The Overengineering Trap: How I Built Fake Big Data Systems
I remember the first time I deployed a Kafka cluster for a dataset that updated once per day. The data fit comfortably in memory on my laptop, but there I was—configuring Zookeeper, tuning consumer groups, and monitoring replication factors. It felt sophisticated. Professional. Like I was doing "real" data engineering.
Then reality hit. I spent more time debugging why messages weren't flowing than actually processing data. The pipeline had twelve moving parts, each with its own failure modes. When something broke (and something always broke), tracing the issue felt like forensic archaeology. I was maintaining distributed systems infrastructure for what was essentially a glorified cron job.
Sound familiar? You're not alone. In 2025, the data engineering landscape is still littered with what I call "fake-big-data" systems—overengineered solutions to modest problems. We reach for Spark when pandas would do. We deploy Kubernetes operators for scripts that run twice a day. We build distributed data lakes for datasets that would fit on a USB drive.
The irony? This complexity often makes our pipelines less reliable, not more. Each additional component introduces new failure points, debugging complexity, and operational overhead. The system becomes so fragile that we need to babysit it constantly, which defeats the purpose of automation in the first place.
After several painful experiences, I developed a simple checklist that changed everything. It's not fancy. It won't get you invited to speak at conferences about cutting-edge distributed systems. But it will save you from building systems that are more complex than the problems they solve.
Start with the SLA, Not the Tech Stack
Here's the first and most important rule: Begin with requirements, not with technology choices. This seems obvious when you say it out loud, but I've watched countless teams (including my own) do the exact opposite.
We get excited about new tools. We want to use the shiny things we read about on engineering blogs. We want to put "Apache Flink" or "Delta Lake" on our resumes. So we start with the technology and work backward to justify it.
Stop doing that. Seriously.
Instead, ask these questions before you write a single line of code or choose a single tool:
Freshness Requirements
How current does the data need to be? Is this:
- Real-time (seconds to minutes)?
- Near-real-time (minutes to an hour)?
- Daily batch?
- Weekly or monthly?
Be brutally honest here. I once worked on a "real-time dashboard" where the business users checked it once per day during their morning meeting. They said they needed real-time data, but their actual behavior showed daily updates were perfectly sufficient. When I switched from a streaming pipeline to a daily batch job, reliability improved by 300% and costs dropped by 80%.
Ask stakeholders: "What happens if this data is 5 minutes old? What about an hour? What about until tomorrow morning?" Their answers will tell you what you actually need to build.
Volume and Growth Projections
How much data are we talking about right now? And how fast is it growing?
I work with datasets in the GB to low TB range—what many would call "small data." But here's the thing: small data can still benefit from good engineering practices. The difference is you don't need distributed systems to handle it.
Do the math. If you're processing 10GB today and growth is 10% per year, you won't need Spark in 2025. Or 2026. Or probably 2030. But if you're at 500GB today with 200% year-over-year growth, you might need to think about scale.
The key is to separate current needs from hypothetical future needs. You can always refactor when you actually hit scale limitations. Premature optimization isn't just inefficient—it actively makes your system worse.
The Boring Technology Principle Applied to Data
Dan McKinley's "Choose Boring Technology" essay should be required reading for every data engineer. The core idea: innovation carries risk, so innovate in only one area at a time. For data pipelines, this means using boring, proven tools for everything except the unique part of your problem.
Let me give you a concrete example from my own work.
I recently built a pipeline that processes customer support tickets for sentiment analysis. The volume: about 50,000 tickets per month. The requirement: daily updates for a morning report.
My old self would have reached for:
- Kafka for ingestion
- Spark for processing
- Airflow for orchestration
- Redis for caching
- Some fancy time-series database for storage
My new, checklist-following self built:
- A Python script that runs daily via cron
- Direct API calls to the support system
- pandas for transformation (in memory, it all fits)
- PostgreSQL for storage (with a JSONB column for flexibility)
The second pipeline took two days to build instead of two weeks. It has zero moving parts besides the script itself. It's been running flawlessly for eight months. When we needed to add a new field, I modified the script and it took 15 minutes.
