The Real Costs of ETL and How MindsDB Eliminates Them

Businesses rely on data to make faster, smarter decisions. Traditionally, that starts with ETL—Extract, Transform, Load—a process that moves data from various systems into one place for analysis. But while ETL has been a foundation of analytics for years, it comes with trade-offs in time, cost, and complexity that many teams can no longer afford.

But while ETL is a cornerstone of many analytics architectures, it comes with real costs - in time, money, and flexibility - that can weigh down even the most modern data teams.

According to IBM, in markets where real-time response is critical—like retail, healthcare, and finance—delayed insights can be devastating. Retailers, for instance, are losing up to $471 billion annually due to overstocking and inventory inefficiencies, while companies that rely on outdated data for AI-driven predictions see about a 6% global revenue loss, which translates to approximately $406 million in missed revenue opportunities. 

This underscores how even brief delays or stale data can compound into massive financial impacts—far beyond what many organizations anticipate, so how can MindsDB flip the script by bringing AI directly to the data - no ETL required? Let’s explore.

The Real Costs of ETL

1. Infrastructure and Tooling Overhead

Setting up ETL pipelines often requires multiple tools: data connectors, transformation engines, workflow orchestrators, and data warehouses. This stack isn’t cheap. Whether you're paying for commercial ETL services or managing open-source tools, infrastructure costs can quickly spiral.

Associated Cost: Licensing fees, cloud compute usage, storage, and DevOps overhead for maintaining complex pipelines.

2. Engineering Bottlenecks

ETL pipelines are usually built and maintained by data engineers. These pipelines require constant tuning - especially as data sources, formats, and business needs evolve. This creates a bottleneck between analysts, data scientists, and the insights they need.

Associated Cost: Developer time spent managing pipelines instead of building products or models.

3. Data Latency

ETL processes are typically batch-based. This means there's a delay between when data is generated and when it becomes available for analysis - from hours to even days.

Associated Cost: Missed opportunities for real-time decisions or dynamic personalization.

4. Data Duplication and Compliance Risk

Moving data across systems introduces redundancy and governance challenges. Maintaining consistency between source systems and the destination warehouse becomes a constant battle.

Associated Cost: Increased risk of data breaches, compliance issues, and data versioning nightmares.

5. Slower Time to Insight

Every step in an ETL pipeline adds delay between a question being asked and an answer being available. Business teams often wait days or weeks for answers they needed yesterday.

Associated  Cost: Lost agility. Slower feedback loops. Decisions based on outdated information.

6. Tool and Team Fragmentation

ETL often separates teams into silos—engineers own the pipelines, analysts own the queries, and data scientists wait in line. Collaboration suffers because the workflow isn’t shared.

Associated Cost: More context-switching, slower iteration, and duplicated effort.

With MindsDB, there’s no “ETL tax”.

MindsDB: AI Without the ETL Baggage

MindsDB changes how you work with AI by keeping everything where your data already is. Instead of exporting data to separate systems for modeling, MindsDB connects directly to your database—whether that’s MySQL, PostgreSQL, MongoDB, Snowflake, or the other 200+ data connectors available.

Here’s how MindsDB eliminates the ETL tax:

In-Database Machine Learning

No data duplication, no syncing. MindsDB runs the model where the data lives, which means less lag, lower infrastructure costs, and fewer compliance headaches.

Use SQL, or no code at all

You can train, deploy, and query AI models using plain SQL. That means analysts and developers alike can build predictive models without managing ETL pipelines or learning new tools.

Real-Time Predictions

Since MindsDB sits inside the database layer, it can provide real-time predictions on fresh data - no waiting for the next ETL batch.

Simplified Architecture

When you don’t have to glue together multiple tools and workflows, things get a lot simpler. Less complexity means fewer things to break—and lower costs.

MindsDB makes all of that happen right inside your existing setup. A few SQL commands later, you're training models and getting live forecasts straight from your BI tool or application. That’s time saved, infrastructure avoided, and insights delivered when they actually matter.

A Real-World Example: Forecasting Without ETL

Imagine you're a retailer looking to forecast inventory needs across hundreds of stores. With a traditional ETL/AI stack, you’d:

1. Extract sales data into a staging area.

2. Transform it into a training format.

3. Load it into a separate environment for AI modeling.

4. Export predictions back into your warehouse.

This ETL-heavy approach creates delays, increases costs, and introduces unnecessary complexity—especially when data changes often or needs to stay secure and compliant.

With MindsDB, there’s no need for staging areas, external modeling tools, or batch exports, you can simply connect and unify your data, and then query it using natural language.

To get started, you can access MindsDB through local Docker Installation, MindsDB’s Extension via Docker Desktop.

To connect your database to MindsDB, the CREATE DATABASE statement will be used.

CREATE DATABASE postgresql_conn
WITH ENGINE = 'postgres',
PARAMETERS = {
  "user": "demo_user",
  "password": "demo_password",
  "host": "samples.mindsdb.com",
  "port": "5432",
  "database": "demo",
  "schema": "sample_data"
};

You can set your LLM model which will be used by default by the agent by navigating to Models in Settings.

