A Detailed Road Map to Data Warehouse Implementation

04 October 2023

8 min read

Introduction to data warehouse implementation

Data has been growing tremendously over the past few years and this data contains valuable insights that help the business grow faster. It has become a challenging task for organizations to manage ever-growing data sets with traditional systems and that’s where the data warehouse technology comes into the picture. Data warehouse plays a key role in organizing large volumes of business data from a wide range of sources. Successful data warehouse implementation helps business in taking more informed decisions.

Data warehouse implementation can be done after a clear understanding of organizational requirements. Each Data Warehouse implementation process is associated with a few essential components that need to be defined while designing the data warehouse implementation process and those components are Metadata, ETL, OLTP/OLAP, Data Marts, etc.

WHAT IS DATA WAREHOUSE IMPLEMENTATION?

A data warehouse is defined as an area where huge volumes of business data can be stored. Employing BI and analytics tools on this data helps organizations in bringing the hidden insights out of data. Organizations make uses of data warehouse technology to effectively manage, organize, and store large volumes of data.

A Data Warehouse collects raw data from multiple sources and transforms it into standard data by making use of advanced ETL tools. On this standard data, organizations can conduct analysis and make more informed business decisions. The process of establishing a data warehouse system in an organization is called a data warehouse implementation. This can be done by taking multiple factors into consideration.

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Components of Data warehouse Implementation: Following are the 5 core components of data warehouse implementation.

1) DATA MARTS:

A data mart is an essential component of a data warehouse platform and is a subject-oriented database. It focuses on storing data of a particular business line such as sales, marketing, or finance, etc.

2) OLTP

The word OLTP stands for online transactional processing of data that focuses on transaction-oriented data. It typically deals with inserting, updating, and/or deleting a small volume of data in a database. In general, it deals with a large number of transactions and users.

3) OLAP

OLAP stands for online analytical processing and acts as a powerful medium for data discovery. This layer processes and analyzes the data stored in the database. It is quite opposite from OLTP and deals with data that is not frequently changeable in nature.

4) ETL

ETL is a short form of Extract Load and Transform. It pushes huge volumes of raw data from multiple business sources to a data warehouse by making the data ready for analysis. In simple terminology, an ETL tool extracts data from various business sources make some transformations to raw data in order to make it compatible with the destination system and loads data into the targeted system.

5) METADATA

Metadata is defined as data that provides the information of other data. In layman words, it is “data about data”. There are different types of metadata that exist which include descriptive metadata, administrative metadata, structural metadata, statistical metadata, reference metadata and legal metadata.

Steps involved in data warehouse implementation: Following are the typical steps required for implementing a data warehouse in an organization.

1) REQUIREMENT GATHERING:

Requirement gathering is the first step in the data warehouse implementation process and here you need to interact with multiple teams which are:

Decision Makers: Here you need to interact with business leaders, strategists etc. these people will make you understand the strategic objectives of an organization.
IT Teams: IT teams play a crucial role and help you practice the implementation of a data warehouse in an organization. They help you connect sources to the pipeline, rectify errors, and provide all other support you need.
Analytics: This team will help you understand the complete requirements of the project.
Security and Compliance: Data warehouse implementation process needs to interact with various applications and it must not break any security rules.

After finishing the above tasks you are ready to move to the next process.

2) Set-up Warehouse Environment

Here you are presented with multiple options to set up your warehouse environments which are as follows:

Public Cloud: You can make use of hosted cloud solution like Google Cloud or AWS.
Private Cloud: You can host on your own hardware.
On-premise: You can host on local hardware.
Hybrid cloud: You can store data on promises and use cloud or mix on-premises and cloud.

Each environment is associated with different advantages and disadvantages choosing the best one that fits your requirement will help you in the successful implementation of the Data warehouse platform.

Irrespective of the environment you choose there is a need to set up three separate environments which are:

Development Environment: Here the development team works to test integrations and features of the project. This environment stores the test data and encrypts the sensitive information in it.

Test Environment: This Environment allows you to test the warehouse and also enables you to perform QA.

Production Environment: It is a live data warehouse and can be accessed by users and an analytics team. Here you are not allowed to make any changes.

3) Selecting a data model

Selecting a data model is a complex task of the data warehouse implementation process. Each data source has its own schema whereas a data warehouse has a single schema. All these source Schemas should align and fit with the data warehouse schema. To fulfil this requirement you need to choose a data model that fits your requirements and is easily scalable.

Types of Schemas:

Star Schema
Snowflake Schema
Galaxy Schema
Constellation Schema

Data Scientists are the professionals who design schemas from the scratch. There are multiple cloud and on-premises systems that help you select a schema model that fits your requirements.

4) CONNECT TO SOURCES

It is involved with a two-step process: one is connecting with the data sources & extraction of data and the other one is loading the data into a data warehouse or targeted system. Data extraction can happen in the following ways:

API call
Filter Transfer
Direct query

Once the data obtained from the data sources using any of the above-stated ways the next thing you need to do is loading it into a data warehouse platform.

5) TRANSFORMING INCOMING DATA

Here we make use of ETL tools to extract and load data. Here ETL tools convert the data from its original schema to destination schema.

Following are the steps associated with transformations which are:

Validation: It makes sure that the data fits within logical constraints.

Cleansing: It eliminates unnecessary or duplicate data and makes it authentic.

Harmonization: Converts all data into a single format. EX: Convert all date formats into MM/DD.

6) CREATION OF DATA MARTS

Data Warehouse stores a variety of business information from multiple sources. The majority of the people don’t require all this information and it becomes a challenging task to retrieve required information from large sets of data.

Data Mart’s acts as a solution for providing simplified access to data and provides the right data to the right user at the right time. It acts as a subdivision of a data warehouse platform and provides an area for storing the relevant data such as sales data, marketing data, operations data, etc. Data Marts improves data security as they allow the right people from viewing relevant information.

7) CONFIGURE BI ANALYTICS

There is a wide range of Business Intelligence platforms available in the market which is designed in a way to easily integrate with all types of data warehouse platforms. You can also have a chance to integrate BI tools with ETL tools, which gives you faster and efficient insights. BI tools largely rely on 3 factors which are volume, velocity, and veracity.

8) AUDIT AND REVIEW

This is the last step in the implementation of the data warehouse platform. The data warehouse starts working and ready to serve with the right information to the analytics team. Here you have to check the functionality of the data warehouse platform and make use of data quality testing tools to measure the quality of data warehouse contents. Here you can also execute sense checks to compare raw data and stored data.

Advantages of Data Warehouse Implementation: Following are the typical benefits of using a data warehouse in an organization.

EFFECTIVE DATA MANAGEMENT:

Using Data warehouse platform organizations can collect and store all their data in a single destination. It helps businesses to effectively manage data and streamlines processes for business analytics.

IMPROVED ANALYTICAL PROCESS:

Data warehouse acts as a powerhouse for the business intelligence tools and presents organizations with the right insight to make the right business decisions.

COST REDUCTION:

It helps in eliminating the rework and employing the resources in the right area.

COMPETITIVE ADVANTAGE:

Having the right information and the ability to make quick and more informed decisions will help organizations in finding new market opportunities and achieving a competitive edge over their rivals.

Conclusion: Implementing a sound data warehouse contributes a lot to the growth of an organization. Data warehouse helps organizations in increasing their efficiency, optimum usage of resources, finding new opportunities, analyzing market conditions, etc. Efficient usage of data warehouse platforms will streamline the process for the success of the business.