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A DataOps Process Template for Unleashing its Full Potential

DataOps Process

DataOps Process

Solutions Review’s Expert Insights Series is a collection of contributed articles written by industry experts in enterprise software categories. In this feature, Talend‘s Thibaut Gourdel offers a DataOps process template for your organization to consider.

Expert Insights Badge SmallOne common misconception is holding back businesses from getting the full value out of DataOps: the belief that it’s meant for citizen data scientists or other data consumers alone. In reality, DataOps must start upstream with data engineers to benefit end users.

91% of organizations plan to make significant investments in DataOps initiatives over the next year. When considering how their investments will take their DataOps to the next level, managers must ensure they correct what the industry got wrong: every single member of data teams, not only data engineers or data scientists, are the key to an effective DataOps machine.

Data engineers are the first to leverage DataOps to establish better automation and observability within the organization’s data pipeline. With proper processes, monitoring, and governance in place, organizations will achieve what they hope to from their investments: improved data agility.

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DataOps Process

The Role of Data Scientists vs. Engineers

Data analysts and data scientists collect, analyze and interpret data for an organization to make informed business decisions. The critical role data analysts and scientists play would not be possible without the support of data engineers. Data engineers are the first line of defense in creating agile data use within an organization.

Before DataOps can be beneficial to citizen data professionals, data engineers must be introduced to the process to extract data and monitor the pipeline so that when data scientists do step in, they have healthy data ready to activate. By becoming more integrated into the early stages of DataOps, engineers make it possible for organizations to unleash the full potential of DataOps.

What Data Engineers Need to Support DataOps

Data engineers play a massive role in the ongoing success of DataOps, but it’s vital that they be set up with the tools to achieve this success. This can be done by establishing a version control by using git to store pipeline and job code and also to manage changes. Most enterprise platforms will support a git integration.

From here, set up efficient chain management pipelines. It’s crucial to determine the keys to proper continuous delivery and deployment (CI/CD) pipelines. Leverage existing tools and automate as much of the chain as possible. With streamlined CI/CD pipelines, time is reduced from deployment to market. In maintaining CI/CD pipelines, accelerate the feedback loop and iterate often.

Another best practice to maintain is using configuration as code for orchestration jobs and data pipelines in order to be stored in source code version systems. This promotes reusability and reproducibility.

Data engineers need DataOps to drive business innovation. With it, there’s a tangible improvement across productivity, agility, and management of both new and legacy data. DataOps allows for new solutions to be developed with less of the fuss around scrambled data flow.

Empowering your DataOps Team

IT leaders can empower engineers to make the most of their DataOps strategy by creating a feedback loop within the organization. Allowing your data teams to align on the language in the DataOps process will ensure better collaboration and communication.

The current issue with this feedback loop is that different areas of business speak different “languages.” For instance, there is a technical and business side of data. Managers need to implement a universal communication method for their DataOps teams.

Additionally, data engineers will need to set up a feedback loop from data operations to data analysts and scientists. The more visibility into what each data segment is working on, the better. Because DataOps is highly collaborative, it requires careful orchestration of diverse teams. Ideally, IT, data and analytics managers will prioritize regular check-ins with business leaders to ensure the importance of data quality is known across the organization, and all teams are aware of the importance of individual roles.

Recapping the Journey

When making the first step in the DataOps journey, take a moment to reflect and re-assess the organizational structure. Understand that much of the existing operational issues may be due to lack of collaboration and misalignment. From here, collaboration is imperative to successful implementation. Data engineers and data analysts must work more closely while aligning on objectives. From here, assess how the DataOps processes and tools can help with collaboration as well as automation.

Harnessing the full potential of DataOps is a continuous process. In doing so, it benefits teams with automation, productivity and collaboration.

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