly favor lower latency of operations with clock cycles in the nanoseconds and we have built general purpose software architectures that can exploit these low latencies very well. General Purpose GPU programming.

AWS 136

AWS Latency Programming Architecture 136