Data Warehouse Best Practices: 9 Factors to Consider in 2024

Data warehousing is the process of collating data from multiple sources in an organization and storing it in one place for further analysis, reporting, and business decision-making. Typically, organizations will have a transactional database that contains information on all day-to-day activities. Organizations will also have other data sources, such as third-party or internal operations. Data from all these sources are collated and stored in a data warehouse through an ELT or ETL process. The data model of the warehouse is designed so that it is possible to combine data from all these sources and make business decisions based on them. 

In this blog, we will discuss the nine most important factors and data warehouse best practices to consider when building your first data warehouse.

1. Data Warehouse Best Practices: Impact of Data Sources

The kind of data sources and their format determine a lot of decisions in a data warehouse architecture. Some of the best practices related to source data while implementing a data warehousing solution are as follows.

• Detailed discovery of data sources, data types, and their formats should be undertaken before the warehouse architecture design phase. This will help in avoiding surprises while developing the extract and transformation logic.
• Data sources will also be a factor in choosing the ETL framework. Irrespective of whether the ETL framework is custom-built or bought from a third party, the extent of its ability to interface with the data sources will determine the success of the implementation.

2. Data Warehouse Best Practices: The Choice of Data Warehouse

One of the primary questions to be answered while designing a data warehouse system is whether to use a cloud-based data warehouse or build and maintain an on-premise system. There are multiple alternatives for data warehouses that can be used as a service based on a pay-as-you-use model. Likewise, there are many open sources and paid data warehouse systems that organizations can deploy on their infrastructure. 

On-Premise Data Warehouse

An on-premise data warehouse means the customer deploys one of the available data warehouse systems – either open-source or paid systems on his/her own infrastructure. 

There are advantages and disadvantages to such a strategy.

Advantages of using an on-premise setup

• The biggest advantage here is that you have complete control of your data. In an enterprise with strict data security policies, an on-premise system is the best choice.
• The data is close to where it will be used and latency of getting the data from cloud services or the hassle of logging to a cloud system can be annoying at times. Cloud services with multiple regions support to solve this problem to an extent, but nothing beats the flexibility of having all your systems in the internal network.
• An on-premise data warehouse may offer easier interfaces to data sources if most of your data sources are inside the internal network and the organization uses very little third-party cloud data.

Disadvantages of using an on-premise setup

• Building and maintaining an on-premise system requires significant effort on the development front. 
• Scaling can be a pain because even if you require higher capacity only for a small amount of time, the infrastructure cost of new hardware has to be borne by the company.
• Scaling down at zero cost is not an option in an on-premise setup.

Cloud Data Warehouse

In a cloud-based data warehouse service, the customer does not need to worry about deploying and maintaining a data warehouse at all. The data warehouse is built and maintained by the provider, and all the functionalities required to operate it are provided as web APIs. Examples of such services are AWS Redshift, Microsoft Azure SQL Data Warehouse, Google BigQuery, Snowflake, etc.

Advantages of using a cloud data warehouse

• Scaling in a cloud data warehouse is very easy. The provider manages the scaling seamlessly, and the customer only has to pay for the actual storage and processing capacity that he uses. 
• Scaling down is also easy, and the moment instances are stopped, billing will stop for those instances, providing great flexibility for organizations with budget constraints.
• The customer is spared of all activities related to building, updating, and maintaining a highly available and reliable data warehouse.

Disadvantages of using a cloud data warehouse

• The biggest downside is that the organization’s data will be located inside the service provider’s infrastructure, leading to data security concerns for high-security industries.
• There can be latency issues since the data is not present in the organization’s internal network. To an extent, this is mitigated by the multi-region support offered by cloud services, which ensures that data is stored in preferred geographical regions. 

The decision to choose whether an on-premise data warehouse or cloud-based service is best taken upfront. For organizations with high processing volumes throughout the day, it may be worthwhile considering an on-premise system since the obvious advantages of seamless scaling up and down may not be applicable to them. 

3. Data Warehouse Best Practices: ETL vs ELT

The movement of data from different sources to the data warehouse and the related transformation is done through an extract-transform-load or an extract-load-transform workflow. Choosing ETL vs ELT is an important decision in data warehouse design. In an ETL flow, the data is transformed before loading, and the expectation is that no further transformation is needed for reporting and analysis. ETL was traditionally the de facto standard until cloud-based database services with high-speed processing capabilities came in. This meant the data warehouse did not completely transform data, and data could be transformed later when the need arises. This method of data warehousing has the advantages below.

• The transformation logic need not be known while designing the data flow structure.
• Only the data that is required needs to be transformed, as opposed to the ETL flow where all data is transformed before being loaded to the data warehouse.
• ELT is a better way to handle unstructured data since what to do with the data is not usually known beforehand in case of unstructured data.

As a best practice, the decision of whether to use ETL or ELT needs to be done before the data warehouse is selected. An ELT system needs a data warehouse with a very high processing ability. 

4. Data Warehouse Best Practices: Architecture Consideration

Designing a high-performance data warehouse architecture is a tough job and there are so many factors that need to be considered. Some of the best practices are given below.

