What is Data Warehousing: Definition, Stages and Tools

What do you mean by data warehousing and what is it made of?

Every day, organizations generate a huge amount of data. To make the most of this information, it needs to be stored in a central location so that different departments can use it for analysis, reporting, and decision-making. This is where data warehousing comes in – it’s the process of gathering, storing, and managing data from various sources into one convenient repository.

Think of a data warehouse as a giant electronic filing cabinet for a company’s business data, gathered from various sources and made easily accessible for analysis. The system is divided into three parts: the front-end client, which presents the data through tools like reporting and data mining; the analytics engine, used to analyze the data; and the database server, where all the data is stored. These three parts work together to make data warehousing the backbone of a business intelligence system, helping organizations make smart decisions.

History of Data warehouse

The Data Warehouse enables users to improve their organization’s performance by providing insight into the data. This development emerged as computer systems grew more complex and required managing larger amounts of information. Although not a new concept, the evolution of Data Warehousing has a rich history.

Some important milestones in this evolution include:

In 1960, Dartmouth and General Mills partnered in a research project and defined the terms “dimensions” and “facts”.
In 1970, Nielsen and IRI introduced dimensional data marts for retail sales.
In 1983, Tera Data Corporation released a database management system designed specifically for decision support.
In the late 1980s, IBM workers Paul Murphy and Barry Devlin created the Business Data Warehouse.
Bill Inmon, considered the father of data warehousing, further developed the concept and wrote extensively about building, using, and maintaining a warehouse and Corporate Information Factory.

How Data warehouse works?

A Data Warehouse serves as a central repository that collects data from one or more sources. The data is extracted from transactional systems and relational databases and can be either structured, semi-structured, or unstructured. The data is then transformed and loaded into the Data Warehouse to allow users to access it through Business Intelligence tools, SQL clients, and spreadsheets. The Data Warehouse merges data from various sources into a single, comprehensive database.

By bringing all the information together in one place, organizations can gain a more complete picture of their customers. This enables more informed decision making by considering all available information. Data warehousing also enables data mining, which involves searching for patterns in the data to drive increased sales and profits.

Types of Data Warehouse

There are three main types of Data Warehouses:

Enterprise Data Warehouse (EDW):
An EDW is a centralized warehouse that provides decision support across the enterprise. It offers a unified approach to organizing and classifying data and provides access to data based on subject divisions.

Operational Data Store (ODS):
An ODS is used when neither Data Warehouse nor OLTP systems meet an organization’s reporting needs. It is real-time refreshed and often used for routine activities like storing employee records.

Data Mart:
A Data Mart is a subset of the Data Warehouse designed for a specific line of business, such as sales, finance, or marketing. Data can be collected directly from sources in an independent Data Mart.

What are the stages of building a data warehouse?

There are 4 stages of a data warehouse that help in finding out and understanding how the data changes in the warehouse.

4 Stages of Data Warehousing

Offline Operational Database: This is the initial stage where data is simply copied to a server from an operating system. It is done so that data loading, processing, and reporting do not affect the performance of the operational system.

Offline Data Warehouse: In this stage, all the data warehouses are updated on a regular time cycle from the operational database to get actionable business insights.

Real-time Data Warehouse: In this stage, data warehouses are updated based on transaction or event basis. Whenever a transaction takes place in an operational database, it is updated in the data warehouse.

Integrated Data Warehouse: This is the final stage where all the transactions which are used daily by the organization are passed back into the operational system. Each transaction that takes place in the operational database is updated in the warehouse simultaneously. These transactions are then forwarded to the operational database.

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Components of Data warehouse

Four key components of a Data Warehouse are:

Load Manager: Also known as the front-end component, the Load Manager is responsible for all operations involved in extracting and loading data into the warehouse. This includes transforming the data to prepare it for storage in the Data Warehouse.

Warehouse Manager: The Warehouse Manager handles all operations related to managing the data within the warehouse. This includes analyzing data for consistency, creating indexes and views, generating denormalization and aggregations, merging and transforming source data, and archiving and backing up data.

Query Manager: Also known as the back-end component, the Query Manager manages user queries. It directs queries to the appropriate tables and schedules the execution of queries.

Who needs a Data warehouse?

A Data Warehouse is necessary for various types of users including:

Decision-makers who require access to large amounts of data
Users who use complex, custom processes to gather information from multiple sources.
Those who want a simple means of accessing data.
Those who need a systematic approach to decision-making.
Users who require quick performance on a vast amount of data for reports, grids, or charts.
Those who aim to uncover hidden patterns in data flows and groupings.

What are the examples of data warehousing in various industries?

