Data warehouse serves as a perfect storage system and it allows users to extract actionable insights from stored data to make improved business decisions.
In other words, it acts as a relational database. Apart from that, a data warehouse environment also uses an extraction, transformation, and loading (ETL) solution that takes an enormous load off transactional systems, as well as enhancing data quality and helping to prepare it for analysis.
ETL acts like a lever that unlocks the value of a firm’s data warehouse, whether they are looking for loading data from a sales stack into your warehouse or constructing simple pipelines between basic apps.
Let’s explore the basics of ETL and key data warehousing concepts in detail.
Big Data Evolution
Big data has witnessed a major evolution in the last few years. Studies report that data growth has reached a whopping amount of 44 trillion GB and for businesses, that data is gold.
Companies tap into big data to observe profits jump from 8% to 10 %. Likewise, those who fail to embrace the power of big data will reduce them to rubble.
Hence, it’s no wonder that data warehouses are regarded as a major asset by almost 70% of businesses in 2020.
However, to use big data effectively, companies must have access to three important tools:
- Data warehouses
- ETL tool
- Robust BI tools
Among these three, the job of the data warehouse is to act as a storage place for all data as well as business intelligence solutions that use data to generate quality insights.
ETL, however, is the intermediary that extract, transform, load all data into the data warehouse for analysis. Though the ETL phase is deemed important, it is important to know how it works, and does one need it to successfully load data from one system to the next?
ETL Technology Explained
ETL is a pertinent data integration step that gets completed in three phases: extraction, transformation, and loading.
Simply put, the ETL process takes raw data gathered from multiple sources, transforms it to make it suitable for analysis, and loads that data into the warehouse. Here are the three steps in detail:
1) Extract
Data is extracted from possible sources such as Salesforce, Google Adwords, etc, and placed into the staging area that acts as a buffer between the data warehouse and source data.
The goal of this staging area is to remove all possible errors or discrepancies that may be present in the data.
2) Transform
The data cleaning is the transformation stage. In this phase, data from multiple source systems is normalized and converted into a single format, enhancing data quality and compliance. This phase includes:
- Cleaning
- Filtering
- Joining
- Sorting
- Splitting
- Deduplication
- Summarization
Also Read: Why Augmented Analytics is the Future of the Data Industry
3) Loading
In this final stage, data extracted and transformed is loaded into the data warehouse. Depending upon business needs, data can be loaded in batched or all at once.
Finally, when data is loaded into data warehouses, companies can savor a multitude of benefits that includes:
- Improved decision-making: The transformed data stored in the warehouse can be used to make quality-driven decisions and users no longer have to rely on limited data and hunches. Data warehouses store important facts and statistics, which can be used to make better decisions. Additionally, the data warehouse also assists in streamlining marketing segmentation, inventory management, financial management, and sales.
- Easy and quick access to data: To corner the business landscape, speed plays an important role. Business users can easily access data from the warehouse with ease and precision. Since the speed of access is faster than usual, users don’t need to waste time on retrieving data from a plethora of sources. As a result, companies can make accurate decisions in no time – without any support of IT.
- Enhanced Data quality and consistency: By gathering data from different sources and converting it into a single and widely used format, data experts can improve the data and results that are in line and consistent with each other. When data is standardized, it is more accurate, and accurate data results in strong business decisions.
In short, businesses can use ETL technology to store data in a warehouse for making accurate business decisions in no time.
