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@sancharini shared a post, 6 days, 20 hours ago

How Software Development Tools Influence Code Quality Over Time?

Learn how software development tools shape code quality over time by enforcing standards, automating testing, and improving developer workflows. Discover key factors that impact long-term software reliability.

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@koukibadr shared a link, 6 days, 20 hours ago

Mobile Developer, Nventive

Code Templating

Story Trending

@laura_garcia shared a post, 1 week ago

Software Developer, RELIANOID

𝗛𝗶𝗴𝗵 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗹𝗼𝗻𝗲 𝘄𝗼𝗻’𝘁 𝘀𝗮𝘃𝗲 𝘆𝗼𝘂.

🚨 𝗛𝗶𝗴𝗵 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗹𝗼𝗻𝗲 𝘄𝗼𝗻’𝘁 𝘀𝗮𝘃𝗲 𝘆𝗼𝘂.

HA handles failures like node crashes or AZ outages.

But what about:

❌ Ransomware

❌ Region-wide outages

❌ Human error

👉 That’s 𝗗𝗶𝘀𝗮𝘀𝘁𝗲𝗿 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆 (𝗗𝗥) territory.

Real-world proof:

GitLab → redundancy ≠ recovery

Maersk → one offline backup saved everything

Code Spaces → no DR = shutdown

🎯 𝗛𝗔 = 𝗸𝗲𝗲𝗽 𝗿𝘂𝗻𝗻𝗶𝗻𝗴

🎯 𝗗𝗥 = 𝗰𝗼𝗺𝗲 𝗯𝗮𝗰𝗸 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲

At RELIANOID, we design both:

✔️ HA with clustering & failover

✔️ DR with multi-region + immutable backups

Because uptime is good—but 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗶𝘀 𝗯𝗲𝘁𝘁𝗲𝗿.

#HighAvailability #DisasterRecovery #Resilience #Cloud #DevOps #RELIANOID

https://www.relianoid.com/blog/beyond-high-availability-why-disaster-recovery-matters-and-how-relianoid-delivers/

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@koukibadr started using tool Jenkins , 1 week ago.

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@koukibadr started using tool Firebase , 1 week ago.

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@koukibadr started using tool Docker Compose , 1 week ago.

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@koukibadr started using tool Docker , 1 week ago.

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@koukibadr started using tool Azure Pipelines , 1 week ago.

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@koukibadr started using tool Amazon S3 , 1 week ago.

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@ravikyada started using tool Kubernetes , 1 week, 1 day ago.

Grafana Tempo is a distributed tracing backend built for massive scale and low operational overhead. Unlike traditional tracing systems that depend on complex databases, Tempo uses object storage—such as S3, GCS, or Azure Blob Storage—to store trace data, making it highly cost-effective and resilient. Tempo is part of the Grafana observability stack and integrates natively with Grafana, Prometheus, and Loki, enabling unified visualization and correlation across metrics, logs, and traces.

Technically, Tempo supports ingestion from major tracing protocols including Jaeger, Zipkin, OpenCensus, and OpenTelemetry, ensuring easy interoperability. It features TraceQL, a domain-specific query language for traces inspired by PromQL and LogQL, allowing developers to perform targeted searches and complex trace-based analytics. The newer TraceQL Metrics capability even lets users derive metrics directly from trace data, bridging the gap between tracing and performance analysis.

Tempo’s Traces Drilldown UI further enhances usability by providing intuitive, queryless analysis of latency, errors, and performance bottlenecks. Combined with the tempo-cli and tempo-vulture tools, it delivers a full suite for trace collection, verification, and debugging.

Built in Go and following OpenTelemetry standards, Grafana Tempo is ideal for organizations seeking scalable, vendor-neutral distributed tracing to power observability at cloud scale.