What is ETL in Business Intelligence: Use Cases

With increasing dependencies on data, business intelligence (BI) has become imperative for all business organizations. However, relevant data is found in diverse forms from various sources.

Several techniques exist to collect and leverage this data for different organizational workflows. ETL technique is one such solution that helps you effectively leverage business data for powerful analysis and revenue generation. This blog helps you understand what is ETL in business intelligence comprehensively.

Introduction to ETL in Business Intelligence

BI is a technological process that enables you to analyze data effectively for better decision-making. It involves implementing several processes, including data integration, analytics, and visualization, to help you gain meaningful data insights.

Business data can come from various sources, including ERP systems, CRM systems, and social media platforms. You can utilize this data efficiently if it is consolidated at a centralized location. This is where the ETL (Extract, Transform, Load) approach comes into the picture. The next section gives you an overview of the ETL process in detail.

ETL Process Overview

ETL is a data integration method that allows you to collect data from various sources. You can clean this data using suitable transformation tools or techniques and store it in an organized manner in a data repository. Here is a detailed explanation of each step of the ETL process:

Extract

This is the first step in which raw data is copied or exported from different source data systems to a staging area. Databases, Marketing apps, APIs, CSV files, and log files are some examples of data sources. Depending on your requirements, you can opt to extract data in real-time or in batches.

Transform

In the staging area, data processing is performed to transform the raw data into a standardized format to ensure its compatibility with destination systems and downstream applications. This involves the use of filtering, cleansing, de-duplicating, aggregating, and validating techniques. You can also summarize, translate, or perform calculations on this data.

Load

This is the last step in which the transformed data is moved from the staging area to a target data system. Databases, warehouses, and data lakes are some of the destinations where you can load your data. You can transfer this data using full, incremental, or real-time loading methods.

Why is ETL Important in Business Intelligence?

Here are some of the important functions fulfilled by the ETL process in business intelligence:

1. Faster Data Processing

Automated ETL tools in business intelligence enable you to convert the data into a suitable format by optimizing the transformation process. These tools simplify complex operations such as joining, filtering, aggregating, and enriching BI data, facilitating faster data processing.

Some ETL platforms support parallel processing, a technique to perform multiple tasks simultaneously on different nodes or servers. This enables you to process large datasets quickly, readily providing insights for better business decisions. 

2. Effective Data Analytics

The processed data obtained through the ETL process can be analyzed statistically by calculating central tendencies, standard deviation, or variance. You can also perform inferential statistics or hypothesis testing on your datasets to better understand data patterns and trends.

‍BI tools like Power BI, Tableau, or Looker Studio enable you to prepare graphs, charts, reports, and dashboards. You can gain insights about data that are not apparent from numerical representation. As a result, the ETL process improves the outcomes of data analytics for business intelligence.

3. Improved Master Data Management

Master data is foundational information in an organization and is essential for running important business operations. It includes data related to customers, products, employees, and financial assets.

Using the ETL method, you can consolidate the master data at a centralized location in a standardized form for better accessibility. You can perform different operations and implement data lineage and a data governance framework on the unified master dataset. This ensures that your business data complies with necessary data regulations.

4. Fostering Business Growth

You gain meaningful insights after analyzing the datasets processed through the ETL technique. These data insights are essential to develop efficient business strategies and make well-informed decisions for various operations.

You can leverage such benefits to stay ahead of your competitors and innovate with your business products for continued business success.

Data Sources for ETL

To perform ETL in business intelligence, you need to access data from different types of sources, which could contain structured, semi-structured, or unstructured data. Some of these sources are as follows:

1. Databases

Relational Database Systems

This includes data sources such as MySQL, PostgreSQL, Oracle, or MS SQL Server. Here, data is arranged in an organized manner in a tabular format, making search and query operations easier.

NoSQL Databases

NoSQL databases are designed to manage large datasets in a non-relational format. Owing to this format, they are widely used to store unstructured or semi-structured data. Databases such as MongoDB, Redis, or Cassandra are some examples of NoSQL databases.

2. Files

You can use the following files as data sources for business intelligence:

CSV

These are simple text files that enable you to store complex business datasets in a simplified form. You can easily import or export these files into your desired data system to perform further operations.

JSON

JSON files are text files that allow you to store data in human-readable format as key-value pairs and arrays. They contain a hierarchical structure and support various programming languages. This makes JSON files a versatile solution for handling and retrieving business data.

