Data Warehousing

What is Data Warehousing?

• Data warehousing is a technology that enables businesses to store, manage, and analyze large volumes of data from various sources in a centralized repository. The primary goal of data warehousing is to provide a comprehensive and integrated view of an organization's data to support informed decision-making.
• A data warehouse is a collection of data that is specifically designed to support business intelligence (BI) activities, such as reporting, analysis, and data mining. The data is typically extracted from operational databases, transformed, and loaded into the data warehouse. This process is commonly known as Extract, Transform, and Load (ETL).
• Data warehousing is essential for businesses that generate vast amounts of data from various sources, such as sales transactions, customer interactions, and supply chain operations. By consolidating data into a single repository, businesses can gain insights into their operations, identify trends, and make data-driven decisions. Moreover, a data warehouse provides historical data that can be used to analyze trends and patterns over time. The ability to perform time-series analysis is critical for businesses to identify long-term trends, plan for future needs, and forecast demand.

History of Data Warehouse

Data warehousing has a long and evolving history that dates back to the 1960s. Initially, data warehouses were used in large corporations and government agencies to store historical data for reporting and decision-making purposes. One of the earliest examples of a data warehouse was the Integrated Data Store (IDS) developed by General Electric in the early 1960s. IDS was designed to consolidate data from multiple sources and provide a single view of the company's operations.

In the 1980s, the concept of decision support systems (DSS) gained popularity, and data warehousing became a critical component of DSS. During this period, several commercial data warehousings products, such as Teradata and Redbrick, emerged, and businesses began to use data warehousing for analytical purposes. The 1990s saw significant advancements in data warehousing technology, including the introduction of massively parallel processing (MPP) and online analytical processing (OLAP) tools. MPP allowed data warehouses to scale to accommodate large volumes of data, while OLAP tools provided a user-friendly` interface for querying and analyzing data.

In recent years, data warehousing has evolved to support modern analytics applications, such as machine learning and artificial intelligence. Modern data warehouses, such as Amazon Redshift and Google BigQuery, are cloud-based, and scalable, and offer advanced analytics capabilities that enable businesses to derive insights from their data more quickly and efficiently.

How Data Warehouse Works?

• A data warehouse serves as a central repository for information that arrives from one or more data sources, including structured, semi-structured, and unstructured data. The data is processed, transformed, and ingested into the data warehouse, where users can access the processed data through various tools, such as Business Intelligence software, SQL clients, and spreadsheets`.
• By consolidating data from various sources into one comprehensive data warehouse, organizations can analyze their customers more holistically, allowing for better decision-making and strategic planning.
• Data warehousing makes data mining possible by enabling organizations to identify patterns and relationships within the data using advanced statistical and machine learning algorithms. Overall, a data warehouse provides organizations with a centralized platform to store and manage their data, making it easier to access and analyze large volumes of information for reporting, analysis, and decision-making purposes.

Data Warehouse Architecture

Data Warehouse Architecture refers to the design and structure of a data warehouse. The architecture can be divided into three major components - the data source layer, the data warehouse layer, and the data access layer.

• Data Source Layer - This layer consists of the systems that provide data to the data warehouse. These systems can include operational databases, external data sources, and other systems that generate or capture data. In this layer, data is extracted from various sources and transformed into a format that can be loaded into the data warehouse.
• Data Warehouse Layer - This layer is the central repository of data that has been collected from the data source layer. This is where the data is stored in a structured format, making it easier to analyze and query. The data warehouse layer is divided into two components - the staging area and the data warehouse database. The staging area is used to store data before it is transformed and loaded into the data warehouse database.
• Data Access/Analysis Layer - This layer consists of the tools and interfaces that allow users to access and analyze the data stored in the data warehouse. This layer includes tools such as Business Intelligence software, SQL clients, and spreadsheets. The data access layer provides users with various options to access the data warehouse, depending on their specific needs.

Using Data Warehouse Information

Once data is collected and stored in a data warehouse`, it can be used for various purposes. Some of the most common uses of data warehouse information include -

• Business Intelligence - Data warehouses provide a centralized platform for collecting and analyzing data, enabling organizations to gain insights into their business operations. Business Intelligence (BI) tools can be used to query the data warehouse and generate reports, dashboards, and visualizations that help businesses make informed decisions.
• Decision-Making - The insights gained from data warehouse analysis can help organizations make better decisions. By identifying patterns and trends within the data, decision-makers can develop strategies to improve business outcomes, such as increasing revenue, reducing costs, or improving customer satisfaction.
• Marketing - Data warehouses can be used to collect and analyze customer data, allowing organizations to understand their target audience better. Businesses can develop more effective marketing strategies by analyzing customer behavior, such as personalized marketing campaigns or targeted advertising.
• Performance Management - Data warehouse information can be used to track and monitor key performance indicators (KPIs) and measure the success of business initiatives. By tracking KPIs over time, organizations can identify areas where improvements are needed and develop plans to address those areas.

