What does a data warehouse mean?
A data warehouse is a data management system that supports business intelligence activities, particularly analytics. Data warehouses are merely designed to implement queries and analysis and contain huge volumes of historical data. The data within a data warehouse typically comes from a variety of sources, including application log files and transaction applications.
How a data warehouse benefits a business?
Effective data mining is essential for any modern business’ competitive campaign, and a data warehouse is a major component of this data mining.
The capability to rapidly research early trends and have precise data is crucial to great decision-making. By supporting this historical overview, a data warehouse enables decision-makers to learn from past trends and difficulties. Essentially, a data warehouse ensures constant improvement.
Besides, a data warehouse can scale with a company. A developing company requires more and better data, and data warehouses can grow increasingly to handle queries.
What’s more, a data warehouse can enable smarter, metric-based decisions on everything from product launches to inventory levels to key sales levels.
- More details on Data warehouse benefits
Data warehouse tutorial: 6 simple steps
It’s obvious that a data warehouse is of utmost importance. To build a proper data warehouse, let’s start with data warehouse basics.
Step 1: Determine why you need a data warehouse
Businesses often fail to perform a Data Lake as they haven’t built a clear business use case for it. Businesses that start by discovering an issue for their data can concentrate on finding a solution. Here are some main reasons for a data warehouse:
Standardizing your data
Data warehouses save data in a standard format that allows companies to analyze it and collect actionable insights. Standardizing the data collated from different sources decreases the risk of errors and enhances the overall accuracy.
Lowering costs
Data Warehouses allow decision-makers to delve deeper into historical data and determine the success of earlier initiatives. They can identify how they need to change their method to reduce costs, drive development and boost operational effectiveness.
Enhance decision-making
Businesses usually make decisions without analyzing and getting the comprehensive picture from their data, as opposed to successful companies that grow data-driven strategies and plans. Data Warehousing increases the efficacy and speed of data access, enabling business leaders to draw data-driven strategies and have a clear advantage over the competition.
Businesses often fail to perform a Data Lake as they haven’t built a clear business use case for it.
Step 2: Choose between a cloud data warehouse and an on-premises data warehouse
One of the most important questions to be resolved while establishing a data warehouse system is whether to employ a cloud data warehouse or construct an on-premises system.
Various alternatives for data warehouses can be utilized as a service, depending on a pay-as-you-use model. Correspondingly, there are multiple open sources and paid data warehouse systems businesses can use on their infrastructure.
Cloud data warehouse
With a cloud-based data warehouse service, customers don’t need to worry about building a data warehouse. The data warehouse is designed and maintained by the provider, and all the functionalities to run the data warehouse are offered as web APIs.
On-premises data warehouse
An on-premises data warehouse will let customers deploy one of the available data warehouse systems – either open-source or paid systems on their infrastructure.
Step 3: Select data sources
Type of data sources and their format decide many decisions in a data warehouse architecture. Here are some helpful practices to source data while performing a data warehousing solution:
- It’s crucial to discover data sources, data types, and formats before the warehouse architecture design phase. This will aid in preventing surprises while growing the extract and transformation logic.
- Data sources will also play a role in selecting the ETL framework. Regardless of whether the ETL framework is custom-built or purchased from a third party, its interfacing ability with the data sources will decide the efficiency of the implementation.
It’s crucial to discover data sources, data types, and formats before the warehouse architecture design phase.
Step 4: Choose ETL vs. ELT
The migration of data from various sources to data warehouses and the related movement is completed via an extract-load-transform or an extract-transform-load workflow. Whether to pick ETL or ELT is a vital step in the data warehouse design.
In an ETL flow, the data is moved before loading, and the expectation is that no additional movement is needed for reporting and analyzing.
ETL has been the practical standard traditionally until the cloud-based database services with high-speed processing ability appeared. In other words, the data warehouse need not have completely converted data, and data could be transformed later if needed. This way of data warehousing brings some advantages, including:
- The transformation logic need not be identified while building the data flow structure.
- Only the required data needs to be converted, contrary to the ELT flow where all data is transformed before being moved to the data warehouse.
ELT is a more fantastic way to handle unstructured data because what to do with the data is not typically known beforehand in the case of unstructured data.
The choice of whether to use ETL or ELT needs to be implemented before the data warehouse is chosen. An ELT system requires a data warehouse with a high processing ability.
- Know about Data Warehouse Costs
Step 5: Choose ELT tools rather than ETL
Data warehouses often involve the extract, load, transform (ELT) or the extract, transform, load (ETL) data integration method.
ETL and ELT are two popular ways of gathering data from different sources and storing it in a data warehouse.
The primary advantage of ELT over ETL is the ease and flexibility of storing new, unstructured data. With ELT, you can store all kinds of information, covering unstructured information.
Step 6: Build an agile approach rather than a big bang approach
Depending on the complexity, it can take several months to years to construct a modern data warehouse. The company cannot recognize any value from its investment during the implementation.
The requirements also increase over time and sometimes differ greatly from the first set of requirements. This is why a Big Bang approach to data warehousing has a higher possibility of failure since businesses delay the projects. Moreover, the Big Bang approach cannot be personalized to a specific vertical, company, or industry.
With an agile approach, you enable the data warehouse to grow with the business requirements and concentrate on existing business issues. This model is a repetitive process in which modern data warehouses are developed in various sprints while involving the business user during the process for constant feedback.
Building the data warehouse from scratch is a daunting start for beginners, so it’s best to have advice from experience data scientist and data analyst.
Synodus provides Data warehouse (DWH) services, including advisory, implementation, support, migration, BI reporting components and managed services to help companies benefit from a high-performing DWH.
Conclusion
The article has just lays out data warehouse fundamentals to build a proper data warehouse for your business. That will be the foundation for the success of any BI or analytics program.
A data warehouse will offer necessary information for your data-driven decisions, which helps your business make the right call on everything from new product development to inventory levels. If you have any excellent data warehouse practices to share, please drop a line in the comment section.
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