In today’s era, when there is neck-to-neck competition, Data Integration is essential for the development of an effective Business Intelligence framework. For any organization, even a small piece of information can be a game-changer. As a result, ETL BI has become increasingly popular in today’s market.
BI (Business Intelligence) is a set of processes and technologies for transforming raw data into useful information and gaining actionable insights from it. ETL BI refers to the process of extracting data from multiple sources, transforming it into the appropriate format for Querying, Reporting, and Analysis, and then loading it into a Data Warehouse or other centralized data repository.
This article will give you a comprehensive guide to ETL BI. You will get to know about the key steps involved in the ETL BI process. You will also explore the key features and challenges associated with ETL BI tools in further sections. Let’s get started.
Table of Contents
- Introduction to Business Intelligence
- Introduction to ETL BI
- Key Steps Involved in the ETL BI Process
- Key Features of ETL BI
- Applications of ETL BI
- Challenges of ETL BI
Introduction to Business Intelligence
Business Intelligence (BI) is an umbrella term that refers to the Gathering, Pruning, Analysis, and Presentation of business data to make better business decisions. It provides a broad overview of business processes, thus allowing businesses to assess their efficiency and productivity. BI includes all of the software, hardware, best practices, and skills that go into this process.
BI tools can be used to predict future trends, marketing dynamics, variations in demand and supply, and many others. Some of the popular BI tools include PowerBI, Tableau, Looker, and many more. Nonetheless, BI’s final outputs are simple enough for non-technical users to understand. Some of the advantages of BI include:
- Increased Operational Efficiency: Businesses can use Business Intelligence tools to figure out what their customers desire and what services they should focus on. Having a clear objective in mind would significantly improve operational efficiency.
- Increased Competitive Advantage: Organizations can be more competitive if they understand the market and how they function within it. Businesses can use BI tools to keep up with industry trends, track seasonal market shifts, and predict customer needs.
- Quick Reporting: The use of a BI Tool would allow for faster data analysis and quick insight generation, which would have taken a long time if done manually.
To know more about BI, visit this link.
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Introduction to ETL BI
ETL BI is one of the most crucial Data Integration techniques. ETL BI is a process that involves extracting data from multiple data sources, transforming it into a common format, and loading the transformed data into a new Data Warehouse to gain useful business insights. ETL BI is usually implemented with ETL BI tools, which allow Developers to write ETL codes and perform other development and management tasks.
With the emergence of the Cloud, many organizations are seeking to migrate their data from legacy source systems to Cloud platforms by utilizing the ETL BI process. Organizations using legacy data sources such as RDBMS (Relational Database Management Systems), DW (Data Warehouse), and others usually lack performance and scalability. Hence, to improve performance, scalability, and fault tolerance, organizations are migrating data to Cloud technologies such as Amazon Web Services, Google Cloud Platform, Microsoft Azure, Private Clouds, and many others.
Today’s Business Intelligence (BI) processes and systems rely heavily on ETL. The ETL BI process has aided organizations of all sizes in extracting valuable insights from their enormous data silos. Thus, the ETL BI process aids in data integrity, allowing an organization to make better and more effective decisions.
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Key Steps Involved in the ETL BI Process
The ETL BI process consists of 4 key steps that allow data to be integrated from source to destination and thereafter analyze the given data. These 4 key steps include:
Extraction is an important aspect of the ETL BI process as it unifies structured and unstructured data from numerous data sources, including databases, SaaS (Software-as-as-Service) applications, files, CRMs (Customer Relationship Management), etc. Extraction tools make this process easier by allowing users to extract valuable data with just a few clicks. While executing the data extraction process, ETL solutions must ensure the following criteria:
- Remove all the redundant data.
- Remove unwanted data.
- Records and source data must reconcile.
- Ensure that the key attributes and data types are correct.
Transformation is the process of converting extracted data into a common format so that a Data Warehouse or a BI tool can easily manipulate it. Sorting, Cleaning, Removing Redundant Information, and Verifying Data from Data Sources are some of the transformation strategies that are widely followed in every organization. The following are some of the key validation factors to consider while transforming data:
- Filter data according to your requirements and load only the columns that you require.
- Appropriate Data Engineering techniques should be used to fill in missing values.
- Before loading confidential data into Data Warehouse, make sure it’s appropriately masked.
- Transposing the tables wherever required.
- Ensure that all measurement units are converted to a single unit, such as converting all currencies to USD, length in meters, weights in kg, and so on.
Loading is the process of loading transformed data into a destination, usually a Data Warehouse. The loading stage is critical as after this stage, the customer data is visualized using different BI tools. There are 3 types of loading functions in a typical ETL BI process:
- Initial Load: When you load tables through your ETL process for the first time, this is known as an Initial Load. The tables are then populated to the entire Data Warehouse.
