Data integration should not be overlooked when developing an efficient business intelligence framework. It is one of the key components that extract data from multiple sources and integrate them into a common space. In an era of fierce competition, any small information can be an important factor for any modern enterprise. Therefore, companies must use appropriate processes to aggregate all relevant data from different sources.

Such companies who master the integrating process set themselves apart from the rest of the companies. In the current world, data exists in an inconsiderable amount.

In other words: Data Available is Overwhelming

Also, it is exponentially increasing and changing every day. It is extremely difficult to make sense out of data and derive business insights from it. Hence, it is necessary to assemble, clean, and take control of your data, but how do you do so?

The answer is ETL. 

ETL is the rock of today’s data-driven strategies utilized in enterprises. ETL proves to be fruitful in optimizing and cleaning data for analysis. It is an abbreviation for extract, transform and load. There are five steps of the ETL process.

What is ETL? (Extract, Transform and Load)

ETL (Extract, Transform, Load) refers to extracting data from various academic sources, loading it into your data warehousing system. The data is converted from raw to the format required by the enterprise data warehouse. It is a common technique of moving data to the target systems and displaying it differently from the source data. 

A well-designed ETL system extracts data from the source system, applies data quality and integrity standards, and tunes the data to share individual sources. As a result, it maintains data integrity and helps businesses to make result-oriented decisions. Also, it enables businesses to gain better insights from big data silos. 

ETL systems typically integrate data from multiple applications (systems). These are typically developed and supported by various vendors or hosted on separate computer hardware. Individual systems that use the original data are often managed and operated by different employees. For example, a costing system can combine payroll, sales, and purchase data.

Why is ETL integration important?

In today’s competitive digital data market, it is essential to produce good data quality. Good data caters to business success as it directly relates to accurate insights and better decision-making. That’s why we use ETL (Extract, Transform, Load) to ensure excellent and value-added data becomes part of warehouse systems. 

ETL Database Tool serves several important business functions. 

  • Transferring formatted data into new systems supported by new technology. 
  • Consolidating data from multiple sources such as suppliers, providers, and customers. 
  • Ensuring that data is readable and understandable.

 Large data warehouses like one in Wolf Careers.Inc, provide enterprises with interactive data analytics tools. It accelerates your business results and solves data queries. Also, the improved data readiness allows you to gain valuable insights. 

five steps of etl process

 What is the ETL Process?  

ETL comprises five steps: Extraction, Cleanup, Transform, Load, and Analysis. Data extraction, transformation, and loading are the essential steps. 

  • Extraction

In Extraction, desired data is extracted from unstructured databases and sources. Only estimated data volumes are extracted from each data source. Then it is transferred to a temporary staging data repository. Data extracted has no negative impact on databases. 

Data extraction occurs in three ways:

Update Notification – you extract data when systems notify you that changes are made to the records.

Incremental Extraction – systems extract data on their own when records are modified without providing any notification.  

Full Extraction – system reloads all the data to get it out of the system. This method involves large data transfers and is only used in small businesses. 

  • Cleanup

Cleanup makes sure that quality data is extracted from an unstructured data pool. Also, it ensures that only quality data is transformed. It is one of the crucial steps in which null values, phone numbers, or zip codes are all converted to a standardized form. 

  • Transformation

Transformation refers to preparing data for analysis in two ways:

  1. Cleansing data
  2. Aggregating data

 These two processes either take place in a staging area or analytics warehouse. There are two types of transformation: Basic and Advanced. Basin transformation includes cleaning, eliminating duplicate records, formatting, and key structuring.

The advanced transformation includes the following:

  • Deriving new values by applying business rules to data
  • Filtering data
  • Linking data from multiple sources
  • Summarizing data to obtain figures 
  • Aggregating data elements
  • Data integration
  1. Loading

After transformation, data is loaded into the warehouse. The loaded data is checked for defects. Else, a business tool or an alerting system is inserted into it. There are two ways to load data:

  1. Full Load: entire data is loaded into the warehouse
  2. Incremental Load: data between target and source data is dumped at regular intervals

 Each step is executed one after another. However, the exact nature of each step (the format required for the target database) depends on your company’s specific needs and requirements.

  1. Analysis

Once the data is loaded, it is analyzed in warehouses. This process helps in gaining business insights from the data. There are data analysis tools that help in analyzing data. 

Following applications provide data analysis tools:

  • Alteryx: It is a data analytics platform that provides a visual workflow tool to analyze data. 
  • Amazon Quicksight: It helps developers easily build visualizations, perform ad-hoc analysis and get business insights from data. 
  • Amazon SageMaker: It is a data analytics platform that helps data developers to build, train, and deploy machine learning models quickly. 
  • Apache Spark: It is an open-source analytics engine that runs batch, stream workloads and provides modules for machine learning and graph processing.

Conclusion:

In a nutshell, ETL is a data-driven strategy that aids in running business enterprises successfully. The data influences the business strategies and decisions. Hence, it is essential to take an ETL expert on board. It helps in making result-oriented decisions in less time.