• Deciding the data model as easily as possible – Ideally, the data model should be decided during the design phase itself. The first ETL job should be written only after finalizing this.
• At this day and age, it is better to use architectures that are based on massively parallel processing. Using a single instance-based data warehousing system will prove difficult to scale. Even if the use case currently does not need massive processing abilities, it makes sense to do this since you could end up stuck in a non-scalable system in the future.
• If the use case includes a real-time component, it is better to use the industry-standard lambda architecture where there is a separate real-time layer augmented by a batch layer.
• ELT is preferred when compared to ETL in modern architectures unless there is a complete understanding of the complete ETL job specification and there is no possibility of new kinds of data coming into the system. 

Build a Source Agnostic Integration Layer

The primary purpose of the integration layers is to extract information from multiple sources. By building a Source Agnostic integration layer you can ensure better business reporting. So, unless the company has a personalized application developed with a business-aligned data model on the back end, opting for a third-party source to align defeats the purpose. Integration needs to align with the business model.

5. Data Warehouse Best Practices: ETL Tool Considerations

Once the choice of data warehouse and the ETL vs ELT decision is made, the next big decision is about the ETL tool, which will actually execute the data mapping jobs. An ETL tool takes care of the execution and scheduling of all the mapping jobs. The business and transformation logic can be specified either in terms of SQL or custom domain-specific languages designed as part of the tool. The alternatives available for ETL tools are as follows.

• Completely custom-built tools – This means the organization exploits open-source frameworks and languages to implement a custom ETL framework that will execute jobs according to the configuration and business logic provided. This is an expensive option, but the advantage is that the tool can be built to have the best interfacing ability with internal data sources.
• Completely managed ETL services – Data warehouse providers like AWS and Microsoft offer ETL tools as well as a service. An example is the AWS glue or AWS data pipeline. Such services relieve the customer of the design, development, and maintenance activities and allow them to focus only on the business logic. A limitation is that these tools may have limited abilities to interface with internal data sources that are custom or not commonly used.
• Fully Managed Data Integration Platform like Hevo: Hevo Data’s code-free platform can help you move from 100s of different data sources into any warehouse in mins. Hevo automatically takes care of handling everything from Schema changes to data flow errors, making data integration a zero-maintenance affair for users.

6. Data Warehouse Best Practices: Identify Why You Need a Data Warehouse

Organizations usually fail to implement a Data Lake because they haven’t established a clear business use case for it. Organizations that begin by identifying a business problem for their data can stay focused on finding a solution. Here are a few primary reasons why you might need a Data Warehouse:

• Improving Decision Making: Generally, organizations make decisions without analyzing and obtaining the complete picture from their data, unlike successful businesses that develop data-driven strategies and plans. Data Warehousing improves the efficiency and speed of data access, allowing business leaders to make data-driven strategies and have a clear edge over the competition.
• Standardizing Your Data: Data Warehouses store data in a standard format, making it easier for business leaders to analyze it and extract actionable insights from it. Standardizing the data collated from various disparate sources reduces the risk of errors and improves overall accuracy.
• Reducing Costs: Data Warehouses let decision-makers dive deeper into historical data and ascertain the success of past initiatives. They can take a look at how they need to change their approach to minimize costs, drive growth, and increase operational efficiencies.

7. Data Warehouse Best Practices: Define a Change Data Capture (CDC) Policy for Real-Time Data

By defining the CDC policy, you can capture any changes that are made in a database and ensure that these changes are replicated in the data warehouse. The changes are captured, tracked, and stored in relational tables known as change tables. These change tables provide a view of historical data that has been changed over time. CDC is a highly effective mechanism for minimizing the impact on the source when loading new data into your Data Warehouse. It also does away with the need for bulk load updating along with inconvenient batch windows. You can also use CDC to populate real-time analytics dashboards and optimize your data migrations.

8. Data Warehouse Best Practices: Consider Adopting an Agile Data Warehouse Methodology

Data Warehouses don’t have to be monolithic, huge, multi-quarter/yearly efforts anymore. With proper planning aligned to a single integration layer, Data Warehouse projects can be dissected into smaller and faster deliverable pieces that return value that much more quickly. By adopting an agile data warehouse methodology, you can prioritize the data warehouse as the business changes.

9. Data Warehouse Best Practices: Use Tools instead of Building Custom ETL Solutions

With the recent developments in Data Analysis, there are enough 3rd party SaaS tools (hosted solutions) for a very small fee that can effectively replace the need for coding and eliminate a lot of future headaches. For instance, loading and extracting tools are so good these days that you can pick up litter for free, all the way to tens of thousands of dollars a month. You can quite easily find a solution that is tailored to your budget constraints, support expectations, and performance needs. However, there are various legitimate fears in choosing the right tool since there are so many SaaS solutions with clever marketing teams behind them.

Conclusion

The above sections detail the best practices in terms of the three most important factors that affect the success of a warehousing process – The data sources, the ETL tool and the actual data warehouse that will be used. This includes data warehouse considerations, ETL considerations, change data capture, adoption of an agile methodology, etc.

Extracting complex data from a diverse set of data sources to carry out an insightful analysis can be challenging, and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases or SaaS applications into your Data Warehouse. Hevo is fully automated and, hence, does not require you to code.

Want to take Hevo for a spin? SIGN UP and experience the feature-rich Hevo suite firsthand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
Are there any other factors that you want us to touch upon? Let us know in the comments!

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