What Is a Data Warehouse? Definition, Architecture, Tools, and Applications

A data warehouse is a solution that helps aggregate enterprise data from multiple sources. It organizes them in a relational database to support querying, analysis, and eventually data-driven business decisions. This article explains the architecture of a data warehouse, the top tools, and critical applications in 2022.

What Is a Data Warehouse?

A data warehouse is defined as a solution that helps aggregate enterprise data from multiple sources and organizes them in a relational database to support querying, analysis, and eventually data-driven business decisions. 

Paul Murphy and Barry Devlin, two IBM employees, created the Business Data Warehouse in the late 1980s, which marked the beginning of data warehousing. However, Inmon Bill provided the primary notion. He was regarded as the inventor of the data warehouse. He had written on various subjects related to the warehouse’s construction, operation, upkeep, and the Corporate Information Factory.

A specific data management approach, known as a data warehouse, is purpose-built to facilitate business intelligence through aggregated data analysis. In addition to this, the warehouse may contain historical data that is only meant for querying purposes. It typically uses a variety of sources, including transaction programs and application log files.

The combination of several technologies and elements facilitates the strategic use of data. Large amounts of data are electronically stored by a company and are intended for analysis and inquiry rather than transaction processing. This involves converting data into information while ensuring that it is accessible to users easily, in a way that can be impactful and valuable.

In a data warehouse, massive volumes of data from multiple sources are centralized and consolidated. Organizations can gain valuable business insights from their data by using their analytical skills to enhance decision-making. It creates a historical record over time that data scientists and business analysts can use to their advantage. 

The organization’s operational database is kept separate from the decision support database. The warehouse, in effect, is more of an environment than a product. It is an architectural design element of the information system providing users access to recent and historical decision-support data that may not be readily accessible in the conventional operational data store.

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Data Warehouse Architecture

Data warehouse architecture is complex as a system of information containing historical and commutative data from various sources. Data in several databases are organized according to a data warehouse architecture. A contemporary data warehouse layout determines the most efficient method of obtaining information from raw data because the data must be sorted and cleaned to be valuable. Three modes – single-tier, two-tier, and three-tier – are available for building data warehouse layers.

1. Single-tier architecture

A single layer’s goal is to store as little data as possible. The elimination of data redundancy is the aim. In reality, single-tier architecture is not frequently employed. To accomplish this, it eliminates redundant data to keep as little data as possible. The way a single-tier data warehouse is made reduces the amount of data that is stored while making a dense data set.

Even though this warehouse design style is suitable for eliminating redundancies, it is not right for companies with complex data needs and multiple data streams. Multi-tier data warehouse architectures can help in this situation since they can handle more complicated data streams.

A relational database system is typically represented by the bottom tier or data warehouse server. This architecture is vulnerable since it does not separate analytical and transactional processing as required. Following the interpretation of the middleware, analysis queries are approved for operational data. This is how inquiries have an impact on transactional workloads.

2. Two-layer architecture

The data structure of a two-tier data warehouse architecture maintains a clear separation between the actual data sources and the warehouse itself. In contrast to a single layer, the two-tier model uses a system and a database server. This style of data warehouse architecture is generally utilized by small businesses that use servers as data marts. The two-tier structure is not scalable even though it is better at data management and storage. Additionally, it only accommodates a small number of users. It consists of four consecutive stages of data flow:

• Source layer: A data warehouse system makes use of several data sources. The information may originate from an information system beyond the company’s boundaries or be initially housed in legacy or internal relational databases.
• Data staging: It entails extracting the data from the source, cleaning it to remove discrepancies and fill in any gaps, and integrating it to combine data from several sources into a single standard schema. The Extraction, Transformation, and Loading Tools (ETL) process can combine data schemas that are different from one another, besides enabling data extraction, transformation, cleaning, validation, and filtration to be loaded into a data warehouse.
• Data warehouse layer: A data warehouse is where one can store information in a way that makes sense as per centralization logic. Users can access data warehouses directly but can also use them to make data marts for specific departments within the company and partly copy the contents from the data warehouse. Data staging, users, sources, access processes, data mart schema, and other information are all stored in meta-data repositories.
• Analysis: This layer allows for rapid and flexible access to integrated data to generate reports, analyze data in real-time, and model fictitious business scenarios. It should have customer-friendly GUIs, advanced query optimizers, and aggregate information navigators.

