Scuba Tech Library

What is an Enterprise Data Warehouse?

As companies become more data-driven, a central repository to collect disparate data for analysis is crucial to gaining true insights that can then be put into action. 

An enterprise data warehouse (EDW) is a database or collection of databases, either on-premise or via the cloud, that centralizes data from across the business so that it can be accessed, analyzed, and used in decision-making. They serve many functions, from developing marketing strategies to improving customer relations to driving business growth and optimizing business operations.

Although they’re often used interchangeably, it’s important to note that there is a difference between a data warehouse and an EDW. An EDW is designed to encompass the entirety of an organization’s data, whereas a data warehouse is more simplistic–housing data from one specific component of the business or a product category, for example. An EDW should also not be confused with a data lake. Data lakes are similarly a repository but differ in that it stores raw, unprocessed data–whereas an EDW stores structured data, making the data in an EDW easier to query, but less malleable compared to a data lake.  

Benefits of an EDW

An EDW helps an enterprise achieve true business intelligence. Some of the benefits of an EDW include: 

  • Scalability, handling trillions of data points: An EDW is designed to scale with the size of the business.
  • Unifying and centralizing data in one place: By collecting data from diverse sources all across the business and storing them in one repository, an EDW is designed to eliminate siloed data and bottlenecks. This gives decision-makers a clear, true picture of their business and takes a holistic approach to form strategies and take action.
  • Faster insights: An EDW makes data accessible for all stakeholders and can aggregate that data automatically, bringing quicker insights. 
  • Accessibility: An EDW stores structured data, which is better for analytics purposes than raw data. By eliminating the noise of raw data, it’s easier for those with less technical know-how to run queries and find what they’re looking for without the need for a data scientist.

Drawbacks of an EDW

There are also drawbacks to EDWs, including: 

  • Rigidity: Because the data stored in an EDW is structured, it is easier to read, but as a consequence, it is also less malleable than the data found in other repositories, such as a data lake. Data in an EDW is purpose-built, and this limitation means making changes to the data can be prohibitively difficult.
  • Cost: Maintaining an EDW is costly and resource-intensive for an IT department.
  • Managing Big Data: Transforming large volumes of data through an EDW can be expensive and time-consuming, creating bottlenecks that may prevent getting fast insights into data. Moving data transformation over to frameworks specifically designed for processing Big Data, such as Adobe Hadoop in combination with an ETL/ELT tool, can free up an EDW’s resources. 

Components of an EDW

Most EDWs consist of a combination of the following parts:

  • Data sources: Data from across an enterprise is collected. This is a diverse mix of data types from all systems (such as data from transaction databases and marketing tools).
  • Integrated data tools: An EDW can include a range of integrated components such as ETL tools and APIs. 
  • Staging area: This is where data is organized and prepared to be suitable for analytics purposes. 
  • Final access area: Here, the processed data is accessible for analytics use. 

How Scuba Analytics can simplify an EDW

An EDW can help brands make data analytics accessible to all stakeholders in an organization. From customer relations to business operations, using data to inform decisions is key to running a successful business. 

Scuba Analytics takes the complexity out of running a data warehouse or other data repositories. Learn more about how Scuba helps decision-makers gain valuable insights into all facets of their business.

Data Science

Data Lakes and Data Warehouses -- Which Is Right For You?

Data lakes and data warehouses are both commonly used for storing data, but there are key differences between the two that make them unique in their own way. Learn which fits your business purposes best and if there is a better solution.

Data Science

What is a Data Ecosystem?

The term “data ecosystem” collectively refers to all the programming languages, algorithms, applications, and the general infrastructure used to collect, analyze and store data.

Data Science

What is Data Modeling

Data modeling is a means of creating a conceptual framework for your data in preparation for storage in a data warehouse. The resulting model is a visual representation of the data which maps out the relationships between data, and the rules.

Data Science

What is Lambda Architecture?

Lambda architecture processes data through a hybrid combination of batch processing and stream processing.

Data Science

What is an ETL Pipeline?

ETL is a method to collect raw data from various sources, clean it up, and translate it so it can be used to inform decision making.

Data Science

What is Data Governance?

Data governance allows organizations to ensure high-quality data through formalized processes for management, monitoring, and control of data assets.

Data Science

What is Hadoop?

Apache Hadoop is one of the most widely used open source frameworks designed to address the problem of storing and processing big data.

Data Science

What is Parquet?

Unlike row-based formats such as CSV, Parquet is a columnar data file storage format.

Data Science

What is Querying?

A query is a question or request for a database written in a code the database can understand, in order to retrieve or modify the correct information.

Data Science

What is an Ad Hoc Query?

An ad hoc query is any kind of question you can ask a data system off the top of your head.

Data Science

What is Metadata Storage?

When building a database, all data requires some description to help identify its uniqueness, which is where metadata comes in.

Data Science

What is a Data Platform?

Data platforms are tools that allow businesses to collect, analyze, and present data.

Data Science

What is Time-Series Data?

Time-series data analysis serves critical functions in most modern industries, and is a powerful method to glean accurate analysis.

Data Science

What is Data Sovereignty?

Data sovereignty defines the regulations data is subject to. Fortunately, there are actionable steps brands can take to ensure compliance.

Data Science

What is Self-Service Analytics?

Self-service analytics empower non-technical teams to interact with data, perform queries, and glean helpful business insights.

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