In this digital economy, data is paramount. Today, all sectors, from private enterprises to public entities, use big data to make critical business decisions.
However, the data ecosystem faces numerous challenges regarding large data volume, variety, and velocity. Businesses must employ certain techniques to organize, manage, and analyze this data.
Enter data warehousing!
Data warehousing is a critical component in the data ecosystem of a modern enterprise. It can streamline an organization’s data flow and enhance its decision-making capabilities. This is also evident in the global data warehousing market growth, which is expected to reach $51.18 billion by 2028, compared to $21.18 billion in 2019.
This article will explore data warehousing, its architecture types, key components, benefits, and challenges.
What is Data Warehousing?
Data warehousing is a data management system to support Business Intelligence (BI) operations. It is a process of collecting, cleaning, and transforming data from diverse sources and storing it in a centralized repository. It can handle vast amounts of data and facilitate complex queries.
In BI systems, data warehousing first converts disparate raw data into clean, organized, and integrated data, which is then used to extract actionable insights to facilitate analysis, reporting, and data-informed decision-making.
Moreover, modern data warehousing pipelines are suitable for growth forecasting and predictive analysis using artificial intelligence (AI) and machine learning (ML) techniques. Cloud data warehousing further amplifies these capabilities offering greater scalability and accessibility, making the entire data management process even more flexible.
Before we discuss different data warehouse architectures, let’s look at the major components that constitute a data warehouse.
Key Components of Data Warehousing
Data warehousing comprises several components working together to manage data efficiently. The following elements serve as a backbone for a functional data warehouse.
- Data Sources: Data sources provide information and context to a data warehouse. They can contain structured, unstructured, or semi-structured data. These can include structured databases, log files, CSV files, transaction tables, third-party business tools, sensor data, etc.
- ETL (Extract, Transform, Load) Pipeline: It is a data integration mechanism responsible for extracting data from data sources, transforming it into a suitable format, and loading it into the data destination like a data warehouse. The pipeline ensures correct, complete, and consistent data.
- Metadata: Metadata is data about the data. It provides structural information and a comprehensive view of the warehouse data. Metadata is essential for governance and effective data management.
- Data Access: It refers to the methods data teams use to access the data in the data warehouse, e.g., SQL queries, reporting tools, analytics tools, etc.
- Data Destination: These are physical storage spaces for data, such as a data warehouse, data lake, or data mart.
Typically, these components are standard across data warehouse types. Let’s briefly discuss how the architecture of a traditional data warehouse differs from a cloud-based data warehouse.
Architecture: Traditional Data Warehouse vs Active-Cloud Data Warehouse
Traditional data warehouses focus on storing, processing, and presenting data in structured tiers. They are typically deployed in an on-premise setting where the relevant organization manages the hardware infrastructure like servers, drives, and memory.
On the other hand, active-cloud warehouses emphasize continuous data updates and real-time processing by leveraging cloud platforms like Snowflake, AWS, and Azure. Their architectures also differ based on their applications.
Some key differences are discussed below.
Traditional Data Warehouse Architecture
- Bottom Tier (Database Server): This tier is responsible for storing (a process known as data ingestion) and retrieving data. The data ecosystem is connected to company-defined data sources that can ingest historical data after a specified period.
- Middle Tier (Application Server): This tier processes user queries and transforms data (a process known as data integration) using Online Analytical Processing (OLAP) tools. Data is typically stored in a data warehouse.
- Top Tier (Interface Layer): The top tier serves as the front-end layer for user interaction. It supports actions like querying, reporting, and visualization. Typical tasks include market research, customer analysis, financial reporting, etc.
Active-Cloud Data Warehouse Architecture
- Bottom Tier (Database Server): Besides storing data, this tier provides continuous data updates for real-time data processing, meaning that data latency is very low from source to destination. The data ecosystem uses pre-built connectors or integrations to fetch real-time data from numerous sources.
- Middle Tier (Application Server): Immediate data transformation occurs in this tier. It is done using OLAP tools. Data is typically stored in an online data mart or data lakehouse.
- Top Tier (Interface Layer): This tier enables user interactions, predictive analytics, and real-time reporting. Typical tasks include fraud detection, risk management, supply chain optimization, etc.
Best Practices in Data Warehousing
While designing data warehouses, the data teams must follow these best practices to increase the success of their data pipelines.
- Self-Service Analytics: Properly label and structure data elements to keep track of traceability – the ability to track the entire data warehouse lifecycle. It enables self-service analytics that empowers business analysts to generate reports with nominal support from the data team.
- Data Governance: Set robust internal policies to govern the use of organizational data across different teams and departments.
- Data Security: Monitor the data warehouse security regularly. Apply industry-grade encryption to protect your data pipelines and comply with privacy standards like GDPR, CCPA, and HIPAA.
- Scalability and Performance: Streamline processes to improve operational efficiency while saving time and cost. Optimize the warehouse infrastructure and make it robust enough to manage any load.
- Agile Development: Follow an agile development methodology to incorporate changes to the data warehouse ecosystem. Start small and expand your warehouse in iterations.
Benefits of Data Warehousing
Some key data warehouse benefits for organizations include:
- Improved Data Quality: A data warehouse provides better quality by gathering data from various sources into a centralized storage after cleansing and standardizing.
- Cost Reduction: A data warehouse reduces operational costs by integrating data sources into a single repository, thus saving data storage space and separate infrastructure costs.
- Improved Decision Making: A data warehouse supports BI functions like data mining, visualization, and reporting. It also supports advanced functions like AI-based predictive analytics for data-driven decisions about marketing campaigns, supply chains, etc.
Challenges of Data Warehousing
Some of the most notable challenges that occur while constructing a data warehouse are as follows:
- Data Security: A data warehouse contains sensitive information, making it vulnerable to cyber-attacks.
- Large Data Volumes: Managing and processing big data is complex. Achieving low latency throughout the data pipeline is a significant challenge.
- Alignment with Business Requirements: Every organization has different data needs. Hence, there is no one-size-fits-all data warehouse solution. Organizations must align their warehouse design with their business needs to reduce the chances of failure.
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