Datasources and Data Warehouse

In the previous chapter, we looked at the logical architecture of a data analysis platform, where multiple data sources are ingested, transformed and made available for consumption in different areas like visualization, reporting and analysis. Central to this architecture is a data warehouse (DWH), that consolidates data from multiple sources and prepares it for analysis and consumption.

In this chapter, we will explore the different types of datasources and how these are mapped to Facts and Dimension tables. We will understand Data warehouse design techniques and their different implementation types (Star and Snowflake schemas). We will also introduce the concept of the golden record, a critical mechanism for ensuring data consistency and accuracy within the enterprise ecosystem. Finally, we will learn how to create a data model in Infoveave using the Star schema.

History of Data Warehousing

The concept of data warehousing began in the early 1980s as businesses faced increasing challenges in managing and analyzing data from disparate operational systems. Traditional databases lacked the ability to handle large-scale analytical queries efficiently. To overcome this gap, organizations began to develop systems specifically for consolidating and storing historical data to support better decision-making.

Bill Inmon, the father of data warehousing, introduced the concept of a central repository for integrated, subject-oriented, non-volatile, and time-variant data. This structure allowed organizations to analyze data trends over time. Around the same time, Ralph Kimball proposed the dimensional modeling approach, which emphasized user-friendly structures for querying data warehouses. Today, data warehouses are a critical component of modern business intelligence systems, underpinning data-driven strategies across industries.

Benefits of DWH in an Enterprise

A data warehouse serves as the backbone of enterprise data management. A well-designed data warehouse can bridge the gap between disparate systems, ensuring that businesses have access to reliable, up-to-date information.

One of the primary advantages of a DWH is its ability to aggregate data from multiple systems into a single source of truth. DWH helps maintain historical data records, providing businesses with insights into past performance. They help businesses with robust infrastructure for analytics by maintaining separate transactional data from analytical workloads. DWH supports business with actionable insights through cleaned, transformed, and structured data guiding future decisions.

Challenges of Data Warehousing

While data warehouses offer significant advantages, they also come with their own set of challenges. One of the biggest challenges is the high cost of implementation. Additionally, the complexity of integrating data from multiple sources poses a significant challenge, as each source may have different structures, formats, and quality levels. Data quality management is another critical issue. Designing, deploying, and maintaining a data warehouse requires substantial investment in both hardware and skilled personnel.

Data quality management is another critical issue. A data warehouse is only as good as the data it contains. Inconsistencies, inaccuracies, or missing data can lead to flawed insights and poor decision-making.

Next, let us look at the differences between OLAP (used for analysis) and OLTP (used for daily operations) systems, that serve very different purposes.