Ralph Kimball, a father of data warehouse architecture, suggests a four-step process for data profiling:. Also, helps identify orphan keys, which are problematic for ETL and future analysis. Cardinality —checks relationships like one-to-one, one-to-many, many-to-many, between related data sets.
This helps BI tools perform inner or outer joins correctly. Pattern and frequency distributions —checks if data fields are formatted correctly, for example if emails are in a valid format. Extremely important for data fields used for outbound communications emails, phone numbers, addresses. Data profiling, a tedious and labor intensive activity, can be automated with tools, to make huge data projects more feasible. These are essential to your data analytics stack. Traditional data profiling, as described in this post, is a complex activity performed by data engineers prior to, and during, ingestion of data to a data warehouse.
Data is meticulously analyzed and processed with partial automation before it is ready to enter the pipeline. Today more organizations are moving data infrastructure to the cloud, and discovering that data ingestion can happen at the click of a button.
Cloud data warehouses, data management tools and ETL services come pre-integrated with hundreds of data sources. But if you can click a button and move data instantly into your target system, what about data profiling? Data profiling is more crucial than ever, with huge volumes flowing through the big data pipeline and the prevalence of unstructured data.
In a cloud-based data pipeline architecture, you need an automated data warehouse that can take care of data profiling and preparation on its own. Instead of analyzing and treating the data using a data profiling tool, just pour it into the automated data warehouse, and it will automatically be cleaned, optimized, and prepared for analysis. Analytics Stack Guide. What is data profiling?
Data profiling produces critical insights into data that companies can then leverage to their advantage. More specifically, data profiling sifts through data to determine its legitimacy and quality.
Analytical algorithms detect dataset characteristics such as mean, minimum, maximum, percentile, and frequency to examine data in minute detail. It then performs analyses to uncover metadata, including frequency distributions, key relationships, foreign key candidates, and functional dependencies. Data profiling can eliminate costly errors that are common in customer databases. Learn how data profiling helps reduce data integrity risk.
For many companies that means millions of dollars wasted, strategies that must be recalculated, and tarnished reputations. So how do data quality problems arise? Often the culprit is oversight. Companies can become so busy collecting data and managing operations that the efficacy and quality of data becomes compromised. That could mean lost productivity, missed sales opportunities, and missed chances to improve the bottom line.
Once a data profiling application is engaged, it continually analyzes, cleans , and updates data in order to provide critical insights that are available right from your laptop. Specifically, data profiling provides:. Once data has been analyzed, the application can help eliminate duplications or anomalies. Profiled information can be used to stop small mistakes from becoming big problems.
It can also reveal possible outcomes for new scenarios. Most databases interact with a diverse set of data that could include blogs, social media, and other big data markets. Profiling can trace back to the original data source and ensure proper encryption for safety.
A data profiler can then analyze those different databases, source applications, or tables, and ensure that the data meets standard statistical measures and specific business rules. Name required. Email required. Please note: comment moderation is enabled and may delay your comment. There is no need to resubmit your comment.
Notify me of followup comments via e-mail. Written by : Sagar Khillar. Burlington, Massachusetts: Morgan Kaufmann, Print [1]Loshin, David. Print [2]Kantardzic, Mehmed. Print [3]Olson, Jack E..
Data Quality: The Accuracy Dimension. Amsterdam, Netherlands: Elsevier, Print [4]Abedjan, Ziawasch, et al. Data Profiling. Print [5]Nong, Ye. Data Mining: Theories, Algorithms, and Examples. User assumes all risk of use, damage, or injury. You agree that we have no liability for any damages. Author Recent Posts. Sagar Khillar.
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