Do you wish to understand what an Enterprise Data Warehouse (EDW) is? Do you want to understand its various possible architectures and types? If yes, then you’ve come to the right place.
This article will provide you with an in-depth understanding of what an Enterprise Data Warehouse is, its architecture, various types, along with the best Data Warehousing solutions available, allowing you to make the best choice for your business.
Understanding Enterprise Data Warehouses (EDW) and its Importance
- An Enterprise Data Warehouse (EDW) can be defined as a Database or a collection of Databases used to centralize an enterprise’s historical business data. These data sources could be the Databases of various Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, and other forms of Online Transactional Processing (OLTP) systems.
- Enterprise Data Warehouse (EDW) is the most preferred form of data storage today due to its ability to scale storage requirements up or down as per the business and data requirements. This means that an Enterprise Data Warehouse (EDW) is capable of providing unlimited storage to any enterprise.
- Enterprise Data Warehouses (EDW) are required simply because businesses today rely on data-driven decision-making to plan their business strategies. For any analysis to be performed successfully, the data from all data sources must be loaded into the Data Warehouse in a form suitable for analysis. A Business Intelligence tool of choice can then be connected to this Data Warehouse to perform the required analysis.
For example, the world’s most popular streaming platform, Netflix, has approximately 93 million active users per month. The data pipeline of Netflix captures more than 500 billion user events per day. This includes data on various things such as video viewing activities, error logs, performance reports, etc. The storage of this data requires approximately a storage space of 1.3 Petabytes (1 Petabyte = 1,000,000 Gigabytes) per day. The advantages of having such high volumes of data are as follows:
- It allows Netflix to plan their future releases by analyzing the kind of content viewers like.
- It allows Netflix to understand how they can make the user experience on their website and Android/iOS applications better by analyzing user behavior on these services.
Advantages of Using Enterprise Data Warehouses (EDW)
The advantages of using an Enterprise Data Warehouse are as follows:
- Data Standardization: Operational data of any enterprise is stored across various Online Transactional Processing (OLTP) systems and Databases. Each system or Database will store the data in a different format. For any useful analysis to be performed, the data first needs to be standardized such that all the data is in the same format. Data from these Databases is loaded into the Data Warehouse using an ETL (Extract, Transform, Load) pipeline. This pipeline can be used to convert the data into a standard format and store it in the Data Warehouse. This makes the data easier to analyze and improves the understanding of the data in the Data Warehouse.
- Flexibility: As an enterprise grows, the volume of data it’ll store will increase rapidly. Along with the increase in volume, there might be situations where the data model has to be updated to accommodate various other attributes that have to be recorded. Enterprise Data Warehouses are known for their flexibility and can easily accommodate any change in requirements such as the volume of data being stored or change in the data model without a complete overhaul of the system.
- Connectivity to Business Intelligence Tools: Most modern Enterprise Data Warehouses allows easy connectivity to a Business Intelligence tool of choice. This seamless connectivity enables enterprises to easily establish and keep track of various Key Performance Indicators (KPIs) that can be used to measure how well a business is achieving its key objectives.
Understanding Enterprise Data Warehouse (EDW) Architecture
The various possible architectures an Enterprise Data Warehouse can incorporate are as follows:
1) One-tier Architecture
This is considered to be one of the most primitive forms of Enterprise Data Warehouse architectures. In this architecture, the Reporting Tools are connected directly to the Data Warehouse. Although this architecture is easy to set up and implement, it causes various issues for large datasets. Most organizations today have hundreds of Gigabytes of data. This means that to perform any query, the Reporting Tool would have to go through all that data which is a time taking process. Going through the large dataset for each query would result in low performance.
Hence, the One-tier Enterprise Data Warehouse Architecture is only suitable for organizations with small datasets.
2) Two-tier Architecture
The Two-tier Architecture implements a Data Mart Layer between the Reporting Layer and the Enterprise Data Warehouse. Data Marts can be seen as smaller Databases that contain domain-specific information, which is only a part of the data stored in the Data Warehouse. All information stored in the Data Warehouse is split into various Data Marts based on the domain of information.
The Reporting Tools are then connected to this Data Mart Layer. Because a single Data Mart consists of only a small part of the data in the Data Warehouse, performing queries on it would require much less time than it would on a Data Warehouse. A Two-tier Enterprise Data Warehouse is considered to be more suitable for real-life scenarios.
3) Three-tier Architecture
A Three-tier Architecture further implements an OLAP Layer between the Data Mart Layer and the Reporting Layer. The OLAP Layer consists of OLAP Cubes which are used to store data in a multidimensional form allowing faster analysis to be performed on the data. A sample OLAP Cube can be seen below.
