Why do I need a data warehouse in my company (and not only a database or data lake instead)?

“Every two days we create as much information as we did from the dawn of civilization up until 2003”
Eric Schmidt

This overwhelming amount of data as we just read in Eric Schmidt’s quote is what we refer to as Big Data, and it has become an important factor in how companies manage their operations in recent years. But how are companies dealing with this immense amount of data? This is where the data warehouse comes into play, serving as a vital tool in the field of business intelligence.

So, what exactly is a data warehouse, and what does it involve?

The term data warehouse essentially means a place for storing data. This initial understanding can lead to the concept of collecting and managing data from various sources.

A data warehouse is essentially a specialized data storage system often used to aggregate and analyze business data. This process facilitates the strategic use of data, which can then be accessed and analyzed by users in an organized manner. This capability sets a company apart by enabling data-driven decision-making based on more precise information.

Furthermore, the use of data warehouses lays the foundation of business intelligence, a collection of processes necessary to provide computer solutions for analyzing a company’s operations through data and indicators.

It is important to note that when we discuss data warehouses, we are talking about an environment rather than a product. This environment serves the purpose of providing users with information to support decision-making, reducing the time spent searching for data, and enhancing performance when querying for information used in reports and analyses.

How does a data warehouse operate?

When working with data storage, data is typically organized into tables and columns within different databases. Each column can contain descriptions, such as numbers or text, and these tables can be thought of as being organized in a manner similar to folders. When data is added, it is stored in tables defined by a schema, allowing users to choose which tables they want to access.

What are the key features of a data warehouse?

Focus on specific topics: A data warehouse presents information related to a particular topic, whether it’s product details, sales data, or information about suppliers. This structure allows users to quickly access the information they need.

Integration of data: Data warehouses combine data from diverse sources, including simple files or complex databases. This data can be stored at different levels, tailored to the specific requirements of each user.

Historical data: Data included in a data warehouse provides insights into specific points in history, making it possible for users to conduct trend analysis and comparisons.

Nonvolatility: Old data is not discarded when new data is added. Instead, it remains accessible for querying and analysis, ensuring a comprehensive historical record.

Additionally, data warehouses offer many advantages that can significantly impact a company’s operations. The centralization of data from various sources within a data warehouse offers a streamlined approach for users to access pertinent information. This accessibility eliminates the need to navigate through disparate systems, saving time and effort.

A core advantage lies in the provision of precise and comprehensive data for diverse activities. This data serves as the foundation for generating insightful reports and conducting specific queries. By reducing the time invested in these processes, organizations can allocate resources more effectively and make informed decisions promptly.

We can also mention that the integration of a multitude of data sources into a data warehouse serves to alleviate the strain on production system. Unlike traditional setups where data retrieval could burden operational databases, a well-designed data warehouse enables efficient data consolidation, preventing undue stress on primary systems.

Likewise, an essential transformation occurs through the restructuring and matching of data. The combination of disparate data into a coherent structure enhances user experience when generating reports and conducting analyses. This user-centric approach simplifies complex data interpretations and empowers stakeholders to extract valuable insights effortlessly.

Furthermore, the utilization of a data warehouse unlocks access to a wealth of historical data, because this historical repository enables users to analyze data facilitating trend identification and comparisons. Consequently, these analyses pave the way for accurate future predictions grounded in reliable, consistent, and high-quality data.

Differences between data warehouse, database, and data lake

Companies often leverage not only data warehouses but also databases and data lakes to curate information for subsequent analysis. Each has distinct characteristics that set them apart:

Data warehouse vs. database:

• A fundamental contrast emerges in the nature of their applications, a data warehouse specializes in data analysis to decode relationships and trends, while databases primarily house transactional data, such as logs.
• The sourcing of data diverges significantly, data warehouses aggregate data from diverse sources, allowing a holistic view. In contrast, databases typically rely on data from a singular source.
• The mechanism of data loading varies, data warehouses employ bulk batch write loads, whereas databases accommodate new data based on availability.
• Data storage optimization diverges between the two, data warehouses prioritize column-based storage for rapid query access, while databases optimize for row-based write operations.
• Difference in access optimization between them, data warehouses minimize inbound and outbound operations while maximizing throughput, while databases excel in reduced read operations.

Data warehouse vs. data lake:

• A data warehouse primarily houses structured data, whereas a data lake accommodates a broader spectrum of data types, including structured, semi-structured, and unstructured data
• Data processing strategies diverge considerably, because data warehouses emphasize structuring data during ingestion (schema-on-write), whereas data lakes retain raw data until actual usage (schema-on-read).
• User profiles differ since data warehouse users encompass analysts, data scientists, and developers, while data lake users extend to data engineers and architects.
• The foundation of data warehouses is rooted in established technology, bolstering their security. In contrast, data lakes are often built on newer Big Data frameworks and may exhibit different security challenges.
• Data quality also differs significantly, because data Warehouses rely on meticulously curated data, whereas data lakes might include less processed data due to their flexible storage approach.

In summary, a data warehouse offers a strategic advantage by facilitating data accessibility, informed decision-making, and historical trend analysis, while databases and data lakes play distinct roles, catering to specific data needs while differing in data sourcing, storage optimization, and user profiles. Understanding these differences empowers organizations to harness the full potential of their data assets.