In today’s business environment, an organization needs to have reliable reporting and analysis of large amounts of data. Businesses need their data to be consolidated and integrated for different levels of aggregation, from customer service to partner integration to top-level executive business decisions. This is where data warehousing comes in as it makes reporting and analysis easier.
To understand the importance of data storage, let’s visit the important data warehousing concepts.
What is Data Warehousing?
The data warehouse (DWH) is a repository where an organization electronically stores data by extracting it from operational systems, and making it available for ad-hoc queries and scheduled reporting. In contrast, the process of building a data warehouse entails designing a data model that can quickly generate insights.
Data stored in the DWH is different from data found in the operational environment. It is organized so that relevant data is clustered together to facilitate day-to-day operations, analysis, and reporting. This helps determine the trends over time and allows users to create plans based on that information. Hence, reinforcing the importance of data warehouse use in businesses.
Data Warehouse Architecture
Data Warehouse Architecture
A data warehouse architecture uses dimensional models to identify the best technique for extracting meaningful information from raw data and translating it into an easy-to-understand structure. However, you should keep in mind three main types of architecture when designing a business-level real-time data warehouse.
- Single-tier Architecture
- Two-tier Architecture
- Three-tier Architecture
Enlisting the Key Features of a Data Warehouse
The key features of a data warehouse include:
- Subject Oriented: It provides information catered to a specific subject instead of the whole organization’s ongoing operations. Examples of subjects include product information, sales data, customer and supplier details, etc.
- Integrated: It is developed by combining data from multiple sources, such as flat files and relational databases, which offers better data analysis.
- Time-Variant: The data in a DWH gives information from a specific historical point of time; therefore, the data is categorized with a particular time frame.
- Non-volatile: Non-volatile refers to historical data that is not omitted when newer data is added. A DWH is separate from an operational database, which means that any regular changes in the operational database are not seen in the data warehouse.
The Role of Data Pipelines in the EDW
A lot of effort goes into unlocking the true power of your data warehouse. Using a metadata-driven ETL approach, you can build low-latency data pipelines that are reliable and flexible.
A data warehouse is populated using data pipelines. They transport raw data from disparate sources to a centralized data warehouse for reporting and analytics. Along the way, the data is transformed and optimized.
However, the increase in volume, velocity, and variety has rendered the traditional approach to building data pipelines —involving manual coding and reconfiguration — ineffective and obsolete.
Automation is an integral part of building efficient data pipelines that can match the agility and speed of your business processes.
Data Pipeline Automation
You can seamlessly transport data from source to visualization through data pipeline automation. It is a modern approach to populating data warehouses that requires designing functional and efficient dataflows.
As we all know, timeliness is one of the crucial elements of high-quality business intelligence — and automated data pipelines help you make data available in the data warehouse as quickly as possible.
Leveraging the power of automated and scalable data pipelines, you can eliminate obsolete, trivial, or duplicated data, maximizing data accessibility and consistency to ensure high-quality analytics.
With a metadata-driven ETL process, you can seamlessly integrate new sources into your architecture and support iterative cycles to fast-track your BI reporting and analysis.
Also, you can follow the ELT approach, where the data is loaded directly to the warehouse, so you can leverage the compute capacity of the destination system to carry out transformations efficiently.
Optimizing Data Pipelines
An enterprise must focus on building automated data pipelines that can dynamically adapt to changing circumstances, for instance, adding and removing data sources or changing transformations.
Of course, moving entire databases when you need data for reporting or analysis can be highly inefficient.
The best practice is to load data incrementally using change data capture to populate your data warehouse. It helps eliminate redundancy and ensures maximum data accuracy.
Other essential capabilities needed to create automated data pipelines are incremental loading, job monitoring, and job scheduling.
- Incremental loading ensures you don’t have to copy all the data to your data warehouse every time there’s a change at the source table to ensure your data warehouse is always accurate and up-to-date.
- Job monitoring helps you understand any issues with your current system and allow you to make any necessary changes to optimize the process.
- Job scheduling allows you to process your data daily, weekly, monthly, or only when specific triggers or conditions are met to streamline the process.
Orchestrating and automating your data pipelines can eliminate manual work, introduce reproducibility, and maximize efficiency.
