What is ETL and its advantage in Business Intelligence

Published on Oct 1, 2019

Updated on May 22, 2024

ETL (Extract, Transform, Load) is a process of extracting data from different data sources; manipulating them according to business calculations; loading the modified data into a different data warehouse. Because of the in-depth analytics data it provides, ETL function lies at the core of Business Intelligence systems. With ETL, enterprises can obtain historical, current, and predictive views of real business data. Let’s look at some ETL features that are necessary for business intelligence.

The Importance of ETL in Business Intelligence

Businesses rely on the ETL process for a consolidated data view that can drive better business decisions. The following ETL features justify the point.

High-level Data Mapping

Leveraging data and transforming them into actionable insights is a challenge with dispersed and voluminous data. Data mapping simplifies database functionalities like integration, migration, warehousing, and transformation.

ETL allows mapping data for specific applications. Data mapping helps in establishing a correlation between different data models.

Data Quality & Big Data Analytics

Huge volumes of data aren’t of much use in their raw form. Applying algorithms on raw data often leads to ambiguous results. It needs structuring, analyzing, and interpreting well to gain powerful insights. ETL also ensures the quality of data in the warehouse through standardization and removing duplicates.

ETL tools combine data integration and processing, making it easier to deal with voluminous data. In its data integration module, ETL assembles data from disparate sources. Post integration, it applies business rules to provide the analytics view of the data.

[Also read: Popular ETL Tools for 2020]

Automatic & Faster Batch Data Processing

The modern-day ETL tools run on scripts, which are faster than traditional programming. Scripts are a lightweight set of instructions that execute specific tasks in the background. ETL also ‘batch’ processes data like moving huge volumes of data between two systems in a set schedule.

Sometimes the volume of incoming data increases to millions of events per second. To handle such situations, stream processing (monitoring and batch processing data) can help in timely decision making. For example, Banks batch process the data generally during night hours to resolves the entire day’s transactions.

Master Data Management

Using ETL and data integration, enterprises can obtain the “best data view” across multiple sources.

How ETL Works?

ETL systems are designed to accomplish three complex database functions: extract, transform and load.

#1 Extraction

Here, a module extracts data from different data sources independent of file formats. For instance, banking and insurance technology platforms operate on different databases, hardware, operating system, and communication protocols. Also, their system derives data from a variety of touchpoints like ATMs, text files, pdfs, spreadsheets, scanned forms, etc. The extraction phase maps the data from different sources into a unified format before processing.

ETL systems ensure the following while extracting data.

Removing redundant (duplicate) or fragmented data
Removing spam or unwanted data
Reconciling records with source data
Checking data types and key attributes.

#2 Transformation

This stage involves applying algorithms and modifying data according to business-specific rules. The common operations performed in ETL’s transformation stage is computation, concatenation, filters, and string operations like currency, time, data format, etc. It also validates the following-

Data cleaning like adding ‘0’ to null values
Threshold validation like age cannot be more than two digits
Data standardization according to the rules and lookup table.

#3 Loading

Loading is a process of migrating structured data into the warehouse. Usually, large volumes of data need to be loaded in a short time. ETL applications play a crucial role in optimizing the load process with efficient recovery mechanisms for the instances of loading failures.

A typical ETL process involves three types of loading functions-

Initial load: it populates the records in the data warehouse.
Incremental load: it applies changes (updates) periodically as per the requirements.
Full refresh: It reloads the warehouse with fresh records by erasing the old contents.

The ETL systems validate the following data loading parameters-

The Business Intelligence report on view layer matches with the loaded facts
Data consistency between the data warehouse and the history table.
Models are based on transformed data and not the raw data from the original databases.

The modern-day ETL applications utilize NoSQL database systems for warehousing. NoSQL systems are suitable for big-data and real-time web-applications. NoSQL executes queries faster than traditional databases and is more memory efficient.

ETL Business Applications

Transactional databases are not enough to resolve complex business queries. Also, dealing with unorganized data formats is more time-taking. ETL can help in obtaining-

Memory efficiency
Real-time query processing
Mapping data historical, current, and predictive data to derive actionable insights
Smart data storage and retrieval.

Almost all industries can deploy the benefits of ETL systems. However, businesses like banking, insurance, customer relations, finance, and healthcare are the early adopters of this technology.

If your business needs intelligent data processing, we’re here to listen to your requirements. Drop us a word at hello@mantralabsglobal.com to know about our previous works on developing ETL applications.

