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Data warehousing can be defined as the process of managing the collection and storage of data from various sources to provide valuable business insights. It can also be referred to as electronic storage where businesses store large amounts of data and information. It is a critical component of a business intelligence system that includes techniques for data analysis.
Business Intelligence Tools And Data Warehouse
Data warehousing is a mix of technology and components that enable the strategic use of data. It is an organization’s electronic collection of significant amounts of information intended for investigation and analysis rather than for the processing of transactions. Data warehousing is a method of translating data into information and making it accessible to customers in a timely manner.
Data Warehousing And Business Intelligence
Data analysis is used to provide in-depth information about an organization’s performance by comparing integrated data from various heterogeneous data sources. A data warehouse performs queries and analyzes on historical data obtained from transactional sources.
The concept of data warehousing was developed in the 1980s to help evaluate data stored in non-relational database systems. It is designed to enable businesses to use their archived data to help achieve a corporate advantage. The vast amount of data in data centers comes from a variety of places, such as communications, sales, finance, customer-oriented applications, and external partner networks.
Any data put into the warehouse is immutable and cannot be modified because the data warehouse focuses on changes in data over time to analyze past events. Data warehousing should be done in such a way that the stored data is secure, reliable, easily retrieved and managed.
The huge return on investment for businesses that have successfully introduced a data warehouse shows the tremendous competitive edge the technology brings. Competitive advantage is achieved by enabling decision makers to access data that may reveal previously unavailable and untapped information regarding customers, needs and trends.
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Data warehousing increases the efficiency of business decision makers by providing an interconnected archive of consistent, unbiased, and historical data. Data warehousing helps integrate data from disparate structures into a single form that provides a clear view of the enterprise. By translating data into usable information, data warehousing helps market managers conduct more practical, accurate and reliable analyses.
Data warehousing keeps all data in one place and doesn’t require much IT support. There is little need for information from outside the industry, which is expensive and difficult to integrate.
Often, we fail to estimate the time required to retrieve, clean and upload data to the warehouse. Although some resources exist to reduce the time and effort spent on the process, it may take up a large portion of the overall production time.
Hidden problems associated with the source networks that supply the data warehouse may be discovered after years of going undetected. For example, when entering new property information, certain fields may accept overrides, which may result in individuals entering incomplete property data even if it is available and relevant.
We’ve Only Scratched The Surface Of The Full Potential For The Data Warehouse
Data warehousing also manages similar data formats across different sources of data. This may lead to loss of some valuable pieces of data.
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The Kimball Group Reader: Relentlessly Practical Tools For Data Warehousing And Business Intelligence: 9780470563106: Computer Science Books @ Amazon.com
Organizations have been using data warehouse and business intelligence (DWBI) workloads to support business decision making for many years. These workloads are brought to the Amazon Web Services () platform to take advantage of the cloud. However, these workloads are built using multiple vendor tools and technologies, and the customer faces the burden of administrative overhead.
This post provides architectural guidance for consolidating multiple DWBI technologies into managed services to help reduce administrative overhead and bring operational ease and business efficiency. Two scenarios are explored:
Organizations are involved in managing multiple DWBI technologies due to acquisitions, mergers, and the lift and shift of workloads. These workloads use extract, transform, and load (ETL) tools to read and process relational data from upstream transactional databases and store it in a data warehouse. After that, these workloads use business intelligence tools to generate valuable insight and present it to users in the form of reports and dashboards.
These DWBI technologies are usually installed and maintained on their own server. Figure 1 shows that this increases the administrative overhead of the organization and creates challenges in maintaining the knowledge of the team as a whole.
Pdf] Data, Text And Web Mining For Business Intelligence: A Survey
Each of these functions can be efficiently performed using a service. For example, you can use Glue for ETL, Amazon Redshift for data warehouse, and Amazon QuickSight for business intelligence.
With the mentioned services, organizations can consolidate their DWBI technology usage. Organizations can also quickly adapt to these services because their engineering team can more easily use their DWBI knowledge in these services. For example, SQL knowledge is used in Glue jobs using SprakSQL, Amazon Redshift queries, and Amazon QuickSight dashboards.
Figure 2 shows the architecture of Figure 1 redesigned with services. In this architecture, ETL functions are consolidated in Glue. A glue crawler is used to automatically catalog source and target table metadata; Then, these catalogs are used by Glue ETL jobs to read data from the source and write it to the target (data warehouse). Glue jobs apply the necessary transformations (such as join, filter, and sum) before writing them. Additionally, a glue trigger is used to schedule job executions. Alternatively, managed workflows for Apache Airflow can be used to schedule jobs.
Similarly, the data warehousing function is integrated with Amazon Redshift. Amazon uses Redshift to store and organize rich data and implement appropriate data access control for workloads and users.
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Finally, Amazon is consolidating business intelligence functions with QuickSight. It was used to create required dashboards sourcing data from AmazonRedshift and apply complex business logic to produce charts and graphs required for business insights. It is also used to enforce necessary access controls to dashboards and data.
In the case where the source databases are in an on-premises datacenter, the overall solution is similar to Scenario 1, with an additional step to continuously move data from the on-premises database to an Amazon Simple Storage Service (Amazon S3) bucket. Data movement can be efficiently managed by Database Migration Service (DMS).
To access the source database DMS, a connection needs to be established between the cloud and the on-premise network. Based on performance and throughput needs, the organization can choose a Direct Connect service or a site-to-site VPN service to move data securely. For the purpose of this discussion, we consider direct connect.
In Figure 3, the DMS task is used to full-load to change data capture to continuously move data to an S3 bucket. In this case, glue is used to catalog and read data from the S3 bucket. The rest of the dataflow is the same as mentioned in Scenario 1.
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Additionally, updated architectures provide the necessary security with access control, data encryption at rest and in transit, monitoring and auditing.
Additionally, Amazon Redshift and Amazon QuickSite provide their own authentication and access controls. So, a user can be a local user or federated person. With the help of these authentication, the environment can control access to the data in Amazon redphifte and access to the dashboard in the Amazon Quick Cruic.
In this blog post, we
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