Examples of Data Warehousing in Various Industries
Big data has become a vital part of performing data warehousing and business intelligence across several industries. Let’s go over some of the examples of data warehousing in various sectors that consider it an essential part of their day-to-day operations.
- Investment and Insurance sector
A data warehouse is primarily used to analyze customer and market trends and other data patterns in the investment and insurance sector. Forex and stock markets are two major sub-sectors where data warehouses play a crucial role because a single point difference can lead to massive losses across the board. DWHs are usually shared in these sectors and focus on real-time data streaming.
- Retail chains
DWHs are primarily used for distribution and marketing in the retail sector to track items, examine pricing policies, keep track of promotional deals, and analyze customer buying trends. Retail chains usually incorporate EDW systems for business intelligence and forecasting needs.
- Healthcare
A DWH is used to forecast outcomes, generate treatment reports, and share data with insurance providers, research labs, and other medical units in the healthcare sector. EDWs are the backbone of healthcare systems because the latest, up-to-date treatment information is crucial for saving lives.
Types of Data Warehouses
There are three main types of data warehouses. Each has its specific role in data management operations.
1- Enterprise Data Warehouse
Enterprise data warehouse (EDW) serves as a central or main database to facilitate decision-making throughout the enterprise. Key benefits of having an EDW include access to cross-organizational information, the ability to run complex queries, and the enablement of enriched, far-sighted insights for data-driven decisions and early risk assessment.
2- ODS (Operational Data Store)
In ODS, the DWH refreshes in real-time. Therefore, organizations often use it for routine enterprise activities, such as storing records of the employees. Business processes also use ODS as a source for providing data to the EDW.
3- Data Mart
It is a subset of a DWH that supports a particular department, region, or business unit. Consider this: You have multiple departments, including sales, marketing, product development, etc. Each department will have a central repository where it stores data. This repository is called a data mart.
The EDW stores the data from the data mart in the ODS daily/weekly (or as configured). The ODS acts as a staging area for data integration. It then sends the data to the EDW to store it and use for BI purposes.
warehouse in 4 easy steps
Why do Businesses Need Data Warehousing and Business Intelligence?
A lot of business users wonder why data warehousing is essential. The simplest way to explain this is through the various benefits to the end-users. These include:
- Improved end-user access to a wide variety of enterprise data
- Increased data consistency
- Additional documentation of the data
- Potentially lower computing costs and increased productivity
- Providing a place to combine related data from separate sources
- Creation of a computing infrastructure that can support changes in computer systems and business structures
- Empowering end-users to perform ad-hoc queries or reports without impacting the performance of the operational systems
Data Warehousing Tools and Techniques
The data infrastructure of most organizations is a collection of heterogeneous systems. For example, an organization might have one system that handles customer relationships, human resources, sales, production, finance, partners, etc. These systems are often poorly or not integrated at all. This makes it difficult to answer simple questions even though the information is available “somewhere” within the disparate data systems.
End users’ access to DWH tools can solve these issues by creating a single database of homogeneous data that is easily retrieved and manipulated. The software tools used for extracting and transforming the data into a homogeneous format for loading into the DWH are also vital components of a data warehousing system.
ADWB – An Enterprise Data Warehousing Automation Tool
Astera DW Builder expedites development of a data warehouse from scratch, helps integrate data from multiple data marts and databases, automates modeling the schema structure, and delivers a high-performance DWH through a unified and unified, and intuitive platform.
ADWB also enables automation so you can easily analyze data and extract insights. It is a metadata-driven data warehousing automation tool with a rich data modeler and includes all the key features of a data warehouse mentioned above. The reverse-engineer functionality allows creating databases in a few clicks without writing any codes. Similarly, users can quickly create schemas from scratch with the easy-to-use drag-and-drop option. The images below briefly depict how the ADWB works.
Reverse-engineering feature in Astera DWB
Dataflow to populate dimension table in ADWB
Once the schema is built and data is populated, the data model can be forward engineered just as quickly to the business’ database.
Learn more about how to build your data warehouse from scratch with Astera Data Warehouse Builder, a high-performance solution that facilitates all your business needs.
If you want to discuss your use case or see a live demo of the product, let us know, and our experts will reach out to you.