ETL in Business Intelligence

ETL (Extract-Transform-Load) is the process of moving data from disparate data sources into one or multiple unified data warehouses for analysis.

ETL plays a crucial role in business intelligence.

By enabling the seamless integration and analysis of data from many sources, ETL empowers organizations to collate and make sense of the vast amounts of data generated daily in a business setting.

Specifically, ETL can help with the following business intelligence use cases:

Data Uniformity and Accuracy: ETL ensures that data is consistent and accurate by organizing data to a single, unified schema.
Data Governance: Governance refers to the practice of maintaining a business's control over its data and ensuring data privacy and security. ETL processes support the development of an organization's data governance strategy by keeping a record of data movement between sources and creating a generalized understanding of each data source's sensitivity to user privacy and security.
Automated Intelligence: ETL processes should not be seen as a one-time task. Instead, ETL can be automated to adapt to the changing needs and demands of the organization. Concerning ETL for business intelligence, there is one thing to note. Do not be surprised if your team looks to hire a group of ETL developers. Or even business consultants that understand the whole process. Individuals on-premises with this knowledge can make it easier for your organization to transform data.

What is ETL?

ETL stands for extract, transform, and load. ETL manipulates data from various sources including cloud databases, third-party applications, and in-house systems. By combining data from multiple data sources, ETL can help companies obtain actionable insights on their data.

Extract

Extract is the first stage of the ETL process. Extraction refers to collecting data from various data sources including: CRMs, social media, spreadsheets, and APIs. This data can come from a wide range of internal and external sources and can be stored in various formats.

Unifying data across different data sources can be advantageous to a business's data strategy. ETL enables business intelligence tools to interpret data silo'd across different source systems in one, unified manner, giving a holistic view of business and financial performance.

Transform

After data is extracted, data processing can ensue. The transformation step involves converting the raw data into a format that can be more easily analyzed. If teams do not have access to raw data, flat files from relational databases can work too. Companies can achieve transformation by aggregating, normalizing, or transforming data into a model; no code ETL tools can provide easy-to-use UIs to transform data into a schema that's usable.

Organizations can choose to transform data in real-time or in batch. Both methods of data transformation allow for automation and loading into a data warehouse or data repository.

Load

The final load step involves loading the transformed data into a target repository, such as a cloud data warehouse (Snowflake, RedShift), where it can be analyzed.

Data warehousing can be an effective way to load data securely and holistically. Various data stores can accept different data types. ETL enables organization to maximize their data storage and operate on data in a single, unified manner.

ETL vs ELT

It is important to note that ETL processes are not the only way to transfer data for business intelligence purposes. Another common approach is ELT, which stands for Extract, Load, and Transform.

In ELT, the raw data is loaded into the target repository first and then transformed within the repository. Data can further be extracted out of the data repository and loaded into application-level databases as well, making ETL a bi-directional data process.

Both ETL and ELT have their strengths and weaknesses. The choice of which approach to use often depends on the organization's specific requirements.

Advantages of using ETL for Business Intelligence

ETL enables organizations to make informed decisions. It does this by leveraging the valuable insights extracted from large datasets.

Below are three additional advantages of using ETL for business intelligence.

Use Case #1: Increase Data Efficiency

ETL enables businesses to leverage their big data in a timely and efficient manner via consolidation. By reducing the complexity and time associated with managing multiple data sources, business can maximize the business potential of their data.

For example, artificial intelligence and machine learning companies can leverage ETL pipelines to establish large training data sets in an efficient, automated manner.

ETL reduces wait-time and complexity, so that businesses can focus on what matters.

Use Case #2: Obtain Consistent Data

ETL enhances data quality. Data transformation and automation with specific schema guidelines reduce manual errors and data duplication.

Use Case #3: Merge Your Data

ETL processes can be used to consolidate and standardize data from disparate sources. Examples of disparate sources include legacy systems, cloud-based solutions, and mobile applications. These sources are loaded into a centralized data warehouse to prepare for merging.

Merging allows organizations to have a single source of truth for their data. It makes data-driven decisions easier because it eliminates the need for manual data integration processes, which are likely to cause a higher degree of errors and inconsistencies.

Top ETL Tools for Business Intelligence

Portable

This platform is a comprehensive integration tool that boasts over 300 data sources. Portable is perfect for teams seeking to extract data with rapid turnaround times. Additionally, its flexible pricing options allow you to tailor the solution to meet your specific needs.

Informatica

Informatica offers a robust data engineering solution. It is ideal for enterprises looking to transform their data. This platform allows organizations to effectively govern, integrate, and deploy their data in the cloud.

Talend

Talend provides a user-friendly, drag-and-drop no code solution for comprehensive cloud data integration. Their platform integrates with popular tools, giving you the flexibility to choose the right solution for your needs.

Dell Boomi

With minimal coding, Dell Boomi simplifies the process of blending data. Its endpoint design accelerates the data-loading process, allowing you to get your work done faster. Also, the application can be leveraged across hybrid infrastructures. In case your organization uses multiple advanced technologies.

Jitterbit

Jitterbit is an iPaaS platform that leverages AI technology to improve data flows. By using AI tools and endpoints, teams can streamline their data management processes, making it an efficient solution.

SnapLogic

SnapLogic is a platform designed for non-technical teams seeking to manage their data. Its intuitive drag-and-drop solutions support the ETL process. Making it a simple and effective solution for your data needs.

Integrate.io

Integrate.io offers support for ETL workflows and data management through APIs. If you're looking to manage internal and cloud databases, Integrate.io is the solution for you.

Oracle

Oracle offers a powerful suite of data integration products. The two premier products include Data Integrator and GoldenGate. These tools enable teams to govern and profile metadata. Especially during extraction, giving you full control over your data ecosystem.

Pentaho

Pentaho uses batch processing to help companies manage their data analytics. This approach simplifies big data authentication. It accomplishes this by providing teams with reliable data management solutions, which means that your data is unlikely to develop inconsistencies.

Hevo

Hevo provides a comprehensive solution for your data management needs. It is an ETL tool that assists SaaS companies in replicating their data. And it can provide you with support for data warehouses, pipelines, and schemas.

IRI Voracity

IRI Voracity offers a data cleansing solution for teams seeking to govern their data better. With its powerful cleaning capabilities, teams can enrich their data and use it in new and innovative ways. Teams can surely look to enhance the data quality when they use this platform.sql

SAP

SAP provides a complete suite of tools to help organizations manage their cloud platform. Its comprehensive platform validates all your workflow requirements. Making it the go-to solution for your data management needs.

ZigiOps

ZigiOps streamlines data workflows by using low code. With this approach, teams can prevent data loss and take control of their data pipeline. Thus providing you with a trustworthy intervention for your data management needs.

Microsoft

Microsoft offers scalable data solutions like Azure, Flow, and SSIS. Each platform serves your data needs, providing the tools you need to grow and succeed. It is an exceptional platform for data transformation and visualization.

IBM

IBM's web services include InfoSphere, DataStage, and App Connect. Each product offers a robust way to help standardize complex data sets. With these powerful tools, teams can effectively manage their data and make informed decisions.

Summary: ETL for Business Intelligence

In conclusion, ETL processes are vital to modern business intelligence systems. ETL enables organizations to collect, process, and optimize large amounts of data and produce insights and business analytics for decision-making purposes.

Consider Portable if you want to eliminate the headache of developing pipelines. It is the ultimate ETL/ELT platform with 300+ built-in data connectors and fast turnaround times for custom connectors.

Get in touch with the Portable team today and discover how they can transform your data integration process.