iman

Dear "Ambassador" Reno, @renoomokri It is understandable that you would vigorously defend President Bola Ahmed Tinubu. You are, after all, a known political ally and, more recently, a nominee of his administration. That context matters. However, what is not acceptable is attempting to manufacture or stretch facts as though Nigerians, (especially students), are not living witnesses to the realities on ground. Defending your principal is one thing; rewriting lived experiences is another. On ASUU and the Claim of “No Strikes” It is simply incorrect to claim that ASUU has not embarked on any strike action during President Tinubu’s tenure. On October 12, 2025, ASUU commenced a nationwide two-week warning strike over unresolved issues bordering on funding, welfare, and renegotiation of agreements. While it did not escalate into a prolonged shutdown, it remains a strike action nonetheless. A warning strike does not become “non-existent” simply because it was suspended. Facts should not be selectively edited to suit political narratives. Beyond ASUU: The Education Sector Has Been Far from Stable Even more concerning is the attempt to reduce the stability of education in Nigeria solely to ASUU strikes, ignoring the broader ecosystem that sustains learning, especially in the health sector. NARD (National Association of Resident Doctors) embarked on one of the longest strikes in recent years, severely crippling healthcare delivery and medical training. JOHESU (Joint Health Sector Union) is currently on strike, and its impact on teaching hospitals has been devastating. Medical students across the country have had their education disrupted and distorted, with clinical postings rendered ineffective due to the absence of patients and key health workers. Between late 2024 and early 2025 (approximately four months), consultants in teaching hospitals were on strike, thereby halting medical education nationwide. For context, my own medical class, scheduled to have convocated this year, was directly affected by these strikes. As things stand, there is now a real possibility of further postponement of our graduation, due to the combined effects of the just-concluded NARD strike and the ongoing JOHESU strike. So when one speaks of “uninterrupted academic calendars,” it becomes difficult not to ask: for whom exactly? On NELFUND and the Cost of Education Yes, the Nigerian Education Loan Fund (NELFUND) exists. But context again matters. Shortly after its implementation: Tuition fees across federal and state institutions skyrocketed. Education funding in the national budget was significantly reduced, signalling a withdrawal of subsidy. Federal universities now operate like privatized institutions. Departments now impose additional exorbitant charges labeled “professional fees,” separate from tuition. My own fees was hiked from ₦33,000 to ₦209,000. Let's not even mention other institutions. If the intention was to help poor students, a more humane approach would have been direct subsidy of education, not transferring the burden to students through loans, loans that they must repay in an economy with rampant unemployment and underemployment. A loan is not a gift. It is a future burden. And for many students, it is a burden taken out of desperation, not opportunity. On New Universities and Political Comparisons Your longstanding political grievance with Peter Obi is your prerogative. Politics thrives on disagreement. But even here, logic should prevail. What is the value of approving new universities when: Existing ones are dilapidated, Laboratories are under-equipped, Hostels are overcrowded, Staff are poorly motivated? Expansion without consolidation is not progress. Fixing what exists is often more impactful than multiplying institutions that will inherit the same systemic decay.

Day 60 of my #BuildingInPublic journey into Data Engineering Learned: - How networking and load balancing work in K8s - Creating and deploying Services - Running ETL and ELT pipelines on Kubernetes - Deploying an ETL workflow - How Kubernetes powers MLOps pipelines #Kubernetes

Day 59 of my #buildinginpublic journey into Data Engineering Learned: How stateless apps run on Kubernetes Deployments for managing replicas Scaling and monitoring workloads Stateful apps with StatefulSets Pods with attached storage and monitoring #Kubernetes #K8s #DevOps

Day 58 of my #BuildingInPublic journey into Data Engineering Learned - How Kubernetes powers modern distributed systems - Containerization and why it matters - Using kubectl to interact with clusters - Kubernetes manifests #Kubernetes #K8s #DevOps #DataEngineerin

@ibn_wittig Efficiency, accuracy and scalability Storing everything in a single table creates data redundancy and update anomalies

If you're a Data Analyst and you can see this.. Why should companies store data in multiple tables? Why not a single whole table?