- Incremental Load: Users can load data in real-time or at regular intervals and update new data in the Data Warehouse. This is known as an Incremental Load.
- Full Refresh: This type of data loading firstly removes the entire table from the Data Warehouse and then replaces it with new data.
4) Business Intelligence
Business Intelligence is a collection of processes and technologies for transforming the loaded data into useful information and gaining actionable insight from it. Some of the popular Business Intelligence tools include PowerBI, Tableau, Looker, etc.
Key Features of ETL BI
Businesses use the ETL BI process to get a consolidated view of data that can help them make better and informed decisions. Some of the key features of ETL BI includes:
1) Big Data Analytics
Enormous volumes of data aren’t very useful in their raw form. To gain valuable insights, these data must be properly structured, analyzed, and interpreted. ETL ensures the quality of data by standardizing and removing duplicates in the Data Warehouse. This significantly helps in analyzing Big Data.
Furthermore, ETL solutions combine data integration and processing, thus making it easier to work with large amounts of data. In the Data Integration phase, ETL BI assembles data from a variety of data sources. After the Data Integration phase, it uses business rules to generate a consolidated view.
2) High-Level Data Mapping
With dispersed and voluminous data, it is difficult to transform the data into actionable insights. Data Mapping facilitates database functionalities such as integration, migration, warehousing, and transformation.
Using ETL BI, data can be easily mapped for specific applications. Data Mapping aids in establishing a correlation between several data models.
3) Faster Data Processing
Scripts are used in today’s ETL BI tools, which are faster than traditional programming. Scripts are a small set of instructions that run in the background to do certain activities. This is one of the reasons for faster data processing.
The volume of incoming data can sometimes reach up to millions of events per second. Stream processing can assist in making timely decisions in such scenarios. Thus, ETL BI ensures faster data processing.
4) Job Scheduling & Automation
Another key feature of the ETL BI tool is job scheduling, which allows users to effortlessly automate their processes. ETL BI teams or Data Analysts working on a project without job scheduling and automation will have to manually map the data and then run the entire workflow regularly. But with automation, they can design a workflow once and set it to execute at a given time or at predefined intervals to update the tables.
ETL Developers can also design transformation task sequences that run in both serial and parallel across several servers. Furthermore, ETL BI job scheduling also supports external program execution, SQL (Structured Query Language) execution, FTP (File Transfer Protocol) uploads/downloads, and email data extraction.
Applications of ETL BI
ETL BI systems have applications in almost every industry. However, early adopters of this technology include banking, insurance, customer service, finance, and healthcare. Some of the applications of ETL BI include:
- The ETL BI process plays a key role in reporting and business intelligence tools. ETL process extracts data from different data sources, transforms it, and loads it into BI tools for quick data analysis.
- The ETL BI process is essential for mapping data between source and target systems. Once the data mapping is done correctly, all of the data from the source system can be loaded into the target system.
- Data migration from legacy systems to modern Data Warehouses can be easily achieved by employing ETL BI tools.
Challenges of ETL BI
Far too often, organizations discover that their ETL BI procedures are riddled with flaws and inefficiencies, resulting in failed tasks and downtime. Some of the challenges of ETL BI include:
- Costly Project Delays and Processing Bottlenecks: With the introduction of Big Data and the demand for timely, accurate data across globally distributed enterprises, businesses are finding that using an ETL BI approach to Data Warehousing often results in costly Project Delays, Processing Bottlenecks, and inability to react seamlessly to market changes.
- Manageability Issue: The streams of incoming data are simply too fast for an ETL BI tool to process. Today’s businesses necessitate a real-time ETL BI solution instead of batch-oriented ETL BI due to the volume and velocity of data they are dealing with. Moreover, most businesses often find it difficult to manage both types of ETL operations.
- Transformation Accuracy: ETL Pipelines that are manually coded can result in multiple errors. To give reliable results in reporting and analysis, the transformed data must be accurate. Thus, manual coding for the ETL BI pipelines can drastically degrade the performance.
This article gave you a brief introduction to ETL BI. It also provided in-depth knowledge about the key steps involved in ETL BI. You also got to know about the key features and challenges associated with ETL BI. ETL BI is beneficial to businesses of all sizes and types. Having the relevant data at the right time will enable you to make informed decisions that will propel your company to a position where you can keep an eye on your competition and stay one step ahead of them.Visit our Website to Explore Hevo
Businesses can use automated platforms like Hevo Data to set the integration and handle the ETL process. It helps you directly transfer data from Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you with a hassle-free experience.
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