Flat Files

Flat files are data files that facilitate data storage in plain text format without defining the structural relationship between data records. Microsoft Word documents, text files, CSV, or TSV files are common examples of flat files. You can use them to retrieve customer, sales, or inventory data.

3. Data Warehouse

A data warehouse is a data management system used to store and analyze types of data. It is an essential component of BI systems that is designed to support decision-making by providing a unified view of historical data. Google BigQuery, Amazon Redshift, and Snowflake are some examples of data warehouses.

4. Data Lake

A data lake is a versatile solution designed to store data in its native format. A data lake allows you to handle structured, semi-structured, and unstructured data without predefined schemas. Amazon S3, Azure Blob Storage, and Google Cloud Storage are some examples of data lakes.

5. Cloud apps and SaaS platforms

Cloud and SaaS applications include customer relationship management (CRM) systems such as Salesforce or Zoho CRM and enterprise resource planning (ERP) systems like SAP. They contain data related to core business processes, including manufacturing, sales, supply chain, project management, sales data, customer profiles, or customer behavior data. 

6. Social Media Platforms

Your business pages on social media platforms such as X (Twitter), LinkedIn, Instagram, or Meta(Facebook) also generate large amounts of data. This data could be comments, likes, shares, or reposts. You can extract this data using the ETL approach and use it for robust marketing campaigns or brand sentiment analysis.

7. APIs

An application programming interface (API) is a set of programming protocols that facilitates communication and data exchange between different software applications. APIs can be used to retrieve business data from source applications in real or near real-time, allowing faster business operations.

8. Streaming Data

Real-time data streams are continuous data flows generated from sources such as real-time applications, IoT devices, social media feeds, and financial applications. You can process this data as soon as it is produced to perform real-time business tasks.       

How is Airbyte Powering Business Intelligence with its ETL Functionality?

After understanding what ETL is in business intelligence, you can implement this process using Airbyte, an efficient data movement tool. The platform offers an extensive library of 400+ connectors that can be used to collect and consolidate business data from disparate sources to a unified destination. If your desired connector is not present in the existing set of connectors, you can build one on your own using Airbyte’s connector development kit (CDK).

In addition to its user-friendly interface, Airbyte offers PyAirbyte to build custom data pipelines. It is an open-source Python library that allows you to use Airbyte connectors in your Python ecosystem. This library enables you to extract data from numerous sources and load it into SQL caches like Postgres, DuckDB, BigQuery, or Snowflake.

Let’s understand how to achieve ETL workflows with PyAirbyte:

• Step 1: The first step is to install PyAirbyte using PyPi.‍
• Step 2: Next, you can extract business data from various sources using Airbyte connectors.

• Step 3: The extracted data can be loaded into SQL caches and converted into Pandas Dataframe. Now, you can perform complex transformation operations to manipulate data according to your requirements using the Pandas library.
• ‍Step 4: Finally, you can load the standardized data to a PyAirbyte-supported destination data system to create a unified repository.

Some other important features of the Airbyte platform are as follows:

• Change Data Capture (CDC): Airbyte’s CDC feature allows you to capture incremental changes made to the source data systems and replicate them in the target data system. This enables you to utilize updated data for BI operations.
• ‍Support for Vector Databases: Airbyte supports vector databases such as Pinecone, Milvus, Weaviate, or Chroma. You can generate and store vector embeddings into a vector database of your choice and integrate it with LLMs for better semantic search and retrieval.
• ‍Streamline GenAI Workflows: Using Airbyte enables you to manage GenAI workflows effectively, as the platform allows you to load unstructured data directly into vector data stores.
• ‍RAG Transformations: You can integrate Airbyte with LLM frameworks like LangChain or LlamaIndex to perform RAG transformations such as chunking and indexing. This enables you to streamline the responses generated by LLMs.

ETL Best Practices for BI

Here are some best practices you should follow when implementing the ETL approach in BI:

Understand Your Business Needs

First, understand your business's specific data requirements and the sources of the relevant data. Then, identify the key metrics, analytics, and reports that you will need to support your business. This will help you design a robust ETL process with the right datasets that align with your business objectives.

Optimize Data Extraction

Prefer to use an incremental data extraction technique in which only newly added data or changes are extracted. This reduces the load on source data systems and minimizes data processing time while dealing with large datasets.

