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Key Advantages of DBMS for Efficient Data Management

Scalegrid

Enhanced data security, better data integrity, and efficient access to information. Despite initial investment costs, DBMS presents long-term savings and improved efficiency through automated processes, efficient query optimizations, and scalability, contributing to enhanced decision-making and end-user productivity.

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Building an elastic query engine on disaggregated storage

The Morning Paper

Building an elastic query engine on disaggregated storage , Vuppalapati, NSDI’20. This paper describes the design decisions behind the Snowflake cloud-based data warehouse. have altered the many assumptions that guided the design and optimization of the Snowflake system. From shared-nothing to disaggregation.

Storage 112
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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Effective management of memory stores with policies like LRU/LFU proactive monitoring of the replication process and advanced metrics such as cache hit ratio and persistence indicators are crucial for ensuring data integrity and optimizing Redis’s performance. offers the Software Watchdog specifically designed for this purpose.

Metrics 130
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PostgreSQL Performance Tuning: Optimizing Database Parameters for Maximum Efficiency

Percona

PostgreSQL performance optimization aims to improve the efficiency of a PostgreSQL database system by adjusting configurations and implementing best practices to identify and resolve bottlenecks, improve query speed, and maximize database throughput and responsiveness. What is PostgreSQL performance tuning?

Tuning 52
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Implementing AWS well-architected pillars with automated workflows

Dynatrace

This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. The framework comprises six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.

AWS 240
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File systems unfit as distributed storage backends: lessons from ten years of Ceph evolution

The Morning Paper

File systems unfit as distributed storage backends: lessons from 10 years of Ceph evolution Aghayev et al., In this case, the assumption that a distributed storage backend should clearly be layered on top of a local file system. What is a distributed storage backend? SOSP’19. This is not surprising in hindsight.

Storage 64
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20X Faster Backup Preparation With Percona XtraBackup 8.0.33-28!

Percona

After the “data dictionary” (DD) engine and DD cache are initialized on a server, the Storage Engines can ask for a table definition. Initializing a DD engine and the cache adds complexity and other server dependencies. Old design (until Percona XtraBackup 8.0.33-27): ibd2sdi data/test/t1.ibd ibd > t1.sdi

Cache 84