What Is a Data Warehouse?
Enterprises large and small are trying to leverage data to enhance their businesses using data analytics. Because large amounts of data are now being utilized, businesses have to find a way to store and access this data in an accessible and efficient way. There are several options for storing data, including databases, data lakes, and cloud storage; but this article will be looking specifically at data warehouses as a means of efficient and effective data storage.
A data warehouse can be defined as a central repository that ingests data from different sources, like relational databases and transactional systems. They are designed to store large amounts of data and enable organizations to analyze and present data accurately.
Data warehouses are being used in all kinds of industries, including health care, banking, and retail. For instance, when you store all of your data in a data warehouse, it can be utilized in the retail industry to pull information like customer lifetime value analysis, market basket analysis, customer buying behavior analysis and more.
In the healthcare industry, once data is stored in a warehouse, it can be used for its analytic capabilities. Visualization and reporting tools can be used to forecast and present results that can predict the health conditions of patients or analyze abnormalities in the health of a patient. Banking services can utilize data warehouses to analyze data and improve their services while generating reports to present to stakeholders.
Data warehouses offer a wide range of benefits, including historical data storage, data security, data consistency, and data stability.
In this article, you’ll learn about the benefits and features of a data warehouse and how to effectively implement, use, and maintain a data warehouse.
Data Warehouse Features and Use Cases
Data warehouses have many unique features and uses, including the following:
- Subject oriented: They store historical data, including previous sales, marketing campaigns, and human resources information. This data helps organizations gain insights and make significant predictions to improve business operations.
- Integration: They store data from multiple sources and can be synchronized to structure the data.
- Nonvolatility: Once data is stored in the warehouse, it can’t be changed.
- Time variant: Because data warehouses store historical data, you can access data based on a time stamp and check if there are changes to the data over time.
- Informed decision-making: Data stored in the warehouse is used by different data professionals to make efficient data-driven decisions.
- Better data visualization: Warehouses provide different visualization and reporting tools to analyze and present data efficiently. You can also use external visualization tools like Tableau, Power BI, Plotly, QlikView etc. and feed data directly from data warehouses to these tools.
Data warehouses not only store data but also provide value. They consolidate data from different sources and synchronize it in a way that data from each source is formatted similarly. This structured data is then used by data analysts and data scientists to create visualizations and make predictions.
Here are some of the main ways data warehouses can be utilized:
- Customer data: Companies can collect data that can be utilized for analytics and machine learning to improve services. Businesses, like Facebook and Google, collect customer data to improve user engagement, and keeping it secure is vitally important. When using data warehouses as a storage option, you need to choose security features like strict user access control, encryption in data transfer, and other important security mechanisms.
- Machine learning (ML): Data stored in a warehouse doesn’t sit idle; it’s used for all kinds of analysis and predictions. For instance, ML applications can be used for weather forecasting, self driving cars, health monitoring, and more.
- Data sanitation: As you add data to a warehouse, it becomes harder to manage. If you keep adding data, you have to continually increase your storage capacity, however, this can be very cost prohibitive. To create space and keep costs in check, you can sanitize your data. Sanitation is the process of deleting data from your warehouses that is no longer useful.
It’s important to note that once data is stored in warehouses, you can’t change it; and once data is deleted, you’re unable to retrieve it.
- Real-time access and analysis: Sometimes there are use cases that need real-time access to data; for example, analysis of a patient’s health condition. In this case, a warehouse is useful for accessing data whenever it’s needed.
Data Warehouse Architecture
A data warehouse is made up of three tiers. Each tier helps different data professionals to leverage the data.
- Tier 1: This is considered to be the bottom tier of the warehouse. This layer is usually a relational database that ingests data from different data sources. It’s responsible for holding all the data.
- Tier 2: Tier 2 or the middle tier is where data is accessed and processed quickly. This layer is made up of online analytical processing (OLAP) servers that are used to fetch and analyze data from the warehouse.
- Tier 3: The top tier or Tier 3 is the frontend that comprises various visualization and reporting tools that enable users to analyze and present data to different stakeholders and customers.
Data warehouse tier architecture
You should not consider a warehouse just as a storage mechanism; instead, it should be considered as a platform that is used for data analysis, data mining, ML, and artificial intelligence (AI) for a large amount of historical data.
The ETL (extract, transform, and load) process is an integral part of ingesting data from multiple sources into a data warehouse.
- Extract: At this stage, data is extracted from various sources, like XML files, relational databases, NoSQL databases, and sensors. Data is then fed to a staging area before it’s fed to the warehouse. The staging area helps format the data so it doesn’t damage the warehouse and make it impossible to roll back.