Build Scalable Structure

As your business grows, your ETL data pipelines should be capable of accommodating an increase in data volume. To accomplish this, you can use cloud-based infrastructure, as it allows you to easily add or remove additional computing resources. Auto-scaling facilitates the automatic adjustment of computing resources and can further contribute to making your ETL architecture scalable.

Automate the Process

Eliminate manual interventions by automating ETL workflows using no-code ETL tools. You can further streamline the integration processes using data orchestration tools like Airflow. Leveraging CI/CD pipelines to develop, test, and deploy this process would also be a suitable approach.

Implement Data Validation

Once the data is loaded into the destination system, validate it to ensure accuracy and quality data. This is essential for performing reliable data analysis and authentic BI reporting.

Real-time ETL for BI

Data sources such as social media applications or IoT devices continuously produce streams of data, and traditional ETL processes cannot handle this data effectively. As a result, businesses are shifting from traditional batch ETL to real-time ETL solutions to deliver quick services and get an edge over their competitors.

In real-time or streaming ETL process, data is ingested as soon as it is generated. Streaming software such as Apache Kafka helps you build real-time ETL data pipelines by capturing event streams instantly after their creation. This facilitates continuous data availability for BI systems, improving businesses' operational efficiency.

Monitoring and Maintaining ETL Processes

Periodic monitoring and proper maintenance of the ETL data pipeline enables you to identify failures, data inconsistencies, and performance bottlenecks. To accomplish this, you can opt for automated monitoring using tools such as Apache Airflow, AWS CloudWatch, or Google Cloud Monitoring.

For better surveillance, you can set performance metrics based on the time required for the ETL process, latency, database size, resource usage, and cost. To enhance the supervision further, you can opt to use version control systems. These systems enable you to manage changes in ETL pipelines by tracking updates and allowing you to reverse them in case of failure.

Future Trends in ETL for BI

• Real-time ETL Workflows: Currently, there is a shift from batch-based ETL process used to create centralized data warehouses. This is due to the increasing demand for real-time data processing, cloud-native infrastructure and distributed data systems. With growing data volumes, modern businesses need faster data pipelines for decision-making, accelerating the move towards streaming ETL solutions.
• ‍AI & ML Learning in ETL: There has also been an upward trend in the use of AI and machine learning to streamline the ETL process. AI and ML can automatically suggest quick transformation tips, detect data inconsistencies, or optimize data flows.
• ‍Zero ETL: For more efficient data operations, opting for the zero ETL method would facilitate data transfer from source to destination without building an ETL pipeline. It is a novel approach in which data can be manipulated during the querying process in a data lake or object storage system. This eliminates the need to perform data transformations separately. As a result, there is a reduction in latency, allowing you to query and analyze data directly from the source in real or near real-time.
• ‍Embedded Analytics: Embedded analytics is another upcoming trend in which you can directly embed BI components into your usual data workflows. This can help you enhance productivity and collaboration within your organization without switching workflows.
• ‍Decision Intelligence: You can also use decision intelligence software that uses machine learning algorithms to assist you in improving your decision-making capabilities. Tellius, Domo, and Decisions are some of the DI software that can be used to make well-informed business decisions.
• ‍Mobile-Based BI: To enable employees to work at their convenience, you can promote the use of mobile-based BI services. The growth in cloud-based technologies and high mobile internet speed has made it feasible to use mobile-supported BI applications.

Measuring ETL Impact on BI Success

To measure the effect of the ETL method on BI success, you can define key performance indicators (KPIs). These may include accuracy, user feedback, ETL processing time, and the adoption rate of the ETL approach by employees in your business organization.

You can check how your business has improved by analyzing the difference in revenue generation and customer satisfaction before and after adapting the ETL technique. You can also calculate the ROI of integrating data by comparing the cost of using the ETL method to business profits.

As a best practice, you should evaluate the outcomes of implementing the ETL approach in your business with industry standards and competitor benchmarks. This enables you to assess the areas where you still need to improve. You may also ask for feedback from employees, colleagues, and senior officials to understand what hurdles they are facing after adopting the ETL approach in business.

Conclusion

ETL process is important for business intelligence tasks as it allows you to convert raw data into actionable insights. You can use these insights for accurate analytics and business reporting. This information is used to optimize marketing strategy, streamline financial transactions, and speed up daily business operations.