Your database is a mess. Customer names are spelled differently across systems. Product IDs don’t match between inventory and finance. Sales records have gaps. When you throw an LLM at this garbage, it produces garbage.
Data engineering services clean up the mess first. This takes months. It costs serious money. But it’s the only reason AI projects don’t crash. Big data engineering services handle the same problems at a massive scale—thousands of data sources, petabytes of data, and real-time requirements. Both are expensive. Skipping them is more expensive.
The Thing Nobody Wants to Admit
I worked at a company that spent $1.2 million on an AI project that lasted four months before getting killed. Six people worked on it full-time. We had access to the best LLMs available. Our data scientists were smart. Our executives were committed.
We failed because our data was corrupt.
Not catastrophically corrupt. Not obviously corrupt. Just… inconsistent. Messy. Full of gaps and duplicates and conflicting versions of the truth.
Nobody told us this was the problem. We hired people to build models. They spent twelve weeks fighting with data instead of building anything. They’d spend an entire day chasing down why customer records were duplicating. Or why transactions from last month suddenly disappeared from our system. Or why the same product had seventeen different names in our database.
The LLM wasn’t the problem. The model was fine. It sat there waiting for clean data that never came.
This happens everywhere. I’ve talked to people at banks, insurance companies, and retailers. Same story every time. The model is ready. The data is not.
That’s what data engineering services actually do. They fix this. They’re not sexy. They don’t write papers about breakthrough algorithms. They write code that makes sure your data is the same thing in two different places. That’s the whole job.
Why Companies Keep Making This Mistake
Executives see a demo of ChatGPT doing something amazing. They want that for their company. They imagine their customer support team replaced by AI. Their financial analysis automated. Their marketing is optimized by algorithms.
So they approve a budget. Hire a data science team. Buy some GPUs. Point everything at their database.
Weeks pass. The data science team isn’t making progress. They’re stuck. What they don’t say in meetings is that they’re stuck because the data is broken.
Customer IDs are formatted in three different ways. Product names are spelled inconsistently. Transaction dates are missing. Currency values are stored with different decimal places. Addresses have apartment numbers in one system and not in another.
The data scientists could fix these problems. But that’s not what you hired them for. You paid them to build models, not to spend their time cleaning databases. So they either try to force the data through anyway, producing bad models, or they quit and move to companies with better data infrastructure.
The executive team doesn’t understand why the project is delayed. They assumed data was just there, ready to use. Nobody explained that enterprise data is a disaster by default.
This is where data engineering services come in. These people expect data to be broken. They know how to fix it. More importantly, they know how long it takes and how much it costs. They can tell you upfront that the first three months of your project will be data preparation, not model building.
What Actually Happens When You Hire Data Engineers
A data engineering services team shows up and immediately asks questions that annoy executives:
Where does your data actually live? How many database systems do you have? How do your systems communicate? Do you have documentation? What’s your backup strategy?
Then they spend a week looking at everything. They map out your data architecture. It looks like spaghetti. Wires going everywhere. Multiple databases doing the same job in different ways.
They find your customer database has 500,000 records. But 80,000 of them are probably duplicates. Same person, different variations of the name, different addresses listed at different times. Your sales system thinks you have one set of customers. Your accounting system thinks you have a different set. Your support system has a third version.
This is the actual job of data engineering services. Not building models. Resolving which customer records actually refer to the same person. Merging them without losing information. Creating a single source of truth.
For your product database, they’ll find that manufacturing has one naming convention for products. Marketing uses a different convention. Sales uses a third. Inventory is inconsistent about which system they’re using. Data engineering services will standardize all of this. They’ll create rules about how products are named. They’ll update existing records to follow those rules. They’ll set up processes so new products follow the convention automatically.
For transactions and financial data, they’ll make sure every transaction has a consistent timestamp, consistent currency representation, and consistent accounting codes. They’ll backfill missing historical data where possible. They’ll set up monitoring so gaps are caught immediately in the future.
Then they’ll build pipelines. Real pipelines. Not someone running manual SQL queries at midnight. Automated processes that move data from your source systems into a unified database continuously. These pipelines validate data as it moves. If something looks wrong, they alert someone. If something is correct, it keeps flowing.
Big Data Engineering Services For When Your Problem Is Massive
Most companies dealing with data engineering services are solving normal-sized problems. Thousands or maybe millions of records. One or two databases. Cleanup takes weeks or months.
Then there are companies with big data engineering service problems.
A major bank processes millions of transactions daily. Billions of them per year. That’s petabytes of data. Their systems don’t just have a customer database and a transaction database. They have transaction systems for different business lines. They have legacy systems running on mainframes alongside modern cloud systems. They have data in New York and London and Singapore all needing to sync. They have regulatory systems that need specific data formats. They have fraud detection systems that need real-time data. They have trading systems that need microsecond-level accuracy.
Big data engineering services is a completely different job. You need specialists who understand distributed systems. People who’ve worked with Hadoop or Spark. People who understand how to move terabytes of data. People who know how to set up systems that process data in real-time across multiple continents.
