Data Warehouse Optimization Mistakes to Avoid

Data warehouse optimization, although hard to achieve, is the goal of every progressive organization.  This is primarily because a Business Intelligence (BI) system working with a struggling data warehouse is similar to a building with a weak foundation; both stand useless without a strong base. Since a BI system relies heavily on a stable groundwork, it is vital to have a strong enterprise data warehouse architecture.

Why Optimizing Data Warehouse is Critical for Your Enterprise?

Many enterprises today are constantly building upon their legacy data warehouse systems; adding layer after layer so that the DW model can cater to their ever-changing needs. This results in an intricate data infrastructure, where digging through the layers of the data warehouse architecture becomes a labor-intensive task. Adding to this complexity is the volume and variety of data, which has increased manifold from the time that these systems were designed.

So, what happened in the past 15 or years so that the state-of-the-art systems of those times have begun to collapse? Businesses have evolved and so have their data architecture. As a result, the data warehouse team is under a heap of requests which it is unable to fulfill in a timely manner. The ever-growing data complexity and the endless user needs are difficult to predict for modeling data warehouse infrastructure. Now, there are often demands of adding disparate data to the existing architecture and with everything so strongly bonded, it gets more and more complicated every day.

Replacing these legacy systems and build new data warehouses after testing and prototyping is not an easy option either. Enterprises are now looking for ways to optimize data warehouses as an alternative – often resorting to inaccurate solutions.

Common Data Warehouse Optimization Mistakes

Here, we have put together common data warehouse optimization mistakes that lead companies to use overly priced solutions and still fail to fulfill their BI needs.

1.    Heading towards the Cloud

The IDC‘s prediction of data swelling up to a total of 163 ZB (zettabytes) by the year 2025 has forced enterprises to rethink their data warehousing strategy. Among the emerging alternatives is shifting the data warehouse to the cloud because of its ability to solve many data volume and data computational issues effectively.

But moving a humongous amount of data to the cloud is not that easy. You will need to move the database schema and data along with performing the strenuous ETL functions on the cloud. Especially for data warehouse optimization purposes, there is often a need for architectural modernization, restructuring database schema, and sometimes, rebuilding the data pipelines. These factors further complicate the overall process.

Moreover, even in monetary terms, there aren’t many benefits. Companies who choose to shift their data warehouses to the cloud generally end up spending amounts which are much higher than what they had originally anticipated.

2.    Relying on a Data Warehouse Appliance

Nowadays, many companies have begun to revert to data warehouse appliances as a solution to their data warehousing modeling problems. A data warehouse appliance is a set of hardware and software solutions that enterprises use to accommodate huge amounts of data, often measuring in terabytes. Netezza and DATAllegro are some of the most common data warehouse appliances.

Even though data warehouse appliances will increase querying performance speed against millions of records, it can only achieve this through extensive hardware and memory configurations. So, even after the data undergoes lengthy migrations, the primary data warehouse architectural problems are the same. This is largely because a data warehouse appliance does not analyze the core, underlying issues with the schema, and structure of the data warehouse. Instead, it uses heavy-duty hardware for Massively Parallel Processing (MPP) architectures to enhance speed and platform scalability. Companies, even after heavily investing in these appliances, are unable to produce the desired results.

3.    Looking towards Big Data Solutions

Big data solutions, like Hadoop, for data warehouse optimization have failed to deliver on the performance and analytics required for a BI system. This is because these systems are not built to act as data warehouses. Architects and scientists, who tried to carry out such feats, have not seen much success due to several reasons, such as:

  • The data structures and database schemas are not user-friendly
  • Data security is compromised  
  • Integration of outside data sources is tough
  • Incorrect query results

While it’s true that systems like Hadoop are affordable in some cases, running an entire data warehouse is not amongst them. Writing complex queries and executing them is, in fact, expensive. So, even though the idea might entice you, do keep in mind there is no guarantee for complex architectures, powered by big data solutions, will work perfectly for your data warehousing processes.

4.    Adopting the Continuous ETL Tuning Method to Boost Performance

It’s true that analytics experts have used the snowflake and star schemas to get better visibility across the data warehouse, but at some levels, especially for disparate data, they don’t work well. At times, these schemas are unable to give the depth that is actually required. This is because they restrict the results of analytics, leading to users to work with bad data. This enforces the DW architects to go back to the basics once again, which doesn’t seem that attractive an idea.