Let’s take a trip back in time—2008. Netflix was nothing like the media juggernaut it is today. Back then, they were a DVD-rental-by-mail service trying to go digital. But here’s the kicker: they hit a major pitfall. The internet was booming, and people were binge-watching shows like never before, but Netflix’s infrastructure couldn’t handle the load. Their single, massive system—what techies call a “monolith”—was creaking under pressure. Slow load times and buffering wheels plagued the experience, a nightmare for any platform or app development company trying to scale

That’s when Netflix decided to do something wild—they broke their monolith into smaller pieces. It was microservices, the tech equivalent of turning one giant pizza into bite-sized slices. Instead of one colossal system doing everything from streaming to recommendations, each piece of Netflix’s architecture became a specialist—one service handled streaming, another handled recommendations, another managed user data, and so on.

But microservices alone weren’t enough. What if one slice of pizza burns? Would the rest of the meal be ruined? Netflix wasn’t about to let a burnt crust take down the whole operation. That’s when they introduced the Circuit Breaker Pattern—just like a home electrical circuit that prevents a total blackout when one fuse blows. Their famous Hystrix tool allowed services to fail without taking down the entire platform.

Fast-forward to today: Netflix isn’t just serving you movie marathons, it’s a digital powerhouse, an icon in platform engineering; it’s deploying new code thousands of times per day without breaking a sweat. They handle 208 million subscribers streaming over 1 billion hours of content every week. Trends in Platform engineering transformed Netflix into an application dev platform with self-service capabilities, supporting app developers and fostering a culture of continuous deployment.

Did Netflix bring order to chaos?

Netflix didn’t just solve its own problem. They blazed the trail for a movement: platform engineering. Now, every company wants a piece of that action. What Netflix did was essentially build an internal platform that developers could innovate without dealing with infrastructure headaches, a dream scenario for any application developer or app development company seeking seamless workflows.

And it’s not just for the big players like Netflix anymore. Across industries, companies are using platform engineering to create Internal Developer Platforms (IDPs)—one-stop shops for mobile application developers to create, test, and deploy apps without waiting on traditional IT. According to Gartner, 80% of organizations will adopt platform engineering by 2025 because it makes everything faster and more efficient, a game-changer for any mobile app developer or development software firm.

All anybody has to do is to make sure the tools are actually connected and working together. To make the most of it. That’s where modern trends like self-service platforms and composable architectures come in. You build, you scale, you innovate.achieving what mobile app dev and web-based development needs And all without breaking a sweat.

Is Mantra Labs Redefining Platform Engineering?

We didn’t just learn from Netflix’s playbook; we’re writing our own chapters in platform engineering. One example of this? Our work with one of India’s leading private-sector general insurance companies.

Their existing DevOps system was like Netflix’s old monolith: complex, clunky, and slowing them down. Multiple teams, diverse workflows, and a lack of standardization were crippling their ability to innovate. Worse yet, they were stuck in a ticket-driven approach, which led to reactive fixes rather than proactive growth. Observability gaps meant they were often solving the wrong problems, without any real insight into what was happening under the hood.

That’s where Mantra Labs stepped in. Mantra Labs brought in the pillars of platform engineering:

Standardization: We unified their workflows, creating a single source of truth for teams across the board.

Customization: Our tailored platform engineering approach addressed the unique demands of their various application development teams.

Traceability: With better observability tools, they could now track their workflows, giving them real-time insights into system health and potential bottlenecks—an essential feature for web and app development and agile software development.

We didn’t just slap a band-aid on the problem; we overhauled their entire infrastructure. By centralizing infrastructure management and removing the ticket-driven chaos, we gave them a self-service platform—where teams could deploy new code without waiting in line. The results? Faster workflows, better adoption of tools, and an infrastructure ready for future growth.

But we didn’t stop there. We solved the critical observability gaps—providing real-time data that helped the insurance giant avoid potential pitfalls before they happened. With our approach, they no longer had to “hope” that things would go right. They could see it happening in real-time which is a major advantage in cross-platform mobile application development and cloud-based web hosting.

The Future of Platform Engineering: What’s Next?

As we look forward, platform engineering will continue to drive innovation, enabling companies to build scalable, resilient systems that adapt to future challenges—whether it’s AI-driven automation or self-healing platforms.

If you’re ready to make the leap into platform engineering, Mantra Labs is here to guide you. Whether you’re aiming for smoother workflows, enhanced observability, or scalable infrastructure, we’ve got the tools and expertise to get you there.

Role of ETL in Business Intelligence