The boring stack won. Again.
When Boring Doesn't Cut It
Now, I'm not saying you should never use distributed systems. There are legitimate use cases! But they should be exceptions, not defaults.
You might need Spark when:
- Your data truly doesn't fit in memory on a single machine
- You need sub-second latency on TB-scale queries
- You're doing machine learning on massive datasets
You might need Kafka when:
- You have multiple consumers with different processing speeds
- You need to replay events from history
- You're handling truly high-volume streaming data (think thousands of events per second)
The key is that these should be conscious, justified decisions—not defaults you reach for because they sound impressive.
The Actual Checklist: Questions to Ask Before Building
Here's the framework I use for every new pipeline. I literally have this printed next to my monitor.
1. Data Characteristics
- Volume today: Will it fit in memory on a reasonable machine (say, 64GB RAM)?
- Growth rate: How long until it won't fit?
- Velocity: How frequently does new data arrive? (Seconds? Minutes? Days?)
- Variety: How many different sources/formats?
- Veracity: How messy/dirty is the data?
2. Processing Requirements
- Latency tolerance: How fresh does output need to be?
- Processing complexity: Simple transformations or complex joins/aggregations?
- Error handling: What happens when things fail? (Retry? Alert? Manual intervention?)
- Idempotency needed: Can we reprocess from scratch if needed?
3. Team and Operational Constraints
- Team expertise: Who will maintain this? What do they know?
- Monitoring: What visibility do we need into pipeline health?
- Budget: What can we spend on infrastructure?
- Existing infrastructure: What's already running in production?
For each question, I assign a score from 1 (simple) to 5 (complex). If the total score is below 15, I use boring technology. Between 15-25, I might add one "interesting" component. Above 25, I consider distributed systems.
This isn't scientific, but it forces me to think through the requirements systematically rather than jumping to solutions.
Practical Tool Recommendations for Small Data (2025 Edition)
Based on my experience, here are the tools that actually work well for small-to-medium data pipelines in 2025:
Orchestration
For simple schedules: Use cron. Seriously. It's been around since 1975 and it works. If you need retries and alerts, wrap your script with a tool like schedule.py or use systemd timers.
For dependencies between jobs: Consider Prefect or Dagster. They're lighter than Airflow and designed for the modern Python ecosystem. I've been particularly impressed with Prefect's simplicity for small pipelines.
Processing
For data that fits in memory: pandas is still king in 2025. The 2.0+ releases have significant performance improvements and better memory management.
For larger-than-memory but single-machine: Try Polars or DuckDB. Polars gives you pandas-like syntax with better performance on larger datasets. DuckDB is incredible for analytical queries on medium-sized data.
When you actually need distributed: Spark is still the default, but consider Dask first. It often gives you 80% of the benefit with 20% of the complexity.
Storage
For structured data: PostgreSQL. It handles JSONB, has great extensions (TimescaleDB for time series, PostGIS for spatial), and scales surprisingly well. For datasets up to several TB, it's often sufficient.
For analytics: ClickHouse if you need speed on large datasets, but only if you're querying terabytes. For smaller datasets, PostgreSQL or DuckDB will be faster to set up and maintain.
Data Collection
This is where many pipelines get unnecessarily complex. If you're collecting data from APIs, consider using a simple Python script with the requests library. For web scraping at moderate scale, tools like Apify can handle the infrastructure so you don't have to build and maintain your own distributed crawler.
The pattern I follow: start with the simplest thing that could possibly work. Monitor it. See where it struggles. Then upgrade only the component that's causing problems.
Common Mistakes and How to Avoid Them
Mistake #1: Building for Theoretical Scale
"We might get big someday!" is the siren song of overengineering. The truth is, most projects never reach the scale that justifies distributed systems. Even if they do, you'll know exactly what needs scaling because you'll have run into actual limitations.
The fix: Build for 2-3x your current scale, not 1000x. When you hit limits, you'll have real usage patterns to inform your scaling decisions.
Mistake #2: Ignoring Operational Complexity
Every component you add needs to be monitored, updated, backed up, and debugged. That Kubernetes cluster might handle scaling beautifully, but who's going to apply security patches? Who's going to debug networking issues at 2 AM?