3. Three-Tier Architecture

The three-tier architecture comprises the source layer – many source systems, the reconciliation layer, and the data warehouse layer. The reconciliation layer sits between the data warehouse and the source data. The primary positive of the reconciled layer is that it creates a uniform reference data model for the whole company, besides setting out the difference between problems with filling the data warehouse and those with getting source data and putting it all together. The top, middle, and bottom tiers make up this hierarchy:

• Bottom tier: A relational database system is typically used. Data is cleaned, changed, and loaded into this layer using back-end tools.
• Middle tier: An online analytical processing (OLAP) server developed using either the ROLAP or MOLAP paradigm makes up the middle tier of a data warehouse. This layer serves as a liaison between the database and the end user.
• Top tier: Front-end client layer makes up the top tier. The tools and application programming interfaces (APIs) you connect to extract data from the data warehouse are considered top tier.

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Top 7 Data Warehouse Tools

Data warehousing technology represents a relatively mature market, which means that there are several top tools to choose from. Some of these include:

1. Snowflake

Snowflake is one solution that may be used to create a cloud data warehouse appropriate for an enterprise-grade application. By enabling users to work with a single language, SQL, to complete blending, analysis, and transformation operations, it simplifies the process of processing data.

Processing power and storage vary due to the shared, multi-cluster design. Unique features of Snowflake include a cloud-neutral approach, shared data architecture for several clusters, the ability to separate the workload and concurrency, minimal administration, and accommodating data that is semi-structured, among others. As a result, it makes it possible to charge for CPU resources based on user activity. Not only that, but scalability accelerates query performance to get insightful results.

2. Azure Synapse Analytics

Microsoft’s Azure Synapse Analytics is an open analytics solution that combines data integration, enterprise data warehousing, and big data analytics. Azure Synapse unifies these realms to ingest, investigate, prepare, process, manage, and provide data for urgent BI and machine learning needs.

Synapse Analytics also offers new capabilities along with these characteristics of a structured query language (SQL) data warehouse.This includes the ability to analyze, query, and store non-relational data, interface with other Microsoft tech, ML, and BI and provide more efficient large-volume data input, transformation, management, and processing. 

3. Google BigQuery

BigQuery from Google is a high-end data warehousing solution. It is one of the top warehouse solutions because it enables lightning-fast SQL queries, which shortens the time needed to store and query large datasets. Additionally, it regulates who has access to the project and provides the option to examine or query the data.

Google BigQuery provides seamless data access control and automatic information sharing. Its key features include flexible data ingestion, Cloud Dataflow, read-and-write data, and services for automated data transfer. Users may enjoy complete command over who can view the saved data. Data in BigQuery may be easily read and written using Cloud Dataflow, Spark, and Hadoop. Data warehouses on BigQuery also offer cost-cutting measures.

4. Amazon Redshift

It is a low-cost, easy-to-use data warehousing technology. Through SQL, it examines nearly every type of data. When using Amazon Redshift to scale computation and storage independently, it becomes necessary to profile the compute needs of diverse production workloads. This will guarantee that the computational layer of the Amazon Redshift cluster architecture is suitably balanced. There are no upfront costs for installing Amazon Redshift. It lets you monitor, manage, and scale your data warehouse by automating most routine administrative processes. Using Amazon Redshift, changes to node types or numbers are possible, increasing the data warehouse cluster’s dependability. Climate control is essential in every data center and continuously checks the cluster’s condition.

5. IBM® Db2® Warehouse

IBM® Db2® Warehouse provides a client-hosted, predefined data warehouse consistent with private and virtual clouds and other systems that support containers. It is designed to be the optimal hybrid cloud option when one has to preserve control over the data while retaining the flexibility of the cloud. The solutions in the IBM InfoSphere Warehouse family combine the power of DB2 with an IBM data warehousing architecture. Building a comprehensive data warehousing system with front-end analytical tools and a highly scalable relational database is possible with InfoSphere Warehouse. 

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6. Oracle Autonomous Data Warehouse

The Oracle Data Warehouse software treats a group of data as a whole, and its primary function is to store and retrieve relevant data. Allowing several users to access the same data aids the server in successfully managing enormous amounts of data. Oracle has implemented many self-service features to increase the productivity of analysts, data scientists, and developers. This relatively new cloud computing system is scalable, responsive, and simple to use.

Oracle Autonomous Data Warehouse can support single-instance and real application clusters and real application testing. It supports a common architecture between any private cloud and Oracle’s public cloud, enabling high-speed connections to move large volumes of data. There is seamless compatibility with UNIX/Linux and Windows platforms, support for virtualization, and the ability to connect to remote databases, tables, and other resources.

