Data Warehouse ETL: How to reinvent your data warehouse ETL

Data Warehouse ETL: How to reinvent your data warehouse ETL

The data warehouse has been a cornerstone of operations for many companies. ETL processes are mostly tightly managed by data warehousing experts who handle data as if it were more valuable than gold. Because this process has worked so well, many companies stick with it indefinitely and don't consider modernizing their efforts to use up-to-date tools. However, there are some ways you can take your data warehouse ETL to the next level.

What are the Three Steps in ETL Processing?

ETL refers to the methods companies use to prepare data from its current form and prepare it for use by other programs and systems. ETL processing has three steps typically done in this order - extract, transform and load. Here's an overview of each step in the table below.

Ways to reinvent your data warehouse ETL

1. Find The Right Sales Leads

The process of finding suitable sales leads is all about quantity. The goal is to drive as many high-quality leads as possible. With that in mind, you'll want to run several marketing campaigns or put together a trade show booth so you can get in front of as many people as possible. Ideally, the cost for these marketing campaigns will be far outweighed by the return on investment (ROI) you see from the prospects who respond.

2. Use the Right Toolset.

Free trials and free versions of products are a great way to explore how the product can benefit you. Even if it's a version that doesn't work with your operating system, they will usually have the old version on their website. It allows you to spend time with the software without committing funds or resources. If you find out you dislike the software after an initial 3-month trial, contact customer service for an unconditional refund.

3. Try New Techniques

It may be a good idea to have a one-off project in which you design an optimized set of transformations. In this case, try new techniques you've never used before - both in the design and deployment phases. For example, use sampling or custom logic to aggregate data instead of over-aggregating it. Don't just do what everyone else does because what worked for them might not work for you. Don't let your old ideas limit you!

4. Review And Revise Success Metrics

At this point, you will have developed the blueprint for your DW/BI solution. Now it's time to determine success metrics for the solution. Choosing success metrics is a process that considers all aspects of the resolution, including end-user productivity and company processes and requirements. Metrics should be measurable, prioritized, and achievable.

5. Provide Estimated Timelines and Costs

As with any business strategy, the key is thoroughly examining your current needs and whether or not a change would be beneficial. Additionally, looking at other warehouses in the industry to see what they're doing might be a great idea. With that said, here are the things you should take into consideration when deciding on an ETL change: 
What is my budget? Is it possible for me to outsource some of the work? How much staff time will I need?

6. Use Workload Management to Improve ETL Runtimes

Workload management can help you improve the performance of your ETL runtimes. As runtime needs to change throughout the day, it helps avoid under-utilization and idle resources in the data warehouse by adjusting resource allocation minute-by-minutely.

7. Perform Table Maintenance Regularly

In setting up an ETL, you need to create tables that can hold all the data that the Extract and Load operations will pull from source systems. The critical thing to remember is that table maintenance is just as important as table creation. The sooner you see any anomalies or issues, the easier it is to fix them before they get out of hand.

What are the tips for improving data warehouse ETL quality

A data warehouse is a potent tool for businesses of all sizes. Still, it won't be nearly as valuable for your company if the quality of your warehouse data is flawed. A poorly built or maintained data warehouse can profoundly impact your business, from affecting customer satisfaction to decreasing revenue and increasing costs. Hence, you must keep improving your data warehouse quality to reap the rewards. Here are tips to help you improve your data warehouse quality.

1. Use Data Validation Techniques

Data validation techniques can help detect invalid data, so it is never used to generate misleading statistics. If a process for validating data does not exist, you should write one and implement it as soon as possible. You can do this by assigning each field a range of values that correspond to its meaning and checking the data input from users against those ranges.

2. Ensure Data is Timely and Up-to-Date

Your data warehouse will be a powerful resource for running your business if it contains up-to-date and high-quality data. Here are five steps you can take to make sure you have the best possible data:  Know your sources – Update the processes that create new data, and ask the responsible people to provide documentation on what happens to their records. This way, you'll know which systems need your help in changing or cleaning up their papers and how often those changes need to happen.

3. Conduct Regular Audits of the Data

Data is only valid when it is correct. Data quality in a data warehouse determines the accuracy of reports and actions taken. It is critical to audit for errors and other issues, and fixing them before reporting on them can save you time and money and prevent confusion.

4. Establish a Data Quality Improvement Plan

To improve data quality, it is necessary first to establish a data quality improvement plan. This plan should contain the methods you will use to measure the data's accuracy and other benchmarks. Determine where your organization has data areas that may be difficult to clean up and work on. Once these issues are addressed, move on to address any other data problems uncovered in your organization.

5. Implement a Data Quality Management System

To maximize the quality of your data warehouse, you will need to implement a system for managing data quality. There are many different types of these systems that you can use, but they usually fall into three categories: manual, automated, and embedded. Of these, the robotic approach is by far the most popular and can significantly reduce the time and cost associated with checking for data integrity issues in your data.

What are the Responsibilities of an ETL Developer?

Business intelligence solutions depend on the quality of the data that goes into them, meaning it's important to work with good data management practices (such as business intelligence development best practices). ETL developers are the professionals who make sure that happens. The below chart shows the responsibilities ETL developers do in their day-to-day jobs.

Conclusion

You may find that following these guidelines I outlined above will make managing your data warehouse much easier. It's worth remembering, though, that all of these methods are there to support you and not the other way around. Like with any undertaking, mistakes will be made along the way, which doesn't mean it's not worth the time or effort. Remember that this is an exploration; you will learn something new daily by expanding on what is already out there. Guru solutions are expert in data warehouse ETL services.