A retail company with 10,000 stores selling products to millions of customers has big data problems. Every store generates sales data. Every inventory movement is tracked. Customers shop online and in-store. Supply chain data comes from manufacturers. This creates massive data volumes that need to move, be processed, and be accessible instantly to support real-time business decisions.
Big data engineering services costs 3-5x more than normal data engineering services. But for companies operating at scale, it’s non-optional. You can’t use the same approaches that work for cleaning up a single database when you’re managing data from dozens of systems handling trillions of transactions annually.
The Real Cost Of Skipping This
Here’s what happens when companies try to build AI without proper data engineering services:
Month one: Excitement. You hire a data science team. They start exploring data. They write initial models.
Month two: Reality sets in. They realize the data doesn’t support what they’re trying to do. They push back. You hire more people. Maybe an external consultant.
Month three: The project is already over budget. You’re not building models anymore. You’re in crisis mode, trying to understand why the data won’t cooperate. People are frustrated. The data scientists you hired are looking for other jobs.
Month four: You kill the project. It’s costing too much. It’s not producing results. You lost $600,000 and the team you hired.
Alternatively, you hire data engineering services first:
Month one: Assessment and planning. Data engineers map out your data landscape. They give you a realistic timeline and budget. It’s longer and more expensive than you wanted. You accept it because the alternative is knowing the project will fail.
Months two through four: Data engineering work happens. It’s boring. Nothing visible is built. But databases are cleaned. Systems are integrated. Pipelines are constructed. Quality monitoring is implemented.
Month five: You’re ready to build models. Your data scientists get clean, consistent data. They produce working models in weeks instead of being stuck for months.
Month six: You launch something. It actually works.
Total project cost with data engineering services: more than the failed project in the first scenario. But you have something that works. Your AI actually helps your business.
Picking A Data Engineering Services Team
Not all data engineering services are the same.
Some firms are just SQL experts who’ve rebranded themselves as data engineers. They can write queries but don’t understand modern data architecture.
Good data engineering services teams have worked with cloud platforms. They understand AWS and Google Cloud and Azure. They know Snowflake or BigQuery. They’ve built data lakes. They understand both SQL databases and NoSQL systems. They know when to use which.
They can talk intelligently about data pipelines. Apache Kafka. Real-time processing. Batch processing. They understand the tradeoffs. They don’t use the same approach for everything.
They care about governance. They ask about compliance requirements. They ask about who should have access to what data. They build systems that enforce these policies automatically, not through human oversight.
If you’re dealing with big data engineering services, they have experience with distributed systems. They’ve worked at scale. They understand how data moves across systems. They’ve dealt with consistency problems at massive scale.
Ask them about their previous projects. What did they build? What went well? What was harder than expected? Have them explain something technical in a way you understand. If they’re vague or if they explain things like you’re stupid, move on.
What It Actually Costs
A typical data engineering services engagement costs $150,000 to $400,000 for a mid-sized company. That’s for assessment, cleanup, and basic pipeline building. It takes three to six months.
Big data engineering services costs more. We’re talking $500,000 to $2 million for companies at serious scale. Timeline is six to twelve months or longer.
This sounds expensive. But compare it to the alternative. Your AI project fails, costing $1 million, and you have nothing to show for it. Or you rebuild it properly the second time, spending another $1 million, and finally it works.
Or you do it right the first time. You spend $300,000 on data engineering upfront. Your AI project succeeds because the foundation is solid.
FAQ
Q: Can’t our current IT team do this? A: No. Your IT team maintains systems. Data engineers build systems for a different purpose. Your IT people aren’t trained for this. Neither were the data engineers, but they deliberately learned it. Hire specialists.
Q: How long does this actually take? A: Depends on your mess. If you have three databases and relatively clean data, maybe three months. If you’re a large enterprise with legacy systems and data corruption everywhere, expect six months to a year.
Q: Can we do a smaller pilot first? A: Yes. Pick one business unit or one critical dataset. Fix that. Learn what the process looks like. Then expand to the rest of the company. This is actually a smart approach.
Q: What’s the difference between data engineering and data science? A: Data scientists analyze data and build models. Data engineers build infrastructure so data scientists have data to work with. You need both. Hiring one without the other is setting up for failure.
Q: How do we know when we’re done? A: You’re done when your data scientists stop complaining about data quality and start building models. When new data sources integrate without weeks of manual work. When you can ask questions about your data and get answers quickly.
Q: Is there software that just does this automatically? A: No. Software is a tool. You need people who know how to use the tool for your specific situation. Your data is unique to your company. Your requirements are unique. No off-the-shelf software fixes that.
The Bottom Line
Your database is broken. Not catastrophically broken. Broken in the boring, expensive way that stops AI projects.
Data engineering services fix this. Not in a weekend. Not cheaply. But it actually works.
Every successful AI project at a big company started with serious data engineering work. Every failed AI project tried to skip this step.
Pick the path that works.