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.

Not too long ago, storing data meant dedicating an entire room to massive CPUs. Then came the era of personal computers, followed by external hard drives and USB sticks. Now, storage has become practically invisible, floating somewhere between data centers and, well, the clouds—probably the ones in the sky. Cloud computing continues to evolve, As cloud computing evolves, optimizing costs without sacrificing performance has become a real concern. How can organizations truly future-proof their cloud strategy while reducing costs? Let’s explore new-age cloud optimization strategies in 2025 designed for maximum performance and cost efficiency.

Smarter Cloud Strategies: Cutting Costs While Boosting Performance

1. AI-Driven Cost Prediction and Auto-Optimization

When AI is doing everything else, why not let it take charge of cloud cost optimization too? Predictive analytics powered by AI can analyze usage trends and automatically scale resources before traffic spikes, preventing unnecessary over-provisioning. Cloud optimization tools like AWS Compute Optimizer and Google’s Active Assist are early versions of this trend.

How it Works: AI tools analyze real-time workload data and predict future cloud resource needs, automating provisioning and scaling decisions to minimize waste while maintaining performance.
Use case: Netflix optimizes cloud costs by using AI-driven auto-scaling to dynamically allocate resources based on streaming demand, reducing unnecessary expenditure while ensuring a smooth user experience.

2. Serverless and Function-as-a-Service (FaaS) Evolution

That seamless experience where everything just works the moment you need it—serverless computing is making cloud management feel exactly like that. Serverless computing eliminates idle resources, cutting down costs while boosting cloud performance. You only pay for the execution time of functions, making it a cost-effective cloud optimization technique.

How it works: Serverless computing platforms like AWS Lambda, Google Cloud Functions, and Azure Functions execute event-driven workloads, ensuring efficient cloud resource utilization while eliminating the need for constant infrastructure management.
Use case: Coca-Cola leveraged AWS Lambda for its vending machines, reducing backend infrastructure costs and improving operational efficiency by scaling automatically with demand.

3. Decentralized Cloud Computing: Edge Computing for Cost Reduction

Why send all your data to the cloud when it can be processed right where it’s generated? Edge computing reduces data transfer costs and latency by handling workloads closer to the source. By distributing computing power across multiple edge nodes, companies can avoid expensive, centralized cloud processing and minimize data egress fees.

How it works: Companies deploy micro data centers and AI-powered edge devices to analyze data closer to the source, reducing dependency on cloud bandwidth and lowering operational costs.
Use case: Retail giant Walmart leverages edge computing to process in-store data locally, reducing latency in inventory management and enhancing customer experience while cutting cloud expenses.

4. Cloud Optimization with FinOps Culture

FinOps (Cloud Financial Operations) is a cloud cost management practice that enables organizations to optimize cloud costs while maintaining operational efficiency. By fostering collaboration between finance, operations, and engineering teams, FinOps ensures cloud investments align with business goals, improving ROI and reducing unnecessary expenses.

How it works: Companies implement FinOps platforms like Apptio Cloudability and CloudHealth to gain real-time insights, automate cost optimization, and enforce financial accountability across cloud operations.
Use case: Early adopters of FinOps were Adobe, which leveraged it to analyze cloud spending patterns and dynamically allocate resources, leading to significant cost savings while maintaining application performance.

5. Storage Tiering with Intelligent Data Lifecycle Management

Not all data needs a VIP seat in high-performance storage. Intelligent data lifecycle management ensures frequently accessed data stays hot, while infrequently used data moves to cost-effective storage. Cloud-adjacent storage, where data is stored closer to compute resources but outside the primary cloud, is gaining traction as a cost-efficient alternative. By reducing egress fees and optimizing storage tiers, businesses can significantly cut expenses while maintaining performance.

How it’s being done: Companies use intelligent storage optimization tools like AWS S3 Intelligent-Tiering, Google Cloud Storage’s Autoclass, and cloud-adjacent storage solutions from providers like Equinix and Wasabi to reduce storage and data transfer costs.
Use case: Dropbox optimizes cloud storage costs by using multi-tiered storage systems, moving less-accessed files to cost-efficient storage while keeping frequently accessed data on high-speed servers.

6. Quantum Cloud Computing: The Future-Proof Cost Gamechanger

Quantum computing sounds like sci-fi, but cloud providers like AWS Braket and Google Quantum AI are already offering early-stage access. While still evolving, quantum cloud computing has the potential to process vast datasets at lightning speed, dramatically cutting costs for complex computations. By solving problems that traditional computers take days or weeks to process, quantum computing reduces the need for excessive computing resources, slashing operational costs.

How it works: Cloud providers integrate quantum computing services with existing cloud infrastructure, allowing businesses to test and run quantum algorithms for complex problem-solving without massive upfront investments.
Use case: Daimler AG leverages quantum computing to optimize battery materials research, reducing R&D costs and accelerating EV development.

7. Sustainable Cloud Optimization: Green Computing Meets Cost Efficiency

Running workloads when renewable energy is at its peak isn’t just good for the planet—it’s good for your budget too. Sustainable cloud computing aligns operations with renewable energy cycles, reducing reliance on non-renewable sources and lowering overall operational costs.

How it works: Companies use carbon-aware cloud scheduling tools like Microsoft’s Emissions Impact Dashboard to track energy consumption and optimize workload placement based on sustainability goals.
Use case: Google Cloud shifts workloads to data centers powered by renewable energy during peak production hours, reducing carbon footprint and lowering energy expenses.

The Next Frontier: Where Cloud Optimization is Headed

Cloud optimization in 2025 isn’t just about playing by the old rules. It’s about reimagining the game entirely. With AI-driven automation, serverless computing, edge computing, FinOps, quantum advancements, and sustainable cloud practices, businesses can achieve cost savings and high cloud performance like never before.

Organizations that embrace these innovations will not only optimize their cloud spend but also gain a competitive edge through improved efficiency, agility, and sustainability. The future of cloud computing in 2025 isn’t just about cost-cutting—it’s about making smarter, more strategic cloud investments.

At Mantra Labs, we specialize in AI-driven cloud solutions, helping businesses optimize cloud costs, improve performance, and stay ahead in an ever-evolving digital landscape. Let’s build a smarter, more cost-efficient cloud strategy together. Get in touch with us today!

Are you ready to make your cloud strategy smarter, cost-efficient, and future-ready with AI-driven, serverless, and sustainable innovations?

Role of ETL in Business Intelligence