Businesses are becoming more data-driven and want to make better business decisions. Data from enterprise customers can provide insights into customer spending patterns, revenue, and behavior. Modern data analysis and Business Intelligence (BI), involves the integration of data from different sources and harnessing it for analysis or BI, often with the help of an enterprise Data Warehouse (EDW).
What is an Enterprise Data Warehouse?
An EDW is a central repository for data from multiple sources. It collects data from multiple sources and makes it accessible for analysis, BI, and data-driven decision-making. All users can have access to the data, provided they have the appropriate privileges.
EDWs include current data such as the most recent feeds, or snapshots from the source system, as well historical data. EDWs provide the best single source of truth for enterprise data because they store all final, non-redundant business information in a single place. EDWs are also the storage platform that underlies live analytical processes.
Enterprise Data Warehouse vs Business Intelligence
An Enterprise Data Warehouse is an organized and central location from which users can access business information. An enterprise uses business intelligence to analyze, summarize, analyze and ultimately extract value from its business data.
It can be difficult to connect data from different sources and within business units or SaaS platforms, as well as to share insights that are based on all data, across organizations. As a result, data analysis and BI suffer. EDWs solve the problem of data silos by making all enterprise data available in a central repository that is accessible and easily accessible for analysis throughout the enterprise.
What are the Benefits of Enterprise Data Warehouse
The greatest utility of an EDW is its centralization of enterprise information, increased availability of the data to users in different business units, and improvements in the organization, structure, and automation of data storage, processing, and storage.
- EDWs improve an organization’s ability to deliver insights faster and give businesses a competitive edge.
- Data can be replicated by business units within an organization from different sources to the repository for analysis or BI. This eliminates communication barriers and allows users to access the information faster.
- To create an EDW that is more suited to analytics, data modeling was necessary.
Cloud Enterprise Data Warehouses
EDWs were once on-premises systems that had a fixed processing and storage capacity. They could not scale quickly or easily in high-demand situations. Cloud EDWs are a popular option for companies looking to replace legacy data warehouse systems. Cloud EDWs offer several benefits:
Scalability and speed: Cloud EDWs provide speed and scalability in a way that legacy system simply cannot. Cloud platforms can scale quickly to meet almost any processing demand. Administrators can quickly scale storage and processing resources with just a few mouse clicks.
Low maintenance: EDW-as-a-service model reduces the amount of upkeep that is required for an on-premises setup. The EDW-as-a-service model eliminates the hassle of setting up and maintaining expensive IT resources and hardware in the home. The service provider upgrades or replaces system hardware.
Cost savings: Cloud infrastructure is available on a subscription-based, pay-as-you-go basis. Software updates are included in your subscription.
Security: EDWs offer data security with built-in data encryption and protection against accidental or malicious loss. They can also adapt quickly to new security threats and deploy countermeasures. Cloud EDWs address a range of compliance standards such as SOC 1 & SOC 2, PCI DSS level 1, and HIPAA.
Availability: Many cloud EDWs have high availability and span many availability zones. The disruption is not noticed by users if a data center goes down. Instead, they shift work to another available datacenter.
Let’s take a look at the most well-known cloud enterprise data warehousing.
4 Best Cloud Enterprise Data Warehouse
Amazon Redshift is a cloud data warehouse that has been in existence since 2013. It boasts the longest history of any cloud warehouse and the largest number of deployments. It leverages column-oriented storage to provide fast data access and processing, just like all cloud data warehouses.
Redshift is highly adaptable, allowing customers to provision clusters of nodes as their computing and storage need change. Each node is equipped with its own CPU, RAM, and storage space. This allows for massive parallel processing (MPP), which is essential for big data applications, particularly the data warehouse.
Redshift uses familiar SQL syntax that is based on PostgreSQL. Redshift can integrate with a variety of services, including many BI tools, platforms, and platforms such as Amazon QuickSight and platforms like IBM Cognos and Periscope Data.
Snowflake allows customers to scale storage and computing resources independently, unlike Redshift. This makes Snowflake suitable and affordable for many use cases.
Customers who want to build a warehouse that holds large amounts of data but slow processing can concentrate on increasing storage space. Organizations that want to implement sophisticated transformations and processing in their warehouse can concentrate on increasing computational power.
Snowflake’s storage layer is automatically managed and can hold structured or semi-structured data such as nested JSON objects. Each cluster can access all data, but each one works independently to allow automatic scaling, distribution, and rebalancing.
Azure SQL Data Warehouse
Azure SQL Data Warehouse (EDW) is Microsoft’s EDW platform. It uses the distributed MPP architecture, just like other cloud storage and computing platforms. It combines data from various databases, including Azure SQL Database, Microsoft’s relational cloud database, and SaaS platforms to create a powerful, centrally managed repository.
Azure is part of Microsoft’s cloud computing ecosystem and provides connectors to a wide variety of third-party software and systems. Customers can use Microsoft SQL Server, a cloud SQL engine, to get support, or use other options that they are already familiar with.
Microsoft offers a wider variety of pricing options and management levels than other cloud EDW providers. Customers who use Azure to store cloud data eventually turn to Azure SQL Data Warehouse for data analytics.
Google BigQuery allows for interactive analysis of large amounts of data. It offers all the functionality required to create a modern EDW and can be used with technologies such as MapReduce and Google Cloud Storage.
BigQuery is unique because it uses a serverless architecture. Because computational and storage provisioning happens continuously and dynamically, users cannot see the details of resource allocation.
BigQuery offers transparent pricing plans that scale with user requirements, billing per query so customers only pay for what they use.BigQuery can be used by users who need the most ad-hoc system. It is less flexible than other cloud EDWs but allows for more flexibility and management.
ETL and Enterprise Data Warehouses
The extract, transform and load (ETL), the process is a critical part of a working EDW. ETL consolidates data and converts it into a consistent, useful, and modeled format for the EDW.
To avoid taxing scarce analytical resources, older on-premises centralized data warehouses perform transformations in the data pipeline. EDWs that are cloud-based use an alternative to this process, ELT (extract load, transform), as cloud warehouse platforms have the ability to perform required transformations after replica.