- Transform: Data from various sources is transformed to a standard format and fed into the warehouse. The tasks that transform the data include the following:
- Data cleaning: Data that is being fetched from multiple sources may have some missing or corrupted values; in this stage, the data is dealt with to remove such values.
- Data filtering: Filtering data helps you load only specific fields from various sources.
- Data joining: For specific needs, data from various sources are joined together into one.
- Data sorting: Data is sorted based on the requirement.
- Data normalizing: ML and analytic algorithms require data to be normalized so that all the attributes are on the same scale.
- Load: This is the final stage where transformed and cleaned data is fed to the warehouse. Usually, data is ingested as a scheduled batch operation, or it can be done regularly if the organization and its LoB (line of business) require it.
The ETL process is also divided into two different categories: Batch ETL and Streaming ETL.
Batch ETL vs Streaming ETL
In the traditional ETL or Batch ETL process, softwares extract data from various sources on a scheduled basis and then transform it by applying various transformations, and finally load it into a warehouse.
On the other hand, the Streaming ETL process moves the real time data from one place to another. Streaming ETL is important for new technologies like IoT, online retailing, and banking services since it generates data in real time and at a great velocity.
You can learn more about both types of ETL processes in this article.
Data Warehouse Components
Data warehouses are built according to the needs of an organization but are composed of various components that coordinate with each other to help data specialists make use of the data, including the following:
- External data source: These are the sources from where the raw data is fetched to feed into data warehouses.
- Data staging area: Data from various sources has different formats and needs to be stored in intermediate storage before being converted to a uniform format.
- Data warehouse database management system (DBMS): This storage area is where transformed data is finally stored with metadata.
- Data marts: Data marts are an optional component that is dependent on the organization’s needs. Warehouses collect all the data for every LoB in an organization while data marts are used to store LoB-specific data that is collected only from the warehouse.
- Data mining tools: This is a collection of tools used for data mining and knowledge discovery.
- OLAP: The OLAP is a collection of various software tools used for analysis and business decisions.
- Reporting tools: These are a set of tools used for the reporting and visualization of data.
Data warehouse components
Things to Consider When Designing a Data Warehouse
Both logical and physical designs should be considered when designing a data warehouse.
Logical design refers to the relationship between different entities in the warehouse, whereas the storage and retrieval of data are considered in the physical design.
Before designing, consider the following:
- Type of data that will be stored in the warehouse
- Relationship between and within data groups
- Different transformations required for data before storage
- Refresh rate of the data
Existing Options for Running a Data Warehouse
There are currently several existing options for running a data warehouse, including Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure, as well as a few other storage services that offer unique advantages.
Snowflake was developed on top of the cloud infrastructures of Amazon Web Services (AWS), Microsoft Azure, and Google. There’s no hardware or software to select, install, configure, or administer; so it’s ideal for businesses that do not want to spend resources on the setup, maintenance, and support of in-house servers.
If you utilize an ETL tool, like Stitch, data can simply migrate into Snowflake.
Redshift is a warehouse service provided by Amazon and is a major component of AWS. It’s a fully managed petabyte-scale cloud data warehouse tool for storing and analyzing large data sets. It can also perform large-scale database migrations.
If ETL is at the heart of your operations, Amazon Redshift may be a good option for you.
BigQuery is a data warehouse service provided by Google that offers the functionality of analyzing petabytes of data. This service provides various tools that can be used to access, analyze, and report on the data present in it.
BigQuery is normally used at the end of big data ETL pipelines or for complex analytical queries and processing.
Microsoft Azure Synapse Analytics
Microsoft Azure is a data warehousing service provided by Microsoft that has unlimited analytic services. Data integration, enterprise data warehousing, and big data analytics are all part of its analytics package.
It’s considered to be faster than BigQuery and Redshift and is preferred by companies that value this feature.
Other Storage Services
PostgreSQL is a very advanced database that can work as both a transactional database and an efficient, low-cost data warehouse. Because it’s open source, it’s a free service, unlike the previously mentioned pay-as-you-go options.
Unlike other warehouse services providers, PostgreSQL provides very minimal data warehouse services that are often used by small-scale businesses.
Alternative Warehouse Options
There are also other alternatives to consider, including data lakes and large-scale databases.
Data lakes can store structured, semi-structured, and unstructured data. Because of this, data professionals, like data analysts, data scientists, and ML engineers, can utilize the data to make informed business decisions. When data professionals need to work on data, like images and text, data lakes are the preferred storage mechanisms.
