What is data warehousing?
Data warehousing is the process of electronically storing and integrating data from multiple sources into a common format using a data warehouse.
Fun fact: The term “data warehouse” was coined in the 1980s. Its “recognized father”, American computer scientist Bill Hinman, defined it as a subject-oriented, nonvolatile, integrated, time-variant collection of data in support of management’s decisions.
Driven by advances in cloud computing and no-code technologies, the concept has evolved a great deal since then. Today, upon a simple online search, you’ll find a miriad of elaborate definitions and use cases for it within the enterprise. However, with the rise of citizen developers, it is important that the entire organization has the same understanding of how a data warehouse benefits every role, technical or non-technical.
In a nutshell, a data warehouse is a massive repository of integrated data drawn from one or multiple sources. It provides the ability to store both current and historical data for long periods of time, and is used in reporting, analytics, and similar operations. The data storage is structured in a way that the various departments and teams in the organization can access and use the data most suited to their needs.
To quickly drive a common understanding of the concept across the enterprise, here’s a simplified representation of a data warehouse with upstream and downstream sources, tools, processes, and operations.
“Data warehouse” as a term is often confused with “database,” “data lake,” or “data mart.” Let’s look at how they differ.
Data warehouse vs. database
A database is a collection of data made available for use by end users and other systems. Simple examples of databases can be an Excel spreadsheet or an address book. An important characteristic of any database is that the data it holds is structured; however, the data is captured ‘as-is’ from a single source, and unaltered.
A data warehouse on the other hand is a system that aggregates data from such databases, as well as other sources of information. It then centralizes and normalizes the data in a way that makes sense for various business needs, so as to serve as a single source of truth for the entire organization.
The card below summarizes the key differences between a data warehouse and a database.
Data warehouse vs. data lake
A data lake is a repository that stores both structured and unstructured data at any scale. The main advantage of this architecture is that the data can be used in its “natural format” – i.e., without having to be structured first – for the purposes of processing, analytics, and visualizations.
A variant of a data lake is a data swamp – a “damaged” data lake that either is inaccessible to its potential audience or cannot provide any valuable information. In other words, a data swamp is a data lake “gone bad.” The line between data lakes and data swamps can be a thin one, especially since there’s a who can realize the full benefits of data lakes.
As opposed to a data lake, a date warehouse contains information that is transformed, structured, and assigned a specific purpose. Again, in contrast to a data lake that typically needs an expert to make its data useful, a warehouse is either semi- or fully automated. This means that the information stored in a data warehouse is easier to access by and readily available to non-technical business users for reporting, analyses, and other operations.
The card below summarizes the key differences between a data warehouse and a data lake.
Data warehouse vs. data mart
A data mart is a database that stores a specific type of information to be used by a particular business function (e.g., sales, finance, or HR data). So it’s limited in its application.
In contrast, a data warehouse contains a much larger amount and a greater variety of data with various applications. In fact, despite often being confused with a data warehouse, a data mart is often a subset of a warehouse.
The card below summarizes the key differences between a data warehouse and a data mart.
Why you need data warehousing
With the rise of big data, data warehouses have become indispensable in managing high volume, velocity, variety data operations.
For a small company, a database may seem enough. But as the company experiences growth and starts to accumulate terabytes of data pouring in at high speeds from various sources, a database soon becomes overmatched. Even having multiple databases or data marts can lead to process inefficiencies and lost opportunities because of:
- Siloed, disparate sources of information often difficult to integrate
- Challenges in meeting compliance with industry regulations regarding data
- Manual, time-consuming and error-prone processes for managing data
- Duplicate, inconsistent, incomplete or redundant data records
- Inability to leverage historical data and innacurate business analyses
A data warehouse, on the other hand, eliminates these challenges. Once implemented and adopted, it can benefit the entire organization by ensuring:
- A single source of truth for all data due to its ability to centralize data gathered from various sources
- Data integrity, consistency, and accuracy due to its ability to automate data loads and transformations
- Accurate business predictions due to its ability to store huge amounts of historical data and use it in business analyses
- Rapid compliance and efficient data governance due its automatic data controls, validations, and auditing capabilities
- Efficiency of data processes and operations due to its ability to ensure an automated flow of information throughout the organization.
All these benefits eventually lead to better decision making and sustained growth.
