BluePes Blog: Insights & Trends

Telco-Grade Java Microservices: Resilient Provisioning, CDR Pipelines, and Observability Under Real Load

Telecom workloads punish weak designs: cascaded timeouts during launches, duplicate activations from “harmless” retries, and CDR jobs that lag exactly when usage spikes. Java 21 LTS gives you reliable building blocks - virtual threads, structured concurrency, modern records - yet stability still depends on operational patterns: explicit state, idempotent commands, guarded dependencies, and observability tied to action. This article lays out a practical approach that holds under real traffic: how to model provisioning flows, move and rate CDRs without double-counting, measure what matters (p50/p95/p99, freshness, backlog), and roll out changes safely. A focused 30-day plan at the end shows how to harden one service without pausing delivery.

• 03 Oct 2025
• 10 min

Power BI vs Amazon QuickSight (2025): Features, Pros & Cons

The business intelligence (BI) software market keeps expanding as companies move from static reporting to cloud-first, AI-assisted analytics. When teams shortlist platforms, Microsoft Power BI and Amazon QuickSight often lead the conversation. Both are modern and widely adopted, yet they differ in ecosystems, pricing models, and where they shine. This article clarifies what each tool is, how it works, when to choose it, and compares features, scalability, and cost patterns ' so you can pick the right fit in 2025.

• 19 Sep 2025
• 10 min

Operational BI in 30 Days: Alerts, Write-Back, and What-If that Close the Loop

Dashboards surface problems; businesses win when those problems turn into actions fast. This article shows how mid-market e-commerce, retail, and manufacturing teams can add alerting, safe write-back, and simple what-if to close the loop directly in BI. You’ll get a concrete 30-day plan, implementation patterns, and the signals that prove decisions land on time.

• 10 Sep 2025
• 10 min

Designing BI for Speed: Modeling Patterns that Cut Query Time by 30–60%

Slow dashboards aren’t inevitable. Most delays come from a handful of fixable choices in modeling and SQL. This article outlines a practical approach for mid-market teams in e-commerce, retail, and manufacturing: shape the data for analysis, pre-compute what’s heavy, and keep queries friendly to the engine. You’ll find concrete patterns, a 30-day plan, and simple signals to track whether performance is actually improving.

• 01 Sep 2025
• 9 min

Lean BI Operating Model for Mid-Market (2025): Roles, Semantic Layer & SLAs

Mid-market teams don’t need more dashboards; they need decisions that land faster. This article describes a lean BI operating model any growing e-commerce, retail, or manufacturing company can start next sprint: clear ownership, a small semantic layer that sticks, and visible SLAs that build trust. Expect practical steps, a four-week playbook, and signals to prove it’s working in 2025 conditions.

• 26 Aug 2025
• 10 min

How to Get the Most Out of Amazon Redshift: A Practical Guide for Analytics Teams

Why tuning your Redshift setup pays off more than you think Redshift Is Powerful — But Only If You Know How to Use It Amazon Redshift is one of the most widely adopted data warehouses for a reason. It’s scalable, relatively affordable, and tightly integrated with the AWS ecosystem. But too often, analytics teams treat it as a black box — dumping data in and hoping it performs well. In reality, Redshift gives you a lot of control over how your data is stored, distributed, and queried. And if you don’t take advantage of those features, performance issues can creep in fast — especially as your data grows. This article breaks down the most common issues BI teams face with Redshift and shows how to optimize your setup for real-world analytics — without throwing more hardware (or budget) at the problem.

• 11 Aug 2025
• 9 min

Why SQL Still Matters in the Age of NoSQL

Why business teams keep coming back to the language of data The Basics Still Matter In a world buzzing with new data frameworks, the classic SQL language remains at the heart of serious data work. Organizations continue relying on SQL not out of reluctance to change, but because it remains one of the most reliable ways to ensure clarity, control, and consistency when working with data. Many modern data tools are trying to simplify how we work with data — drag-and-drop interfaces, auto-generated pipelines, natural language prompts. And yet, at the core of those interfaces, SQL is doing the heavy lifting. This highlights an important point: SQL remains a central component in the way today’s data systems function and interact.

• 23 Jul 2025
• 10 min