Traditional databases can’t provide analytic services, but they can hold a large amount of data. You can partner with external analytics tools, like Tableau, Microsoft Power BI, Microsoft Excel, and Python, Looker, Sigma, to help make business decisions.
When data storage is the only requirement of a company, traditional databases are the preferred storage mechanisms.
Data Warehouses vs Transactional Databases
The purpose of a transactional database is to store data for transactional processing. It holds data from a single source and focuses on storing real-time streaming data. It’s generally used for continuous read-and-write operations and falls under the category of OLTP systems.
A data warehouse, however, provides functionalities, like storing high-volume data along with performing various analytics and reporting services. In addition to real-time data, warehouses can hold data from multiple sources, including historical data and are an example of OLAP systems.
Data Warehouses vs Data Lakes
Data lakes can store all kinds of data, including structured, semi-structured, and unstructured data, in its raw format from different sources, like social media, mobile apps, and IoT devices. This data can be used for ML, deep learning, and business intelligence (BI). Their schemas are designed at the time of implementation and are preferred by data professionals due to their flexibility to store all kinds of data.
Data warehouses can only store structured data from multiple sources in the same format. Warehouses can be used for ML and BI tasks, and their schemas are usually designed prior to implementation. Data scientists and business analysts usually prefer this kind of data because it saves time since the data is already normalized and ready to use. RudderStack is a preferred platform for data warehouse management because it helps you reduce the effort it takes to create and manage your data warehouse.
ML and Analytics for Data Warehouses
Data is at the heart of ML. ML algorithms use math and statistics to analyze data and make required predictions, so warehouses that store large amounts of data from various sources are preferred.
Data warehouse’s data is directly fed to algorithms, like classification, regression, market basket analysis, customer lifetime value analysis, and demand forecasting and recommendation engines, to make necessary predictions. They can hold both labeled and unlabeled data that enables both supervised and unsupervised ML algorithms to use that data.
Data analysts can also use different tools to access data from a data warehouse, including Python, Tableau, Microsoft Power BI, and Apache Spark. These resources can help with data visualization and predictive analytics.
As ML becomes more mainstream, data will become increasingly democratized. Services will be able to be administered by teams with modest data management technical skills without having to worry about infrastructure limits or the data’s source.
On-Premise vs Cloud Warehouse
Data warehouses can be developed on-premise or can be used with online services. On-premise warehouses are used within an organization’s network, while cloud-based warehouses can be used anywhere.
Determining which you should use depends on your company’s needs and the cost, speed, scalability, and security of the warehouse.
Expensive hardware devices are needed in advance while setting up the on-premise data warehouses. Since entire system is handled in-house, extra resources such as database architects, system managers are required to securely manage the server. This is not it Upkeeping to the hardware such as renewals and updates, all of them have to be managed by you which is often costly.
Cloud data warehouses on the other hand, give you relief from the upfront hardware cost. You need to pay only for utilized resources. Cloud warehouses also monitor your usage statistics and suggest you the amount of required resources which directly helps you to save your hardware resources cost.
On-premise data warehouses require up-front costs for setting up and scaling and because warehouses are used for storing historical data that increases over time, you need to scale your storage capacity accordingly. If you regularly have to scale up and down, these costs can become inhibitive.
Cloud-based warehouse services are appealing because they don’t require any up-front cost and can be scaled up and down using different tier subscriptions. In addition, you don’t have to work on administration or updates and maintenance of the storage system, which saves you additional time and money.
On-premise warehouses are used within a network and therefore, the speed can be much faster for teams working at the same location.
Cloud warehouses add some latency to the processing, making them slower than on-premise warehouses. However, as working from home becomes more common, cloud warehouses are gaining popularity.
An organization’s data increases over time, so the size of the warehouses needs to be adjusted accordingly. Scaling on-premise data warehouses increases costs, and additional memory devices are needed.
Cloud data warehouses can be scaled up and down with a simple change in tier subscription.
On-premise data warehouses are considered to be the most secure when they are bound to a certain level of security protocols.
Cloud warehouses are less secure than on-premise warehouses since data is stored in the cloud. However, different cloud service providers are now adopting security measures, like access to registered users and authenticators, to help keep data secure.
Data is increasing at a rapid pace, and technology must evolve to handle its accumulation. Traditional databases can store data but aren’t able to provide the analytics and insights that businesses need to make accurate and perceptive decisions.
Data warehousing is a vital tool for organizations ready to put their data to good use in ML applications, future planning, and trend forecasting.