When making important decisions in your organization, ensuring the integrity, accuracy, and completeness of the data used to inform it is key. This is where data warehousing comes in.
Without it, you are forced to rely on the raw data stored within each application. Not only is this process slow, but the accuracy of the data can be compromised when depending on human processes to retrieve it from various applications.
Data warehouses enable businesses to run these powerful analytics by pulling, storing, and processing data to make it ready for decision-makers to access.
Do you want to know more about how a data warehouse can solve this issue, and how to implement data warehousing in your organization? Keep reading to find out.
What Is a Data Warehouse?
A data warehouse is a central data management system that stores and consolidates data from different sources within an organization in order to support business intelligence (BI) activities such as data analytics, reporting, data mining, machine learning, etc.
The data warehousing system pulls data, processes it, and organizes it to enable efficient analysis that can be easily accessed by anyone in an organization.
Even though data warehouses have been around since 1980s, they have evolved considerably over the past few years due to the rise of big data. Data warehouses now possess advanced analytics capabilities as well as data visualization tools.
Data warehouse vs. database
It’s easy to confuse both terms as a data warehouse and a data base share some similarities. A database is a key component of a data warehouse and can be defined as a storage system where data can be quickly recorded and retrieved. A database collects data for transactional purposes, application support but also to enable reporting. Common databases that are commonly used in the enterprise include ERP, SQL databases, Customer Relationship Management (CRM) systems, business process management systems but also Excel spreadsheets.
In comparison, a data warehouse is designed to centralize and store large amounts of data from multiple databases and make them easier to analyze. A data warehouse uses an automated process called ETL and which stands for extracting, transforming, and loading data into a data warehouse and brings a substantial advantage when it comes analyzing data without the technical expertise.
Data warehouse vs data lake
Data lakes and data warehouses are both used to store, manage, and analyze data. They complement each other and support different use cases even though they have some overlaps.
A data warehouse is a repository that stores structured, cleaned and organized data in order to serve a specific business purpose. In comparison, a data lake stores large volumes of structured, semi-structured, and unstructured data in its native format, and processes it later on-demand.
The need for a data warehouse becomes crucial when an organization has a high level of data diversity and analytical requirements and want to make better decisions in less time. In this scenario, the data warehouse will do the analytic work based on the best data available to ensure decisions can be made faster.
What Are the Different Types of Data Warehouses?
There are three main types of data warehouses. The kind that is right for your company will depend upon your organization’s requirements and how you intend to use it. Here are the key differences to consider:
Type 1: Enterprise Data Warehouse
An enterprise data warehouse (EDW) is the most sophisticated data warehouse. It is used to centralize large volumes of data from across the business and brings a unified approach to organize and classify data.
Type 2: Operational Data Store
The first type of data warehouse, the operational data store (ODS), pulls in data from various sources across the business. The data is refreshed in near real-time and is preferably used for routine business activity.
Type 3: Data Mart
The final kind of data warehouse is the data mart. It is a subset of the data warehouse and supports a specific team or business unit. For example, a finance team may use a data mart to collate data required for accounting purposes.
What Are the Key Characteristics of a Modern Data Warehouse?
With the rise of cloud technology, data warehousing has undergone many changes over the past ten years to provide inbuilt scalability, high availability, performance, and flexibility.
While traditional on-premises data warehouses can still meet an organization’s objectives, they struggle with modern data architecture and are not scalable and cost-efficient enough to deal with all the data that an organization is generating and which keeps on growing.
A modern data warehouse allows to combine all kinds of data, at any scale, and easily to get business intelligence insights through dashboards, visualization tools as well as advanced analytics for all your users.
Additionally, a modern data warehouse focuses on value instead of transaction processes and is primarily built for analytical purposes.
Microsoft has introduced various cloud-based services through Azure to support the modern data warehouse goals and enable a flexible deployment:
Azure Data Factory is a cloud-based ETL and data integration service that allows you to create data-driven workflows for orchestrating data movement and transforming data at scale.
SQL Server Integration Services (SSIS) is a platform that performs high-performance data integration tasks such as extraction, transformation and ETL for data warehousing.
Azure Data Lake is a hyper-scale repository that allows you to store data of any size and kind.
Azure Blob Storage allows you to store and access massive amounts of unstructured data.
3. Prep & Train
Azure Data Bricks: Your data in Azure Blob Storage/Azure Data Lake can then be leveraged to perform scalable analytics with Azure Databricks and obtain cleaned and transformed data.
4. Model & Serve
Move your clean and transformed data to Azure Synapse Analytics and combine it with your current structured data to create one single data hub. You can use built-in connectors between Azure Databricks and Azure Synapse Analytics to move data at scale,
Azure Analysis Services is a cloud data analytics platform that enable large amounts of data to be queried for ad-hoc analysis.
Power BI is a suite of business analytics tools that connects to various data sources and simplify data preparation to create visually interactive reports that are easy to consume.
Three More Things You Need to Know Before Creating a Data Warehouse
Are you ready to introduce a data warehouse to your organization? Here are three more decisions you need to make before you get started:
1. Will you host your data warehouse in the cloud or on-premises?
The platform you will use to host your data warehouse is one of the most important considerations. While many businesses are moving their operations to the cloud, both options have their pros and cons.
For example, on-premises solutions can be more efficient and secure, but they often lack the scalability of cloud-based solutions and are expensive since you must purchase, deploy, and maintain all hardware and software.
Conversely, cloud-based systems are highly scalable and cost-effective and reduce the business continuity risks associated with storing data on-site.
You can also opt for a hybrid solution if you don’t want to have everything in the cloud.
2. What are your requirements?
Arguably, the most crucial part of a data warehousing project is the requirements phase. This step will guide you through the rest of the process, ensuring the end solution is fit for purpose.
Start with an in-depth business and systems analysis to understand what you will use the data warehouse for, the data it must contain, and how it will be retrieved. It’s also essential to know how the data relates to each other and how your current systems store it.
Here are some key questions to include in your analysis.
- What do you need to know about your source data?
Before you begin creating your data warehouse, you should conduct a full data discovery exercise to profile your source data. Validating your data early in the project helps to guide decisions about implementation and choose the right solution. It also improves the cost efficiency as discovering errors at the testing stage will incur additional costs to rectify.
- What are your data mapping specifications?
Detailed requirements specifications for data mapping are vital. This activity ensures the required source data maps onto the target and shapes the migration code, which will be verified in testing.
- What is your migration strategy?
The requirements that emerge from the previous stage will contribute to your migration strategy. It is important to consider the needs of the business when planning your strategy as any system migration to extract and process data will likely lead to downtime. This risk can be reduced by opting for an incremental migration rather than a big bang migration, but the needs of the organization must be considered carefully when reaching a decision.
3. Which vendor will you choose?
Once you have decided where to host your data warehouse, you need to consider who you will choose to provide it.
This choice can seem like an impossible task, given the large number of vendors available: Azure Synapse Analytics, Snowflake, Amazon Redshift, Google BigQuery, etc.) This dilemma is why it’s essential to be clear on your requirements before you reach the decision, so you can verify the solution you’ve chosen meets your needs.
To summarize, there’s a lot to consider when implementing a data warehouse in your organization, but the benefits are clear if your organizations deals with large volumes of data. If you need to make faster decisions and support your employees to do the same, a data warehouse is a logical solution.
Do you need more guidance to understand your requirements and determine the next step?
Get in touch to find out how Softlanding can help.