DevOps for Big Data
Getting software from the developer’s laptop to your production environment can be challenging. It is especially true for BigData projects in regulated industries. For some enterprises, the first production data access happens months, if not years, after the project budget gets approved.
Our experts can drastically reduce this time without compromising data security. They can also implement and deliver DevOps practices for your data processing pipelines. Your data deserves to be easily accessible.
Our Practices
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Site Reliability Engineering is what you get when you treat operations as if it’s a software problem. It’s a set of practices related to operations, monitoring, incident management and automation.
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Manual operation efforts should always be minimised. All regular procedures related to data processing upkeep can be automated based on monitoring and alerting. It’s advisable to standardise our environments and use Cloud-based solutions or programmable clustering solutions like Kubernetes to make the automation possible.
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Traditional software applications use multiple deployment strategies: Canary releases, Blue-Green, Rolling updates and more. But did you know these strategies apply to your data pipelines?
Let’s take Canary releases: you can split your source dataset by row percentage and use two versions of the processing code to process them. In case of issues with the new version, we can always re-process with the previous one!
Blue-Green? Just process your dataset twice with different versions and compare the results.
This exercise makes your deployment procedures more resilient and makes your standard processing less vulnerable to data changes.
For example, we can isolate all errored rows for further reprocessing by adopting the Quarantine pattern, just like the Canary approach.
On top of that, all this works well both for Batch and Streaming solutions!
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Value Stream Mapping is an essential first step in planning the data processing code and automation.
Take account of all kinds of Code delivered by all the teams. Then document the steps necessary to make it available to the business. Each item should have the business value and impact attached.
This exercise allows us to prioritise automation to maximise business value.
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You would be surprised how many Big Data solutions do not version their Code artefacts! They just deploy the “latest” version of the data processing pipeline to the production environment.
We can adopt many practices, including containerisation and packaging managed in a central artefact registry. This allows us to implement versioning of each element that correlates to the source code versions. In case of issues, we will always have a version to roll back to.
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What is considered a deliverable in Big Data?
There are many solutions used for Big Data analytics. They use different technology and sometimes even low code drag-and-drop UIs to express the processing without the need to write the code yourself. Even a Dashboard layout to present the business data can be considered Code.
Additionally, many kinds of engineers work with data: BI specialists, Data Engineers, Data Scientists, MLOPS and more.
Our Success Stories
Creating non-biased algorithms is a complicated matter and a goal that we’re still far from achieving. To do that, the data has to be bias-free, and the engineers creating these algorithms need to ensure they’re not leaking any of their own biases. Needles to say, that AI tends to reflect human societal prejudices.