Data Warehousing has a range of applications in various industries, here are some examples:

Investment and Insurance: In this industry, data warehousing is utilized for analyzing customer data, market trends, and other relevant information. Data warehousing plays a significant role in Forex and stock markets.
Healthcare: Data warehousing systems are used in the healthcare sector for forecasting treatment outcomes, generating reports, and sharing data with different units such as research labs, medical units, and insurance providers. Enterprise data warehouses serve as the backbone of healthcare systems as they are kept up to date with crucial information that can aid in saving lives.
Retail: Data warehousing is used by retail chains for business intelligence and forecasting purposes, as well as distribution, marketing, examining pricing policies, keeping track of promotional deals, and analyzing customer buying trends.

Best practices to implement a Data Warehouse

When designing a Data Warehouse, consider the following steps to ensure consistency, accuracy, and integrity of the data:

Plan to test the data for consistency, accuracy, and integrity.
Ensure that the Data Warehouse is well-integrated, well-defined, and time-stamped.
Use the appropriate tools, follow a development life cycle, and address data conflicts.
Avoid replacing operational systems and reports.
Do not spend excessive time on data extraction, cleaning, and loading.
Engage all stakeholders, including business personnel, in the implementation process and make the Data Warehouse a team project.
Train end-users on how to use the Data Warehouse effectively.

Advantages & Disadvantages of Data Warehousing

The benefits of using a Data Warehouse (DWH) include:

Quick access to critical data from multiple sources in a centralized location.
Consistent information across various cross-functional activities and support for ad-hoc reporting and querying.
Integration of many data sources to ease the burden on the production system.
Reduced turnaround time for analysis and reporting.
Reorganized and integrated data for easier reporting and analysis.
The ability to store large amounts of historical data for trend analysis and future predictions.

However, there are also some drawbacks to using a DWH, including:

Inadequacy for unstructured data.
The time-consuming process of creating and implementing a DWH.
Potential for the DWH to quickly become outdated.
Difficulty in making changes to data types, ranges, source schema, indexes, and queries.
A complexity that may be challenging for average users.
The potential for project scope to increase beyond initial projections.
The risk of users developing different business rules.
The need for significant resources for training and implementation.

What are the different Data Warehousing tools?

Data warehousing increases the query-response time, allows businesses to fetch deeper insights, and improves the access to the organization’s information. Earlier companies used to build their own data warehouses but thanks to cloud technology, the cost of data warehousing for businesses has been reduced.

Here we’ll talk about some of the cloud-based tools that are not just fast but are highly scalable and are also available on a pay-per-use basis:

Teradata
Snowflake
Google BigQuery
Microsoft Azure
SAP HANA

Teradata

Teradata is the market leader when it comes to data management and warehousing. It offers 360-degree insights for collecting and analyzing large amounts of enterprise data in the cloud. The tool has an extremely fast parallel querying infrastructure that speeds up access to actionable insights.

Snowflake

Snowflake allows you to set up an enterprise-grade cloud data warehousing system. With the help of this tool, you can easily analyze data from different sources: structured and unstructured. It has a convenient pricing system which means that you only pay when you use it. Its architecture is reliable and reduces unnecessary complexity.

Google BigQuery

This is a cost-effective data warehousing tool which has built-in machine learning capabilities. It can be integrated with Cloud ML and TensorFlow for creating powerful AI models. This cloud-based data warehouse also supports geospatial analytics. It sets itself apart by its ease of access. When the querying is considered with SQL and Open Database Connectivity, it is easier with this offering.

Microsoft Azure

Azure is a cloud-based database by Microsoft that can be optimized for petabyte-scale data loading and real-time reporting. This data warehousing tool is compatible with other MS Azure resources. Its platform is easy to understand and lets you work with different types of structured and unstructured data.

SAP HANA

SAP-HANA is a cloud based data warehousing tool that supports high speed, real-time transaction processing, and data analytics. It serves as a centralized interface to access, integrate, and visualize data. With SAP-HANA, you can also query remote databases without moving your data.

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Endnotes

In today’s data-driven business world, every organization must have access to the right data integration platform. When large amounts of generated data fails to sustain in the market, it is a huge loss for an organization. This is where data warehousing steps in. It not only saves time, generates high ROI, but also improves the quality and consistency of data. It also delivers improved business intelligence and empowers organizations to predict outcomes with confidence.

People usually get confused between data warehousing and data analytics. But on one hand, data warehousing is the process of collecting all organizational data into one place, data analytics is about analyzing raw data and drawing conclusions. This means that the process of data analytics begins once the process of data warehousing is over.

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