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Big Data Journal: Article

Best Practices for Integrating Different Big Data Sources

Data organization eliminates potential future problems

Choosing when to adopt a data warehouse largely depends on how easily and effectively your organization can manage multiple data sources. When you do decide to combine all data sources into one central location, the decisions become more uniform. You can, of course, approach the integration of all data sources into a data warehouse in your own way, but if you’re not careful, you could create more problems than you solve.

To extract your data and load it into the new data warehouse, there are some basic must-follow rules that help avoid problems down the road. This process is often abbreviated to ETL, or Extract, Transform, Load. Let’s take a look at the steps and examine the best practices for each.

Extraction
There are quite a few things that could go wrong during the extraction process. This is when you’ll copy all the data from every data source in your company, including proprietary databases, files you’ve uploaded during your several years in business, APIs, and even all of your files within any cloud-based storage services you may use.

This may not sound too hard, but there are a few mistakes many make right from the beginning. The most common is copying all data every time they sync with the data warehouse. Consider the data sources you’ll be integrating into the new data warehouse. Do you really have the time or space to copy and transfer those millions of records every time? The time this takes can be a pain, which causes many companies to start relaxing how often and how much data they sync, without any real plan. You definitely don’t want to get your company into this type of situation.

Transformation
One big step toward ensuring you don’t copy and sync every file every time is to cleanse and optimize your data. During this step, the files will be denormalized and pre-calculated so that analysis is easier. By denormalized and pre-calculated, we mean that any inconsistencies will be discovered and resolved. Links with various tags will be standardized, notes and statuses will be examined and organized, and any methods for accessing data will be streamlined.

With these steps complete, there will be no need to continually copy and transfer the same data over and over. You can simply identify the new data, cleanse and denormalize, and then sync with the data warehouse.

Loading
Loading the data into the new data warehouse might be the easiest step, but you could still make critical errors if you’re not careful. You’ll still be working with several different types of information, and one mistake could corrupt several files at once.

Keep in mind that loading the millions of files your company has can take a lot of time, too. You don’t want to cut corners or walk away while the information is being transferred. To do so could result in the loss of vital information. Of course, you can always access this data again from the original sources, but going through the same process multiple times is a waste of company resources and time.

With all your information in one central place, there will never be the need to access several different data sources. You’ll save time, which saves money. You’ll avoid mistakes, which saves money. And you’ll save on additional equipment, which definitely saves money.

Are you ready to integrate all your data sources into one data warehouse? We’re happy to answer any questions you might have, so leave a comment to start the conversation!

More Stories By Keith Cawley

Keith Cawley is the media relations manager at TechnologyAdvice. a market leader in business technology recommendations. He covers a variety of business technology topics, including gamification, business intelligence, and healthcare IT.

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