Create a Data-Driven Organization with DataOps
How to implement and benefit from DataOps in 6 steps.
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Data-centric projects are becoming bigger in both size and complexity, and companies increasingly look to analytics to drive growth strategies. More and more, data teams are responsible for supplying business partners with analytical insights quickly and effectively to provide a competitive edge and keep up with an ever-evolving marketplace. However, there is a disconnect between the speed at which marketing and sales demands evolve and the speed at which many data teams can deliver useful insights. Often times data quality is inhibited by siloed pipelines, a lack of collaboration across data functions, and manual or ungoverned processes for data delivery. This gap between what users need and what data teams can provide is a source of conflict and frustration for many businesses and can prevent an organization from taking full advantage of the strategic benefit of its data.
Data teams today need to be able to deliver reliable and relevant insights on-demand. DataOps is a next-gen vision for data quality, integration, and real-time analytics that promotes cross-team collaborations and seeks to remove obstructions from the flow of data across pipelines.
What is DataOps?
It is common to hear DataOps described as DevOps for data analytics, but that description isn’t quite accurate and misses the mark. DataOps does indeed borrow from DevOps principles, but it also borrows from agile and statistical process controls. It is a combination of tools and methods which streamline the development of new analytics, while ensuring high levels of data quality.
How to Implement DataOps in 6 Steps
DataOps represents an effective approach to optimizing the data and analytics pipeline. Here are 6 steps to implementing DataOps at your organization:
1. Implement Automatic Testing
Code is imperfect and so are even your best team members, so creating a culture of frequent testing is paramount to providing high quality and reliable data. Every time a change is made at any point in your pipeline, a test should be run to make sure everything is working. Implementing automatic testing reduces the time spent on tedious manual tests and helps ensure that your feature release is accurate and functional. Testing should be added incrementally as each feature is added, making testing and quality control built-in to your pipeline.
Tests in your data pipeline serve as a statistical process control that ensures the integrity of the final output. Having a suite of tests in place allows you to make changes quickly, check for flaws automatically, and release your findings confidently.
There are three types of tests your team should consistently run to ensure data quality and accuracy:
- Inputs – verify the inputs at the processing stage
- Business Logic – check that all data matches business assumptions
- Outputs – check that your results are consistent
2. Use Version Controls
Data takes a long journey from raw inputs to valuable insights, and data analysts use a variety of tools along the way to process, cleanse, transform, combine, analyze, and report it. Each of these tools are comprised of different types of code, i.e. scripts, source code, algorithms, configuration files, etc. Your entire data pipeline is configured of and controlled by code from end-to-end, and that code needs to be up-to-date and usable. This is why version control is so important in DataOps-- a version control tool helps teams of individuals organize and manage the changes and revisions to code. It also keeps code in a known repository and facilitates disaster recovery. Most importantly, it allows data teams to branch and merge.
3. Branch & Merge
When a developer wants to work on a feature, they pull a copy of all relevant code from the version control tool and develop changes on that local copy. That copy is called a branch. This method helps data teams maintain several coding changes in the analytics pipeline in parallel. Once the changes to a branch are completed and tested, it can be merged back into the pipeline or “trunk” where it came from.
The process of branching and merging boosts data analytics productivity by allowing teams to make changes to the same source code files in parallel without slowing each other down. Each individual can run tests, make changes, take risks, and experiment in their own environment, which encourages innovation and creativity without undue risks to the pipeline.
4. Provide Isolated Environments
Your team members need their own space to pull data sets and work on them individually, as outlined in the last point. This is important to avoid conflicts on the production database, such as breaking schemas or mixing up models as new data flows in. This diagram shows how version control, branching and merging, and isolated environments all work together:
5. Containerize & Reuse Code
Containerizing and reusing code increases your team’s productivity by cutting out the tedious task of trying to work with a data pipeline as a monolith. Small components that have been segmented or containerized can be reused easily and more efficiently without reinventing the wheel or risking messing with the larger data infrastructure.
Containerization also allows programmers to work with code that they are otherwise unfamiliar with. A container can contain complex, custom tools inside but as long as it has a more universally familiar external interface, anyone can deploy it without breaking the essential programming. One use case for this is an operation that requires a custom tool such as a python script, FTP, or other specialized logic. If the container is already built it can be re-deployed by anyone on your team.
6. Use Parameters in Your Pipeline
Parameters grant your pipeline the flexibility to respond to myriad run-time conditions. Questions that frequently come up are: Which version of the raw data should be used? Is the data directed to production or testing? Should records be filtered according to certain criteria? Should a specific set of processing steps in the workflow be included or not? A robust pipeline design allows you to set parameters for these conditions should they arise, making it ready to accommodate different run-time circumstances and streamlining your efforts.
DataOps empowers your data and analytics team to create and publish fresh analytics to users. It requires an Agile mindset and must be supported by the automated platform outlined in the 6 steps to implement DataOps outlined above. A fine-tuned and well-designed data pipeline gives your organization a competitive advantage and helps foster a data-driven organization.
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