Integrating Heterogeneous Databases

• Integrating heterogeneous databases is a complex task in traditional databases. It requires building "wrappers" and "integrators" over the databases to translate the query from local to a format understood by all the data sources. This process is called the query-driven approach, where the metadata filters and integrates information from multiple data sources.
• However, data warehousing uses the update-driven approach, where the data from multiple heterogeneous data sources is integrated in advance and stored for direct analysis. This approach provides high performance as the data is pre-processed, integrated, restructured, and summarized into one data space.
• One simple example of integrating heterogeneous data sources is merging customer information from multiple platforms such as a CRM system, email marketing software, and social media analytics tool. By combining data from these disparate sources, businesses can gain a comprehensive view of their customers, including their interactions, preferences, and demographics`, enabling more targeted and personalized marketing strategies.
• So, in the above example, the query-driven approach would be used when there is a need to retrieve specific customer data from individual sources in real-time, while the update-driven approach would be applied when the integrated and consolidated customer data in a warehouse is readily available in a centralized location for querying and analysis purposes.

Types of Data Warehouse (DWH)

There are three types of Data Warehouses (DWH) -

• Enterprise Data Warehouse (EDW) - The enterprise data warehouse (EDW) is a comprehensive data repository that stores all of an organization's historical and current data from multiple sources, allowing for centralized data management. The EDW consolidates data from across the organization, creating a single source of truth that supports business intelligence, analytics, and decision-making. EDW can store massive amounts of data and can handle complex data modeling and data integration tasks.
• Operational Data Store (ODS) - An operational data store (ODS) is a database that supports operational and transactional systems by providing real-time data integration. The ODS provides a platform for integrating, cleaning, and consolidating operational data from various sources in real time. It is designed for operational reporting and short-term analysis of business processes, such as order fulfillment, inventory control, and customer service.
• Data Mart - A data mart is a smaller, more focused version of a data warehouse that supports a specific business unit, department, or function within an organization. It is used to serve the needs of a particular group of users and contains a subset of data from the EDW or other data sources. Data marts can be designed for different business areas, such as sales, marketing, finance, or HR, and can be either independent or integrated with an EDW.

Functions of Data Warehouse Tools and Utilities

Data warehouse tools and utilities are designed to perform various functions that help manage and analyze data stored in a data warehouse. Some of the key functions of data warehouse tools and utilities are -

• Data Extraction - This involves extracting data from various sources, such as transactional databases, operational systems, and external data sources. The data is then cleaned, transformed, and loaded into the data warehouse.
• Data Cleaning - This involves identifying and correcting errors or inconsistencies in the data. Data cleaning ensures that the data is accurate and reliable for analysis.
• Data Transformation - This involves converting the data into a format that is suitable for analysis. Data transformation may involve merging data from multiple sources, reformatting data, or creating new variables.
• Data Integration - This involves integrating data from multiple sources into a single data warehouse. This allows for a more comprehensive view of an organization's data, which can improve decision-making.
• Data Storage - Data warehouse tools and utilities provide various storage options, such as relational databases, columnar databases, or cloud-based storage. The choice of storage depends on the size and complexity of the data and the organization's needs.
• Data Analysis - This involves using various tools and techniques to analyze data stored in the data warehouse. Data analysis can help identify patterns, trends, and insights that can inform business decisions.
• Data Refresh - The data refresh refers to updating the warehouse data with the latest information from the source systems. This process is important because the data in the warehouse needs to be as up-to-date as possible to support accurate and timely decision-making. Data refresh can be performed periodically or in real-time, depending on the organization's needs and data availability.

Why We Need Data Warehouse

A few of the most common reasons to explain why we need a data warehouse include the following -

• Centralized data management - Data warehouses provide a centralized location for storing and managing data from multiple sources. By consolidating data into a single location, organizations can better manage their data and reduce the complexity of querying multiple data sources.
• Improved data quality - Data warehouses allow organizations to clean, transform, and integrate data from different sources. This process helps to improve data quality by eliminating errors and inconsistencies and standardizing data formats.
• Support for decision-making - Data warehouses provide a foundation for decision-making by providing a historical view of the organization's data. Organizations can make more informed decisions about their business operations by analyzing trends and patterns in the data.
• Faster access to data - Data warehouses are optimized for querying and analysis, allowing faster access to data than traditional databases. This speed is critical for decision-making and can help organizations stay ahead of their competition.
• Cost savings - By consolidating data into a single location, organizations can reduce the need for expensive hardware and software licenses. Additionally, organizations can reduce costs associated with errors and inefficiencies by improving data quality and supporting better decision-making.

Advantages & Disadvantages of Data Warehouse

A few of the most common advantages of a Data Warehouse include the following -

• It provides a central repository for critical data, making it easy for business users to access information from various sources.
• By providing a consolidated view of data from different sources, data warehouses enable organizations to make informed decisions based on accurate and consistent data.
• Integrates multiple data sources to reduce stress on the production system and reduces the total turnaround time for analysis and reporting.
• Data warehouses provide historical data that can be used to identify trends and patterns over time, leading to better decision-making and planning.
• Restructures and integrates data to make it easier for users to use for reporting and analysis.
• Saves time by allowing users to access critical data from multiple sources in a single place.
• Data warehouses can easily scale to meet the needs of growing organizations, allowing them to store and analyze large volumes of data.

Data warehouses also have several disadvantages, as shown below -

• Implementing and maintaining a data warehouse can be expensive, including hardware, software, and personnel costs.
• Not suitable for unstructured data.
• Not suitable for `real-time or near-real-time data processing.
• Integrating data from multiple sources into a single data warehouse can be complex and time-consuming.
• Data in the warehouse may become outdated quickly.
• Changes in data types and ranges, data source schema, indexes, and queries can be challenging to implement.