Data Science

Challenges of maintaining a traditional data warehouse

traditional data warehouse

One of the most common issues that business analysts face is the lack of access to reliable and timely information about business processes. This information is vital for decision-making yet it is blocked or hindered due to many challenges of a traditional data warehouse (TDW).

The traditional data warehouse consists of on-premise IT resources usually servers & system software and uses ETL (Extract, Transform, Load) to migrate data from source to destination. However, due to its rigid structure and complex architecture, a traditional data warehouse is often considered to be unsuitable for meeting modern data requirements. On the other hand, cloud data warehouses are a lot faster, consist of unified data sources, and more efficient due to the use of optimized data clusters.

In this blog post, we look at the challenges of maintaining and operating a traditional data warehouse and how cloud-based data warehouses can help.

Challenges of a Traditional Data Warehouse (TDW)

Let’s look at challenges of a traditional data warehouse and see how they can lead to higher costs and hampered productivity. Here are the five most common challenges of working with a traditional data warehouse:

High costs and failure rates

TDWs are well-known for their high failure rates, which is over 50%, according to Tim Mitchell data architect. This is not just because of the complex architecture or technical challenges, but because these projects often fail to meet user requirements and needs.

The same challenges exist when a data warehouse needs to be updated or changed to meet new reporting requirements or data needs. What is more is that when such projects don’t fail, they have very high costs and timelines which makes a TDW inadequate for meeting rapid and real-time data requirements.

Rigid, inflexible architecture

In this modern age, businesses need to be more adaptable and agile than ever before and this requires an IT architecture that can be changed quickly on-demand. However, a TDW is not flexible and can become a bottleneck in meeting business requirements today. To illustrate, with a TDW, a simple change to a data model can take weeks or months to complete and the entire process can cost thousands of dollars to your business as well.

High complexity and redundancy

Due to the inflexible structure of TDWs, most organizations purchase hardware add-ons and tools to facilitate their data needs more quickly. This leads to a complex yet redundant architecture with several data silos, each of which needs to be regularly updated and maintained.

Not just this, but having so many isolated data stores can lead to inconsistent and unreliable reporting. Further, it also leads to data accuracy, integration, and validation issues. The entire architecture can also lead to multiple versions of truths since each data silo is responsible for its own reporting.

Slow and degrading performance

The volume of data that businesses need to store, process, and analyze has grown exponentially over the last decade. Such great volumes can affect a traditional data warehouse’s performance leading to slow performance and significant delays in reporting. This can be caused by a number of reasons but the most common are inefficient and redundant methods for ETL and legacy system infrastructure.

However, while TDWs are becoming slower, user requirements are increasing and there is an ongoing need for real-time reporting which makes performance an uphill challenge for IT teams.

Outdated technologies

We talked about outdated hardware being a cause for slow performance in TDWs but it is actually an entirely separate challenge on its own. Technological advancements are made every day, but It is likely that the traditional data warehouse in use at your business was set up years ago which means that you are already quite behind.

Other than performance issues, outdated technologies and hardware can cause the following issues in traditional data warehouses:

Scalability issues: You can not always scale up vertically to meet your requirements. Due to the way TDWs are designed, things such as parallel processing and in-memory storage are not used, which are proven to significantly improve data processing capabilities.

Storage issues: There is a limit to how much data you can store in and with businesses now dealing with petabytes of data, it is near impossible to scale your on-premise storage capabilities enough without incurring exceedingly high costs.

An Alternative Approach: TDW vs Cloud DW

The modern alternative to a TDW is a cloud data warehouse that provides you with the ability to process and analyze huge volumes of data quickly and efficiently. What the cloud DW does best is that it serves as a single repository for all your information that you can quickly integrate with and connect to your SaaS tools, OLTP databases, and BI tools.

Here are the benefits of a cloud data warehouse over a TDW:

  • Highly scalable: Everything on the cloud is scalable from processing power to storage capacity. You can even auto-scale your infrastructure to scale up during peak demand and scale down during low demand to minimize costs.
  • Reduced cost: With a cloud DW, there are no physical servers involved and the complexity of configuring the environment is often much easier so you save up on both the infrastructure costs as well as the cost of maintenance and administration. 
  • Built-in data processing ecosystem: Most cloud DWs can easily integrate with and connect to other cloud services and tools such as a BI tool or data analytics tool. You get the benefit of parallel processing and ready-made tools that can significantly increase the time required to meet business requirements.

Data Warehousing Automation Tools Complement Modern Cloud Data Warehousing Approach 

Want to overcome the challenges of your traditional data warehouse or move to a cloud data warehousing platform? Data warehouse automation (DWA) tools like Astera DW Builder can help. Generally, these tools are packed with automation features and run on a metadata-driven architecture, allowing you to build on-premise and cloud data warehouses quickly.

When you choose DWA tools, you get  a wide range of features and functionalities that facilitate the process of building, deploying, maintaining,  and updating your data warehouse. These include:

  • An easy-to-use drag-and-drop interface for designing and building your data warehouse pipelines. The simplified process allows you to cut down on the resources required for configuring and maintaining your data warehouse.
  • A high-performance ETL/ELT engine that makes use of parallel processing and other performance optimization techniques to accelerate your data retrieval,  making the entire process more efficient.
  • Serve as an end-to-end unified platform that replaces tools like data modelers, ETL/ELT generators, data quality management software, metadata management solutions, and others, to lower TCO and increase developer productivity.
  • Rapid deployment to cloud data warehouses such as Snowflake, Microsoft Azure, and Amazon Redshift.

As compared to traditional data warehousing solutions, the metadata-driven DWA tools provide you with the best-in-class technology and features for  building and managing data warehouses, allowing you to dramatically speed up the entire process.

Leave a Comment

Your email address will not be published.

You may also like

Pin It on Pinterest