An enterprise data warehouse enables faster and better decision-making throughout your firm than if you accessed different data stores directly.
Improved data quality means more trust.
A warehouse’s data has been cleansed, de-duplicated, and standardized.
This is true whether you utilize the conventional ETL pipeline, which transforms data before loading it into your warehouse, or the newer ELT approach, which transforms data in the warehouse as needed by a specific consumer. A consistent “single source of truth” increases confidence in the insights and judgments gained from any analysis.
Superior and quicker analysis
Data from several sources, including operational databases, transactional systems, and flat files, are combined and synchronized in a warehouse. This provides a more comprehensive picture of your company and enables you to use BI techniques like data mining, augmented analytics, and machine learning to see trends that data silos could easily overlook.
Additionally, timely access to reliable, complete data allows you to convert knowledge into insight quickly.
Building Design & Key Ideas
Your organization’s unique requirements will decide your data warehouse’s architecture. Data warehouse automation lets you use visual tools to quickly design, deploy, and manage your whole warehouse lifecycle without writing any code.
The Architecture of the Data Warehouse Cloud
Today’s data warehouses are frequently hosted in the cloud.
The same advantages that the cloud provides in other areas of your professional life, such as lower costs, increased computing capacity, and greater flexibility, apply to data repositories. Cloud designs provide the capability of traditional warehouses while also incorporating the flexibility of big data platforms and the elasticity of the cloud (so you can scale your capacity up or down as needed). Furthermore, tools such as Azure Synapse Analytics, Amazon Redshift, Google BigQuery, and Snowflake are a fraction of the cost of traditional on-premises systems, which typically need a substantial upfront investment and a lengthy deployment procedure.
An agile online data warehouse brings three essential productivity drivers:
- A straightforward solution supports real-time data intake and changes.
- A model-driven automated procedure to continuously improve your warehousing operations.
- A large-scale data catalog for securely sharing your data marts.
Data Mart, Database, and Data Lake versus Data Warehouse
It is not appropriate to use the terms “data warehouse,” “data mart,” “database,” and “data lake” synonymously. Here, we outline the main variations between each.
Data mart vs. Data Warehouse
A data mart is a collection of warehouse data related to a particular subject or division of your company, like finance or sales. Historically, data marts aided analysts and business managers perform analysis faster because they worked with a smaller dataset. They are added between the warehouse and the analytics tools, as seen below.
Database vs. Data Warehouse
A database is typically the major yet limited data source for a certain application (unlike warehouses which contain massive data volume for all applications). Another significant distinction is that databases are designed for conducting fast queries and completing transactions, whereas warehouses are ideally suited for BI and analytics. Databases outperform traditional warehouses in terms of keeping real-time data up to date, but new cloud data warehouses can also manage real-time data.
| FACTOR | DATA WAREHOUSE | DATABASE |
| Type of Data | Summarized historical (in traditional DW’s) | Detailed real-time |
| Use Case | Analyzing large, complex datasets | Recording transactions |
Data warehouse vs. Data Lake
A data lake stores all of your organization’s data, both structured and unstructured. A data lake is thus analogous to a warehouse without the established schemas. As a result, it now supports a broader range of analytics. Many businesses employ both systems to meet a variety of storage requirements.
| FACTOR | DATA WAREHOUSE | DATA LAKE |
| Type of Data | Typically structured data which has been transformed. | Raw, unstructured data. |
| Use Case | Business users analyzing large, complex datasets (data pre-structured to answer pre-determined questions). | Data scientists and engineers exploring raw data to uncover new business insights. |
| Analysis | Data visualization, BI, data analytics. | Predictive analytics, machine learning, data visualization, BI, big data analytics. |
Vizio Helps You Create DataOps for Analytics
Every analytics environment, from Qlik to Tableau, Power BI, and beyond, receives real-time, analytics-ready, and actionable data through modern data connectivity.
- Real-Time Data Streaming (CDC) is a simple, real-time, and global solution that extends enterprise data into live streams to enable modern analytics and microservices.
- Automation for Agile Data Warehouse helps you design, develop, deploy, and administer quickly and without manual coding purpose-built cloud data warehouses.
- Create a Managed Data Lake to automate complex data and transformation processes to create data lakes that are constantly updated and ready for analytics.
Talk to our experts at Vizio for more information about our products, pricing, implementation, or how best to address your data integration and analytics needs. We’d love to help.