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Engineering Machine Learning Data Pipelines Series: Big Data Quality - Cleansing Data at Scale

Making the hurdle from designing a machine learning model to putting it into production is the key to getting value back, and the roadblock that stops many a promising machine learning project. After the data scientists have done their part, engineering robust production data pipelines has its own set of tough problems to solve. Syncsort software helps the data engineer every step of the way.

Once you’ve got data pulled in from multiple sources, you need to assess the mess. In nearly every data set, there will be flaws. Missing data, misspelled data, misfielded data, dozens of common problems that need to be repaired before the data is ready to use. The data quality software that has been on the market for years is the obvious choice, since it already has the full toolset to assess the problems you’re up against and correct them. Unfortunately, most data quality software was built in the age of single server data warehouses and doesn’t scale to cluster-sized problems. It is also, traditionally, far too slow to support the kind of real-time use cases that drive the machine learning world.

When Syncsort bought Trillium, the industry leader in data quality software for over a decade, we combined Trillium Quality with Intelligent Execution, our artificially intelligent dynamic optimizer that provides excellent performance on MapReduce or Spark. Rather than coding everything from scratch and reinventing the data quality wheel, view this short webinar on-demand to learn how you can feed production machine learning models with shiny clean data while spending zero time on coding and performance tuning. These fifteen minutes could save you weeks.