Along with this, we have the famous ETL to look at too. It is a complex process that needs to be fine-tuned based on changing business requirements. However, this process is good for the overall feel and maintenance of the data warehouse. Conversely, expecting these rigorous cycles to help in data warehouse optimization by boosting the overall performance is simply asking too much of them.

Since analytics is a process and not a one-time IT project, database tuning becomes a routine task. The reason for this is the massive surge in data volume, leading to an increase in complexity, all of which needs to be considered. For this to happen, companies need to scale their platforms’ performance. Expecting positive results just on the basis of ETL or through refined data warehouse modeling won’t bear fruit because it will be unable to cope with the increased complexity of the enterprise data.

Solutions for Successful Data Warehouse Optimization

With so many suggested methodologies trying to optimize the failure of legacy data warehouses, alternative approaches like data warehouse automation and data virtualization might work better.

Data Warehouse Automation

Data warehouse automation (DWA) is an effective approach for streamlining traditional data warehousing processes. As a next-gen technology, it relies on advanced approaches to automating the planning, modeling, and integration steps of the data warehousing lifecycle.

DWA solutions have evolved over the decades from hand-coding to a fully automated system. The main reason for this continuous growth is the rapid increase in data volumes and changing integration requirements. It uses a code-free approach to aggregate both structured and unstructured data and then moving the transformed source data to the data warehouse.

Data warehouse automation offers several benefits over the other data warehouse optimization methods in the market, including:

  • Avoid manual ETL mistakes, faster query processing, and improved time-to-insights.
  • Move data to other platforms like the cloud or data visualization tools at an unprecedented pace.
  • Faster data warehouse testing, prototyping, and deployment
  • Near real-time access to the most recent data gives users the ability to respond promptly to the changing market demands.
  • Reduce the manual work required in developing various data processes, improving outcome and saving developer resources; thereby reducing the overall costs.
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Data Virtualization

In order to improve data integration processes involved in preparing data for data warehousing, data virtualization tools have been gaining traction for their ability to speed up the data-to-insights journey.

Adding a layer of the data virtualization tool to the process provides complete abstraction from the complexities of source data and presents it as a database table comprising of all the data. So, no matter how many data sources you are calling data from, the data virtualization tool converts any structured or unstructured data into an easy, readable format.

This abstract layer greatly simplifies the basic data warehousing processes, like ETL and ELT. Moreover, it provides the data in a ready-to-use state for BI and analytics, reporting, and application development. It also makes data accessible through various front-end applications, like portals and dashboards.

Data virtualization gives the added benefit of data security since the front-end users are no more required to get into the technicalities related to the source data. So, organizations can restrict data access to the related personnel only.

Some of the benefits of using data virtualization tools are:

  • It enables data to be collected from different sources and integrated at one place.
  • It reduces the downtime needed to collect data for analyzing the success/failure of different products.
  • If used properly, data virtualization can access data from both relational and non-relational databases (like NoSQL). This feature enables enterprises to create composite results from such sources that otherwise is not possible in a relational data warehouse.
  • At times, when the enterprise data warehouse is down, data virtualization’s merged sources can be used for analytics and reporting.

Data virtualization tools aid in enhanced data warehousing performance by:

  • Integrating data from other data sources (even Hadoop) which minimizes the need to load data into the warehouse prior to analysis. This reduces the time for executing new BI requests.
  • Decrease programming and hardware/software costs of data integration and loading by alleviating the data duplication costs, limiting the network bandwidth usage and increasing execution speeds by making use of in-memory caches.

Conclusion

Data warehouse optimization is vital for ensuring trusted data is available for analytics and decision making. With the ever-increasing data size and complexity, applying every other optimization technique may go drastically wrong if applied incorrectly. Solutions like data warehouse automation and data virtualization, when used in combination, can work wonders for optimizing the performance of your data warehouse. However, you should be careful and evaluate these solutions based on your specific data warehousing environment before implementation.

Need help in optimizing your data warehouse for catering to your growing data needs? Our expert data architects can help you out!