Day 57 of my #buildinginpublic journey into Data Engineering Learned: - How Kafka’s architecture actually works - Brokers, clients, servers, and what ties them together - Managing Kafka clusters with ZooKeeper - Creating, partitioning, and deleting topics - Troubleshooting errors

Day 56 of my #buildinginpublic journey into Data Engineering Learned: -How Kafka topics structure event streams - How producers publish data - How consumers read that data - Using Kafka console tools to write and read messages - Troubleshooting producer/consumer issues

Day 55 of my #BuildingInPublic journey into Data Engineering Explored real-world streaming use cases today. Learned about: - Popular streaming systems - How music services use streaming - Sensor data pipelines + SLAs - Scaling sensor workload

Day 54 of my #buildinginpublic journey Explored streaming systems and what it takes to run real-time pipelines at scale. Key lessons: - What real-time really means - Vertical vs horizontal scaling - SLAs and why they matter - Streaming issues: ordering, throughput, backpressure

Day 53 of my #BuildingInPublic journey into Data Engineering Learned streaming & event-based computing today. Key lessons - Events as the core data unit - How queues work - Log ordering in streaming systems - Batch vs queue vs stream - Real-time log processors

Day 52 of my #buildinginpublic journey into Data Engineering Learnt about batch processing - Batch jobs run on schedules and handle huge volumes - Ordering determines correctness - Horizontal scaling keeps pipelines fast - Some workloads are only realistic with batch

Day 51 of my #buildinginpublic journey Learned how to connect Python to command line for full pipeline automation: What I covered: - Running Python scripts via CLI - Installing dependencies with pip - Scheduling jobs with cron - Automating model runs

Day 50 of my #buildinginpublic journey into Data Engineering Learned how to download data via the command line using curl & wget. What I covered: -Single & multi-file downloads - Using docs & flags - Wait times & constraints for automation #DataEngineering #Linux #Automation

Day 49 of my #buildinginpublic journey into Data Engineering Learned how Spark SQL + PySpark DataFrames make big data processing efficient. - Converting RDD to DataFrame - Loading CSVs - Cleaning & filtering - Running SQL queries - Data visualization in PySpark

Day 48 of my #buildinginpublic journey into Data Engineering Focused on PySpark RDDs Learned about: - Creating RDDs from collections & datasets - Transformations: map, filter - Actions: collect, count - Pair RDDs with reduceByKey - Word frequency analysis #PySpark #BigData

One of the reasons why dashboards confuse stakeholders is lack of context. We design visuals assuming everyone interprets data the same way but they don’t. A dashboard or report that makes sense to you might leave your manager, or client, completely lost. Here’s what could help: 👇 ● Adding annotations that highlight key trends or shifts. ● Using tooltips to add short notes for clarity without cluttering visuals. ● Including summary cards that explains what changed and why it matters. ● Adding titles that guide thinking. E.g., ✅ “Sales are rising faster than costs” is better than just writing ❌“Sales Trend” Dashboards communicate decisions and helps speed up that process for decision makers. In the end, clarity is what turns visuals into decisions. Before your next report goes live, ask yourself: “If I weren’t in the room, would this dashboard still make sense?” If your answer is a NO, that’s your next improvement.

@PromiseNonso_ Clarity > complexity, every time.

Day 47 of my #buildinginpublic journey into Data Engineering Focused on Big Data analysis with Spark Learned about: - 3 V’s of Big Data - SparkContext & PySpark shell - Functional programming with map(), filter(), lambda() Big Data means processing massive data efficiently

Day 46 of my #buildinginpublic journey into Data Engineering Learned how to combine SQL + PySpark for large-scale analytics Created RDDs Ran SQL queries on DataFrames Performed complex aggregations Used broadcasting for optimization of joins #PySpark #SparkSQL #BigData

Not me thinking retail and e-commerce was just about buying, selling, and customer segmentation. I’ve seen the light 😅