Business organizations view data as an essential asset for their business growth. Well-organized data helps them make well-informed decisions, understand their customers, and gain a competitive advantage. However, a huge volume of data is required to achieve these goals, and managing such large-scale data can be extremely difficult. This is where the data warehouses can play an important role.
Data warehouses allow you to collect data scattered across different sources and store it in a unified way. You can then use this data to perform critical tasks such as sales prediction, resource allocation, or supply chain management. Considering these capabilities, let’s learn what a data warehouse is and how you can utilize it for business intelligence functions.
What is a Data Warehouse?
A data warehouse is a system that enables you to store data collected from multiple sources, such as transactional databases, flat files, or data lakes. After this, you can either directly load the data in raw form or clean, transform, and then transfer it to the data warehouse.
So, the data warehouse acts as a centralized repository that allows you to retrieve the stored data for analytics and business intelligence purposes. In this way, the data warehouse facilitates effective storage and querying of data to simplify its use for real-life applications.
Overview of Data Warehouse Architecture
Different data warehouses cater to varied data requirements, but most of them comprise similar basic architectural components. Let’s have a look at some of the common architectural elements of a data warehouse:
Central Database
The central database is the primary component of storage in a data warehouse. Traditionally, data warehouses consisted of on-premise or cloud-based relational databases as central databases. However, with the rise of big data and real-time transactions, in-memory central databases are becoming popular.
Data Integration Tools
Data integration tools enable you to extract data from various source systems. Depending on your requirements, you can prefer the ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) method to transfer this extracted data to a data warehouse.
ETL is the preferred approach, wherein you must first clean and transform data using suitable data manipulation solutions. In ELT, you can directly load the unprocessed data in the warehouse and then perform transformations.
Metadata
Metadata is data that provides detailed information about data records stored in warehouses. It includes:
- Location of data warehouse along with description of its components
- Names and structure of contents within the data warehouse
- Integration and transformation rules
- Data analysis metrics
- Security mechanism used to protect data
Understanding metadata helps you to design and maintain a data warehouse effectively.
Data Access Tools
Access tools enable you to interact with data stored in data warehouses. These include querying tools, mining tools, OLAP tools, and application development tools.
Data Warehouse Architectural Layers
The architectural components of the data warehouse are arranged sequentially to ensure streamlined data warehousing processes. This ordered organization of components is called a layer, and there are different types of layers within a data warehouse architecture. Here is a brief explanation of each of these layers:
Data Source Layer
This is the first layer where you can perform data extraction. It involves collecting data from sources such as databases, flat files, log applications, or APIs.
Data Staging Layer
This layer is like a buffer zone where data is temporarily stored before you transform it using the ETL approach. Here, you can use filtering, aggregation, or normalization techniques to make the raw data analysis-ready. In the ELT approach, the staging area is within the data warehouse.
Data Storage Layer
Here, the cleaned and transformed data is stored in a data warehouse. Depending upon the design of your data warehouse, you can store this data in databases, data marts, or operational data stores (ODS). Data marts are a smaller subset of data warehouses that enable the storage of essential business data for faster retrieval.
ODS, on the other hand, is a data storage system that helps you perform significant business operations in real-time. For example, you can use ODS to store customer data for your e-commerce portal and utilize it for instant bill preparation.
Data Presentation Layer
In the presentation layer, you can execute queries after retrieving data to gain analytical insights. For better results, you can also leverage business intelligence tools like Power BI or Tableau to visualize your data.
Types of Data Warehouses
Traditionally, data warehouses were deployed on-premise, but now you can opt for cloud-based solutions for better data warehousing experience. Other than this, the data warehouses can be classified into the following types:
Enterprise Data Warehouse
Large business organizations use enterprise data warehouses as a single source of truth for all their data-related tasks. They are useful for enterprise data management as well as for conducting large-scale analytical and reporting operations.
Departmental Data Warehouse
Departmental data warehouses are used by specific departments, such as sales, marketing, finance, or small business units. They enable efficient management of medium to small datasets.
Data Mart
Data Marts are a subset of a large data warehouse usually used for faster data retrieval in high-performance applications. They require minimal resources and less time for data integration. For effective usage, you can opt for data marts to manage departmental data such as finance or sales.
Online Analytical Processing (OLAP) Data Warehouse
OLAP data warehouses facilitate complex querying and analysis on large datasets using OLAP cubes. These are array-based multidimensional databases that allow you to analyze higher dimensional data easily.