Now an agent can be created using the CREATE AGENT syntax.

CREATE AGENT supplychain_agent
USING
  data = {
       "tables": ["postgresql_conn.supplychain_inventory", "postgresql_conn.supplychain_logistics", "postgresql_conn.supplychain_products", "postgresql_conn.supplychain_suppliers" ]
  },
  prompt_template=' You are an analyst that provides insights to data and forecasts about supply inventory
      postgresql_conn.supplychain_inventory stores inventory information of the products
      postgresql_conn.supplychain_logistics stores logistics information
      postgresql_conn.supplychain_products stores information about products
      postgresql_conn.supplychain_suppliers stores information about the suppliers
      ';

The agent has been created with the name supplychain_agent and provided the following parameters:

• data: This parameter holds the data linked to the agent, including knowledge bases and data sources integrated with MindsDB.

  • tables: Stores the list of tables from data sources integrated with MindsDB.

• prompt_template: This parameter stores instructions for the agent and description of data. It is recommended to provide data description of the data sources listed in the knowledge_bases and tables parameters to help the agent locate relevant data for answering questions.

With the agent ready, we can now execute queries that return structured insights across Logistics, Products, Suppliers, and Inventory- unlocking a 360° view of operations.

You can ask the agent to provide the top selling products.

SELECT answer
FROM supplychain_agent
WHERE question = 'What are the top selling products?';

This enables businesses to maximize profits, help avoid costly stock outs or overstocking and upsell strategies around what customers are already choosing.

You can also query the agent to provide more inventory insights. You can gain insights on a specific product that is low or multiple products that are low in stock and will need planning to procure more products.

SELECT answer
FROM supplychain_agent
WHERE question = 'What is the current stock level of Porefect?';

SELECT answer
FROM supplychain_agent
WHERE question = 'Which products are low in stock?';

Logistics plays a great part in making sure customers receive their products, we can gain insights on shipping carriers.

SELECT answer
FROM supplychain_agent
WHERE question = 'What are the recent logistics updates for shipments?';

During transit, delivery orders can be damaged which can result into a return order, costing the company. You can gain insight to see which shipping carriers fail inspection for delivery and the cost associated with it.

SELECT answer
FROM supplychain_agent
WHERE question = ' Which shipping carriers fail inspection for certain products? And how much was is the shipping cost?';

Let's look at insights for suppliers. You can determine the delivery times which will assist in planning when restocking products.

SELECT answer
FROM supplychain_agent
WHERE question = 'What are the delivery times for different suppliers?';

You can determine the production volume of products and the total value/cost the company spends on the different suppliers.

SELECT answer
FROM supplychain_agent
WHERE question = ' Who are the top suppliers by volume or value?';

Marketing is a big part of selling your product, and the best way to sell a product is to know your customer. You can ask the agent to provide insights on what the revenue is for each demographic, and try to market to those demographics accordingly.

SELECT answer
FROM supplychain_agent
WHERE question = ' Which customer demographic orders the most products that brings in the highest revenue?';

We can see that the customer demographic that brings in the most revenue do not disclose their information, with females following in second. This can alarm Marketing to push efforts that will motivate their customers to provide information about their demographic to get to know their customer better.

Why It Works

By keeping the model inside the database layer, forecasting becomes:

• Faster — You can go from idea to insight in minutes, not weeks

• Simpler — Fewer tools, fewer steps, fewer things to break

• More secure — Sensitive data never leaves your trusted environment

• Always up to date — Predictions reflect the most recent data, not last night’s batch

Real Impact

This approach isn’t just more elegant—it has real business impact.

• Sales teams can adjust plans mid-quarter.

• Supply chain managers can respond to demand changes immediately.

• Analysts can experiment without waiting on engineering.

All of this happens within your existing workflows—no retraining, no exporting, no ETL.

Forecasting without ETL isn’t just possible—it’s practical. 

With MindsDB, all of that can happen directly in your database. A few SQL commands later, you’ve created an AI agent or time series model and are making live forecasts—right from your BI tool or application. That’s hours of engineering saved, weeks shaved off deployment, and insights delivered in real time. 

Missed decisions cost money.

According to industry estimates, businesses lose up to $1.4M per year in revenue due to delayed decisions caused by outdated pipelines. MindsDB helps eliminate that drag—so your data, and your decisions, stay one step ahead.

Final Thoughts

ETL isn’t going away overnight, but for many use cases - especially real-time AI - it’s becoming increasingly unnecessary. The true cost of ETL lies not just in tools or computers, but in lost agility, slower insights, and engineering overhead.

MindsDB offers a powerful alternative: bring the models to the data, not the other way around.

If you're ready to unlock the full potential of your data without the drag of ETL, it’s time to take MindsDB for a spin. Contact our team to get started.