OLAP Cubes allow various operations to be performed on it, which results in quality analysis. These operations are as follows:
I. Roll-up
This can be defined as the process of reducing the attributes being measured by either performing aggregations or moving up the hierarchy (performing grouping based on a specific order). A sample Roll-up operation is as follows:
II. Drill-down
This can be defined as the process of increasing the number of attributes being measured to perform a more in-depth analysis by moving down the hierarchy. Drill-down is considered to be the opposite of the Roll-up operation. A sample Drill-down operation is as follows:
III. Slice
This can be defined as the process of removing a dimension by specifying a filter on the dimension to be removed. A sample Slice operation is as follows:
IV. Dice
This can be defined as the process of specifying filters for two or more dimensions resulting in the formation of a Sub-Cube. A sample Dice operation is as follows:
Hence, it can be seen that the implementation of an OLAP Layer in the Three-tier Enterprise Data Warehouse (EDW) Architecture allows more in-depth analysis to be performed on the data and also ensure high performance.
Types of Enterprise Data Warehouses (EDW)
The two types of Enterprise Data Warehouses are as follows:
1) On-premise Data Warehouse
Usage of an On-premise Data Warehouse means that the organization is responsible for purchasing, setting up, and maintaining the software and hardware required to manage the Data Warehouse.
The advantages of using an On-premise Data Warehouse are as follows:
- Control: The organization chooses the kind of hardware and software that will be used to set up the Data Warehouse.
- Speed: The Data Warehouse is On-premise and will hence have little to no network latency.
- Security: Only someone who is connected to the organization’s network can access the Data Warehouse.
The disadvantage of using an On-premise Data Warehouse are as follows:
- High Cost of Investment: The Data Warehouse would require high investment to purchase and set up the initial software and hardware. Even then, as the volume of data with the organization increases, more hardware will have to be purchased to accommodate that data.
- Inability to Scale Resources: On-premise Data Warehouses cannot scale resources up or down as per the requirements due to limited hardware.
2) Cloud-based Data Warehouse
Cloud-based Data Warehousing is a service provided by an organization that various other organizations can use to fulfill their Data Warehousing needs. This means that the organization does not have to set up its own On-premise hardware and software but only pay for the Cloud Data Warehousing Solution.
The advantages of using Cloud-based Data Warehouse are as follows:
- Low Cost of Investment: Organizations do not have to invest large amounts of money to purchase and set up hardware and software requirements of the Data Warehouse.
- Scalability: Storage and Compute capabilities of your Data Warehouse can scale up or down as per your business and data requirements automatically and hence, you only have to pay for what you use.
The disadvantages of using Cloud-based Data Warehouses are as follows:
- Security: Since the Cloud-based Data Warehouses store data of many organizations that can be accessed over the Internet, businesses feel some level of concern over the storage of confidential data.
- Accessibility: Cloud-based Data Warehouses cannot be accessed in-case there is no internet access.
- Latency: Depending on where the Data Warehouse is located, businesses might notice some network latency while accessing their data.
Best Enterprise Data Warehousing (EDW) Solutions
Some of the best known Data Warehousing Solutions that are used by most businesses today are as follows:
1) Amazon Redshift
Amazon Redshift is one of the most well-known Cloud-based Data Warehousing Solutions and is part of Amazon Web Services (AWS). Amazon Redshift allows users to query large volumes of Structured and Semi-structured Data and uses AWS infrastructure to provide a quality experience.
Amazon Redshift Pricing
Amazon Redshift offers its first-time users a two-month free trial, following which they have to start paying based on a pricing plan that can be found on official document.
2) Snowflake
Snowflake is a Cloud-based Data Warehousing Solution that provides various functionalities that other Data Warehouses don’t. It follows a pay-per-second model, which means that the pricing is completely based on the usage. Another functionality provided by Snowflake is that it allows users to scale up or down the Storage and Compute Nodes independently as per requirements instead of the Integrated Bundle. This results in comparatively cheaper storage as compared to other Data Warehouses.
Snowflake Pricing
Snowflake offers its first-time users a 30-day free trial, following which they have to start paying based on a pricing plan that can be found .
3) Google BigQuery
Google BigQuery is a Cloud-based Data Warehousing Solution and a part of Google Cloud Platform (GCP). It implements a serverless technology that uses columnar storage and a Tree Architecture model that allows faster execution of queries and aggregations. Google BigQuery was built to analyze billions of records using SQL queries.
Google BigQuery Pricing
Google offers its first-time users a 90-day free trial, following which they have to start paying based on a pricing plan that can be found on official document.
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Conclusion
This article provided you with an in-depth understanding of what an Enterprise Data Warehouse is, why it’s important and what are the various architectures that are used to implement an Enterprise Data Warehouse. It also helped you understand the different types of Enterprise Data Warehouses, along with the best-known solutions used by most enterprises today. You can have a look at 8 Best Data Warehousing Tools.
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Manik is a passionate data enthusiast with extensive experience in data engineering and infrastructure. He excels in writing highly technical content, drawing from his background in data science and big data. Manik's problem-solving skills and analytical thinking drive him to create impactful content for data professionals, helping them navigate their day-to-day challenges. He holds a Bachelor's degree in Computers and Communication, with a minor in Big Data, from Manipal Institute of Technology.