Benefits of Data Warehouse
Data warehouses help streamline the data integration and analytics processes, enabling better data management and usage in any organization. Let’s briefly discuss some benefits of using a data warehouse:
High Scalability
Modern cloud-based data warehouses offer high scalability by providing flexibility to adjust their storage and compute resources. As a result, you can accommodate large volumes of data in data warehouses.
Time-saving
A data warehouse is a centralized repository that you can use to manage your data effectively. It supports data consolidation, simplifying the processes of accessing and querying data. This saves a lot of time, as you do not have to reach out to different sources each time while performing analytical operations. You can utilize this time to perform more important business tasks.
Facilitates High-Quality Data
It is easier to transform and clean the data stored in a unified manner within the data warehouse. You can perform aggregation operations, handle missing values, and remove duplicates and outlier data points in bulk on these datasets. This allows you access to standardized and high-quality data to develop businesses.
Improves Decision-making
You can analyze the centralized and transformed data in a data warehouse using analytical tools like Qlik, Datawrapper, Tableau, or Google Analytics. The data analysis outcomes provide useful information about workflow efficiency, product performance, sales, and churn rates. Using these insights, you can understand the low-performing areas and make effective decisions to refine them.
Challenges of Using Data Warehouse
While data warehouses provide numerous advantages, there are some challenges associated with their usage. Some of these challenges are:
Maintenance Complexities
Managing large volumes of data stored in traditional data warehouses or marts can be difficult. Tasks like regularly updating the data, ensuring data quality, and tuning the data warehouse for optimal query performance are complex.
Data Security Concerns
You may face difficulties while ensuring data security in data warehouses. For this, it is essential to frame robust data governance frameworks and security protocols. Measures such as role-based access control and encryption are effective but limit data availability.
Usually, large businesses use data warehouses, where there is a high probability of data breaches. This leads to financial losses, reputational damages, and penalties for violating regulations.
Lack of Technical Experts
Using a data warehouse requires sufficient knowledge of data integration, querying, and analysis processes. A lack of such skills can lead to issues such as poor data quality and the creation of non-useful outcomes during data analysis. You and your team should also have hands-on experience in diagnosing and resolving problems if there is a system failure.
High Deployment Cost
The cost of implementing data warehouses is very high due to the sophisticated infrastructure and technical workforce requirements. As a result, small businesses with limited budgets cannot utilize data warehouses. Even for large companies, ROI is the biggest concern, as there can be doubts about recovering the money they invested in implementation.
Best Practices for Optimal Use of Data Warehouses
As you have seen in the previous section, there are some constraints to using data warehouses. To overcome them, you can adopt the following best practices:
Understand Your Data Objectives
First, clearly understand why you want to use a data warehouse in your organization. Then, interact with senior management, colleagues, and other stakeholders to inform them about how data warehouses can streamline organizational workflow.
Use Cloud-based Data Warehousing Solutions
Numerous cloud-based data warehouses help you to manage business data efficiently. They offer flexibility and scalability to store and analyze large amounts of data without compromising performance. Many data warehouses support pay-as-you-go pricing models, making them cost-effective solutions. You also do not have to worry about infrastructure management when using cloud data warehouses.
Prefer ELT Over ETL
ETL and ELT are two popular data integration methods used in data warehousing. Both help you collect and consolidate data from various sources into a unified location. However, ELT can be helpful for near-real-time operations as you can directly load data into the data warehouse, and transformation can be performed selectively later.
Define Access Control in Advance
Clearly define the access rules based on the job roles of all your employees to ensure data security. If possible, classify data as confidential and public to protect sensitive data like personally identifiable information (PII). You should also regularly monitor user activity to detect any unusual patterns.
Conclusion
A data warehouse can play an important role in your business organization if you are looking for efficient ways to tap the full potential of your data. It allows you to store data centrally and query and analyze it to obtain valuable information related to your business. You can use this knowledge to streamline workflow and make your business profitable.
This article explains the data warehouse’s meaning and architecture in detail. It also explains the benefits, challenges, and best practices for overcoming them so that you can take full advantage of data warehouses.
FAQs
What are some highly used data warehouses?
Some popular data warehouses are Amazon Redshift, Snowflake, Google BigQuery, Azure Synapse Analytics, IBM Db2, and Firebolt.
What is the difference between a data warehouse and a database?
Data warehouses allow you to store and query large volumes of data for business analytics and reporting purposes. Databases, on the other hand, are helpful in querying transactional data of smaller volumes. They efficiently perform routine operations such as inserting, deleting, or updating data records.