7. Firebolt Cloud Data Warehouse

With Firebolt, enterprise data challenges are resolved at any scale with incredible speed and elasticity. Firebolt has entirely revamped the cloud data warehouse to provide a quick and effective analytics experience. You may now analyze far more data at a greater level of granularity with queries of high magnitude while performing searches. The process of scaling up or down is simple to accommodate any workload, the volume of data or the number of concurrent users. Firebolt concentrates on simplifying all formerly challenging and time-consuming tasks. 

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Data Warehouse Applications

The critical applications of data warehousing are:

1. Banking

Bankers can handle all available resources more efficiently with the ideal data warehousing solution. To help them make better decisions, they can better examine their consumer data, governmental requirements, and market trends. The majority of banks also make use of warehouses to manage the resources at their disposal efficiently.

They are used by some banking sectors for market research, performance evaluations of individual products, the study of exchange and interchange rates, and the creation of marketing initiatives. Analysis of cardholder transactions, spending habits, and merchant categorization allows the bank to offer lucrative bargains and special offers based on cardholder behavior. This sophisticated analysis is performed using data warehouses.

2. Government

The public sector may use data warehouses for services linked to accounting, such as payroll administration, human resources, recruitment, etc. The U.S. federal government is also known to use them for compliance research.

In addition to connecting their complete criminal law database to a state’s data warehouse, the government can use data warehouses to maintain and analyze tax records and health insurance information. This could help forecast criminal activity based on patterns and trends, look up terrorist profiles, assess threats, and detect fraud. The patterns and trends that emerge from the data analysis of historical information about previous offenders are used to forecast criminal activity.

3. Manufacturing production and distribution

Manufacturing and distribution vendors may consolidate all their data under one roof using a data warehouse. This helps forecast market changes, examine current patterns, identify potential growth areas, and ultimately make decisions that will have a positive impact. 

For example, a manufacturing company has to make many buy-or-make choices that can impact the direction of the industry. For this reason, they use high-end OLAP tools as part of data warehouses to examine current business trends, identify warning signs, and ultimately make better choices. They also utilize data warehouses to keep track of product shipments and portfolios over time – discovering successful product lines and assessing weaker ones based on client input and historical data.

4. Retail data management

Retailers must keep the records of multiple parties since they act as the intermediary between wholesalers and end users. The use of data warehousing enters the picture to assist them in organizing their data storage. They can employ data warehouses to keep tabs on products, advertising campaigns, and consumer purchasing patterns. Additionally, they may use a process of predictive elimination to calculate the shelf space for each product line by analyzing sales to identify fast- and slow-selling product lines.

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5. Healthcare data storage

Data warehouses are also crucial in the healthcare industry. They can feed warehouses with their financial, clinical, and employee data. This would make it easier to plan and forecast clinical outcomes. Healthcare firms can also monitor and evaluate customer feedback using warehouse analytics. It also assists in exchanging information with affiliated insurance providers, medical assistance providers, patients, etc. Hospitals can also utilize data mining to track patient trends and advise doctors on operations and testing.

6. Learning analysis in education

Data warehousing is necessary for the educational industry to thoroughly understand their faculty members’ and students’ data in a compliant manner. For educational institutions to make wise and informed judgments, a data warehouse can give them access to real-time data flows. Universities typically use warehouses to collect data for managing their human resources, proposing research funds, and studying the demographics of their students.

Most colleges’ financial departments, including the financial aid division, rely on this technology. It employs data warehouses to bring together data from several sources into a single repository for educational decisions.

7. Underwriting in Insurance

Data warehousing is necessary for the insurance industry to preserve records of current clients and analyze them to spot patterns. In addition to keeping track of records, warehouses are utilized mainly to evaluate data patterns and future customer trends. Warehouses also enable the creation of insurance promotions and offers specifically tailored for each customer. Finally, its most prominent use is to assess customer risk during underwriting and setting the optimal insurance premium. 

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Takeaway 

Data warehouses are now a staple for large enterprises. They help collect disparate data from multiple sources in one location so enterprises can run advanced analytics by integrating with business intelligence software. According to a 2021 report by Allied Market Research, the global data warehousing market will reach $51.18 billion by 2028. This emphasizes its ongoing relevance and the necessity to spend on the appropriate data warehousing systems. 

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