DataOps, a methodology used by data teams to improve the quality of data analytics, is a complex topic. Industry leaders such as LinkedIn, Airbnb, and Uber have created their own operations platforms. Without open standards currently in place, Cloudera promises to incorporate an open standard in its upcoming product feature.
What happens to an organization when they move from hundreds of reports to hundreds of models? They run into similar problems as before and need to make sure operational procedures are in place. Machine learning adds more complexity to production deployment compared to traditional business intelligence systems. Leading organizations have created their own internal proprietary systems to enable DataOps. However, they require significant effort and investment and lack consistency and standards. Also, not every organization can afford to invest heavily in this area due to a lack of maturity.
Cloudera launched a preview of its DataOps offering and intends to launch its open standard–supported product in 2020. Cloudera will let the open standard community define standards. Cloudera is also moving towards the Apache Atlas product and away from its proprietary Cloudera Navigator data governance tool.
Cloudera scores quite well in Info-Tech SoftwareReviews and continues to strengthen its market position:
Source: Cloudera at SoftwareReviews, January 2020
Adopting Apache Atlas and moving away from a proprietary tool such as Cloudera Navigator reaffirms Cloudera’s strategy to stay with the open-source ecosystem after merging with Hortonworks in 2019. Data science production deployment lacks standards, and they need to be established at the industry level. Apache Atlas provides a solid foundation to build upon DataOps repository and deployment standards. However, the industry currently lacks a comprehensive data science policy framework for model development, and that needs to be established to be successful with DataOps open standards.
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