The fix: For each new component, ask: "Who will maintain this? How will we monitor it? What's the recovery procedure?" If you don't have good answers, simplify.
Mistake #3: Underestimating Simple Solutions
A Python script writing to CSV files might not sound impressive, but if it solves the problem reliably, it's better than a "modern data stack" that fails constantly.
The fix: Actually try the simple solution first. You might be surprised how far it gets you. I recently replaced a 5-component pipeline with a 100-line Python script, and reliability went from 95% to 99.9%.
Mistake #4: Not Measuring What Matters
Are you optimizing for developer happiness? System reliability? Query performance? Cost? Different goals lead to different architectures.
The fix: Define success metrics before you build. Is it "data arrives within 5 minutes of source"? "Pipeline runs successfully 99.9% of the time"? "Costs under $500/month"? Write it down and design to those metrics.
When to Bring in Help
Sometimes, despite your best efforts, you need specialized expertise. Maybe you're dealing with a particularly gnarly legacy system, or you need to integrate with an enterprise platform you've never used before.
In those cases, consider hiring a specialist on Fiverr for the specific integration or problem. The key is to contain the complexity—hire someone to build a simple, well-documented component that fits into your otherwise simple pipeline, rather than bringing in a consultant to architect a whole distributed system.
I've used this approach several times: I maintain the overall simple architecture, but bring in an expert for the one tricky piece (like a complex API integration or optimizing a particular query). This keeps the system understandable while still leveraging specialized knowledge where needed.
Books and Resources That Changed My Thinking
If you want to go deeper on this philosophy, here are some resources that influenced my approach:
Designing Data-Intensive Applications - Martin Kleppmann's book is essential reading, but read it with a critical eye. Understand the patterns, then decide which ones you actually need.
The Pragmatic Programmer - The chapters on simplicity and avoiding overengineering are timeless.
Staff Engineer: Leadership beyond the management track - Will Larson's book has excellent advice on making architectural trade-offs, which is really what we're talking about here.
Beyond books, I follow engineers who advocate for simplicity. Charity Majors, Cindy Sridharan, and Dan McKinley all write thoughtfully about avoiding unnecessary complexity.
Putting It All Together: A Real-World Example
Let me walk you through a recent project using the checklist approach.
Project: Marketing attribution pipeline
Requirements: Track which ads lead to purchases, with daily updates for the marketing team
My old approach would have been:
- Kafka to stream click events
- Flink to join clicks with purchases
- Redis for session state
- Data warehouse for storage
- Airflow to coordinate everything
Using the checklist:
- Volume: 10GB/day, fits in memory ✓
- Freshness: Daily batch is fine (marketing team checks in morning) ✓
- Processing: Simple joins and aggregations ✓
- Team expertise: We know Python and SQL well ✓
What I actually built:
- A Python script that runs daily via cron
- Downloads click and purchase data from APIs (using requests)
- Processes with pandas (fits in memory on a 16GB machine)
- Loads results to PostgreSQL
- Sends email alert on failure (using smtplib)
Development time: 2 days instead of 2 weeks. Monthly infrastructure cost: $20 for a small VM instead of $500+ for cloud services. Reliability: Has run without intervention for 6 months.
When we needed to add a new data source last month, it took 3 hours instead of the days it would have taken to modify a complex distributed system.
The Simplicity Mindset
Ultimately, avoiding overengineering isn't about tools or checklists—it's about mindset. It's about having the confidence to say "This simple solution is good enough" when everyone around you is reaching for distributed systems.
It's about recognizing that complexity is a cost, not a virtue. That every moving part is a potential failure point. That the most elegant solution is often the one that doesn't need to exist at all.
In 2025, we have more tools than ever. More frameworks, more platforms, more services promising to solve our data problems. The real skill isn't knowing all of them—it's knowing which ones to ignore.
So next time you're designing a pipeline, try the checklist. Start with requirements. Choose boring technology. Build the simplest thing that could possibly work.
Your future self—the one who isn't debugging Kafka consumer lag at 3 AM—will thank you.