Key Functions of a Data Warehouse
To yield the benefits mentioned above and more, it is crucial that the data warehouse allow for connections with multiple data sources, providing real-time, and on-demand synchronization, and offer source-specific checks and filtering with built-in Extract-Transform-Load (ETL) features.
It should be capable of performing the following functions:
Data integration, extraction and loading
A combination of data warehouses, data lakes, and third-party enterprise applications form the ecosystem of typical sources of data, each having their own proprietary connection and data extraction characteristics. So it is important that the data warehouse have data connectors that facilitate data ingestion from various sources. These include multi-format text flat-files, Excel files, and XML files, which are popular formats of extracted datasets from data providers. In addition to files, a data warehouse should also come equipped with connectors to third-party applications and a comprehensive suite of web services (API-ready) for bi-directional data integration.
To ensure that the information remains correctly synchronized between the data warehouse and third-party systems, it’s important to have the ability to define data processes with sequential and dependent orders and schedule them to run on a recurring basis. A data warehouse should provide its users the ability to control and maintain process scheduling for data imports, exports, calculations, and other operations.
Data ingestion is only one piece of the puzzle. Preparing the data for use often involves data validation, cleansing, transformation, along with statistical and analytic processing and augmentation. So a data warehouse should allow users to perform not only basic data management operations, such as aggregating or deleting records, merging fields or fields from multiple tables, and performing calculations, but also enable them to perform analytical functions on historical records.
Tip: A data warehouse should be able to load, store, and use any data from any time period for reporting, BI/analytics, and analytic modeling. In fact, this is one of the main reasons why enterprises employ a data warehouse – to be able to access and analyze historical data to ensure accurate decision making.
A data warehouse should ensure proper data governance and compliance. A built-in automated governance framework that provides real-time alerts whenever changes are made, auditor-ready comprehensive reports, and detailed logs of the various operations conducted within the warehouse is key in this process. Such a framework helps users to control and continuously audit access, use, and changes to users, roles, applications, data, processes, and system objects.
Cloud data warehousing vs. on-premise
Today, the advantages of using cloud computing are clear:
- Reduced maintenance costs for IT systems
- Flexible work practices
- Scalable business operations and storage
- Protect data and systems from natural disasters or other crisis
- Increase productivity by ensuring data is accessible at all times
As companies build their businesses in the cloud, cloud data warehouses are quickly replacing on-premise ones.
Even though on-premise data warehouses have their merits, they incur huge upfront costs with installing and configuring the necessary infrastructure. They also require companies to set aside budgets for annual maintenance and support. And they’re more rigid and difficult to scale.
Cloud data warehouses are quite the opposite – they don’t require huge upfront investments and they eliminate the need for system maintenance, which allows organizations to invest their time and resources in other areas of potential business growth. Moreover, they offer increased flexibility, reliability, and data security.
So why cloud data warehousing?
- Low costs
- Fast deployment
- Fast processing
- Data security
Simple. The future is no code. Here are some quick facts:
- ResearchandMarkets.com projected the global low-code development platform market size to grow from USD 4.32 Billion in 2017 to USD 27.23 Billion by 2022, at a 44.49% CAGR; according to their newly released report, it is expected to grow further at a CAGR of 27.9% between 2020 and 2025.
- Gartner predicts that by 2023, over 50% of medium to large enterprises will have adopted a low-code application development platform as one of their strategic application platforms.
- As Forrester found, low-code software development makes it possible to develop cloud-native application 10-times faster (with 70% fewer resources).
- According to a recent Gartner survey on citizen development, 41% of respondents already have citizen development initiatives in place and 20% of those that don’t are either evaluating or planning on employing such initiatives.
The simplicity of no-code technology empowers everyone to build automated solutions and to solve problems without worrying about technology proficiency.
No-code is not only a movement for business users that are building or defining a solution; even a savvy IT developer will appreciate the speed of “point-and-click” setup compared to writing code. The menu-driven setup process, that is a characteristic of no-code application development, should be incorporated into other aspects, such as data warehousing, processing, modeling, and workflows.
No-code cloud warehousing enables quick deployment, empowering citizen developers to assist more in automating the business needs, without writing a single line of code. It also enables the enterprise to attain the desired scalability without investing a great deal of time or energy and reducing costly system maintenance.
So why no-code data warehousing?
- easily scale data storage and processing
- leverage the flexibility of a “plug-and-play” architecture
- eliminate the need of SQL knowledge
- lower IT costs and need for expensive resources
- speed up change and digital transformation