According to Bahaa Al Zubaidi, DataOps is a new approach to managing and processing data. It is designed to make the use of big data technologies more efficient and easier to manage, while also reducing costs and improving the quality of data at the same time.
In order to explain what DataOps is we need to understand what it isn’t.
DataOps is not a new technology or programming language; it simply refers to a new way of doing things when working with data, particularly in an operational environment. DataOps uses tools such as Hadoop and Spark (or other similar technologies), but it does not require them.
DataOps is not just for large companies; in fact, it can be applied by any organization that has large amounts of data that need processing, regardless of size or industry vertical.
What is DataOps’ intellectual heritage?
We may thank management consultant W. Edwards Deming, whose ideas are widely credited with sparking the post-World War II Japanese economic miracle, for the creation of DataOps.
Deming-inspired manufacturing practices are increasingly being applied to the fields of software engineering and information technology.
With DataOps, similar approaches are expanded into the realm of data. DataOps is an approach to developing and operating data analytics systems that borrow ideas from Agile software development, DevOps, and lean manufacturing.
Smaller lot sizes reduce work-in-progress and boost overall manufacturing system throughput, which is why Agile is an application of the Theory of Constraints to software development.
When lean ideas are applied to software production, such as “reduce waste,” “continuous improvement,” and “wide focus,” the result is DevOps. A steadfast dedication to quality, supported by methods like statistical process control, is another thing that lean manufacturing brings to data analytics.
Isn’t DataOps only “Data DevOps”?
This is the first thing that most people think of when they hear the word “DataOps.” Despite some semantic fuzziness, the word “DataOps” gets across the message that data analytics has the potential to go to the same level as DevOps did for software development.
When data teams adopt innovative technologies and practices, DataOps can double or even triple the rate at which quality and cycle time can be improved.
DevOps is a process that enhances the efficiency of creating new software. Companies like Amazon, Netflix, and Google can carry out millions of code releases annually because of this. DataOps not only has to manage a dynamic manufacturing operation but also speeds up software development (new analytics) (i.e., data operations).
DataOps is a collection of practices, such as DevOps, that are tailored to the difficulties of overseeing a pipeline for processing vital business data. Download the free paper “DataOps Is NOT Just DevOps for Data” to understand more about the distinctions between the two practices.
What issue does DataOps aim to address?
“DataOps allows you to take command of your workflow and processes, removing the many roadblocks that have been holding back your data organization. And, it from reaching its full potential in terms of efficiency and quality.”, Said Bahaa Al Zubaidi.
The period of time from when an idea is first proposed to when complete analytics are deployed is referred to as the “cycle time.” Deploying just 20 lines of SQL might take months for many companies. Long wait times are frustrating for users and stifle innovation.
An ideal data team works in tandem with its users to quickly implement new ideas, iterate toward higher-quality models and analytics, and respond to suggestions for improvement.
On the contrary, that has been our experience. Problems with data and analytics are a constant source of distraction for data teams. 75% of a data scientist’s time is spent on manual data manipulation and other menial tasks. Data team members and stakeholders are disappointed and frustrated by the slow and error-prone progress.
Several factors contribute to the lengthy nature of the analytics cycle:
- Ineffective collaboration among the data team
- Failing to coordinate efforts among data-related departments
- Assuming IT will eventually dispose of or configure system resources
- Data access is currently being anticipated.
- Taking one’s time and being cautious to avoid sub-par results
DataOps is not something that needs to be implemented exclusively by IT teams. It should be embraced by all departments within an organization because all departments have access to data and can benefit from using it effectively.
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