The Guide to No-Code Cloud Data Warehousing

“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 relatively low number of users 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.

Data Automation

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 Transformation

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.