Data engineering works a lot like cooking, and without a good recipe they can both go wrong! Data systems need the same three things. Raw data is our ingredients, data pipelines are our recipe, and data processing is the skill we use to turn it into great results.
We’re a Richmond-based data engineering company with over 20 years of experience working with enterprise companies. At MPP Insights, we help companies see what’s happening in their business and make decisions with confidence.
If you're convinced that data engineering is a lot like cooking, stay tuned! We’re going to share our very first recipe for great data engineering here. If you have lots of data in your business, you’ll need it. Just like you need food, not any food, but great food.
You can’t cook a dish if the ingredients are missing around the house. You need to:
- collect the ingredients;
- prepare them;
- follow a recipe.
Data systems work the same way.
Companies collect a lot of data from different places and tools. Let’s say CRM, Shopify, Google Analytics, and a few spreadsheets. Each system has its own data, in different places and in different formats.
If a company wants to answer questions like: “Which marketing campaign brings the most sales”, all the data must be combined in one place. Someone needs to collect the data, clean it, organize it, and store it in one place. That is what data engineers build. They build the pipes that move the data.
For cooking, you start with raw ingredients. Data engineers start with raw data. Then you prepare it, clean it, and combine everything into something useful.
Cooking with ingredients that are expired, low quality, or mixed with random things will make the dish taste bad. Even a professional chef can’t do magic with bad ingredients.
Data works the same way. The first rule in data engineering is very simple: if the data is bad, the result will also be bad. In data terms, we say, “Garbage in, garbage out.”
It means that if you start with messy or wrong data, no tool or dashboard can fix it later. Before companies analyze data, they must clean and organize it first. That is why clean data is the first ingredient of good data engineering.
Visual representation of a data pipeline designed by MPP Insights
Common Problems That Make Raw Data Messy
Missing Data
Some fields are empty. For example, a customer record might not have an email.
Duplicates
The same customer appears multiple times. For example:
John Smith
John A. Smith
J. Smith
John A. Smith
J. Smith
These might actually be the same person, but the system treats them as different people.
Different Formats
Different systems store data in different ways. For example dates might look like this:
03/02/2026
2026-03-02
March 2, 2026
2026-03-02
March 2, 2026
For a computer, those are three different formats.
Human Mistakes
People enter data manually, so mistakes happen. For example: wrong spelling or wrong numbers.
A chef doesn’t just throw everything into a pan for cooking. A recipe usually has steps like:
- chop the garlic;
- heat the oil;
- add the chili;
- mix the ingredients;
- cook for a certain time.
Data works the same way, there have to be steps like:
- collect the data;
- clean the data;
- transform the data;
- store the data.
The pipeline is basically the recipe for preparing data. Data pipelines create the path for data to travel automatically to where it will be used. We often use a simple example of a water pipeline; water flows from the water source, through the pipes, and comes to your house.
Data flows in a similar way. It moves from the data source, through the pipeline, to a storage system. For example, you might have data from a website, sales systems, and marketing tools. The pipeline collects that data, cleans and organizes it, and then the data gets stored in a data warehouse. After that, dashboards and reports use the data.
Cooking starts in the kitchen, and the kitchen is the infrastructure. Cooking in a bad kitchen with a broken stove will likely be a failure. For the third time, I have to say the same thing happens with data systems. Data engineering also needs good infrastructure.
Infrastructure is the foundation of the whole system. All that data needs a central place to live. This is where the data lives and where the pipelines run. Common examples are:
- Dedicated Database Servers running DBMS like Oracle or DB2
- Cloud-based storage platforms like Snowflake, Databricks
- On-premise/custom built data warehouses / data lakehouses / data lakes
- Hybrid environments of all sorts
If the infrastructure is weak, problems appear quickly, especially for companies that are growing fast. The system might become slow, hard to scale and probably more expensive.
We did something amazing in Yerevan, where our R&D office is located. We held the first AI/ML hackathon in Armenia. During the competition, teams used our MPP ETL tool with LLM extensions to analyze real datasets.
Take a look at our throwback blog about the Data Engineering & LLM Hackathon . You'll enjoy it.
Data engineers design the architecture of the system. They choose:
- where the data will be stored;
- which data warehouse to use;
- how data pipelines will run;
- how the system will scale as the company grows.
Having expensive knives and a professional stove don’t make someone a great chef. The ingredients for a jar of chili garlic crunch are simple. But the result depends on the skill to balance the flavors. These details come from experience, and that’s craftsmanship.
Data engineering works the same way. The tools might be similar for many companies, but the quality of the work depends on the people who build the system.
Data engineers need to think about things like:
- how the pipeline should be structured;
- how data should be organized;
- how to prevent errors;
- how to make the system reliable.
With more than 20 years of experience in data engineering, we know where problems usually happen, and we have the craft to design pipelines that work effectively. MPP Insights provides data engineering services in Richmond. We build reliable data pipelines and systems for enterprise companies.
When an experienced chef follows a good recipe with fresh ingredients and the right process, they will serve a delicious dish.
In data engineering, all the preparation leads to clear answers about the business. The ultimate goal is understanding what is happening in the business, because the insights you get from this preparation help a company make better decisions.
We deliver a simple message: when everything is done right, people can trust the result. For food, that’s great flavor. For business, that’s clear insights.
At MPP Insights, we provide data engineering services and build data systems with quality ingredients, a clear recipe, and real craftsmanship. We are the data craftsmen with the right ingredients and the recipe for your business growth.
