Avenga’s response to the war on Ukraine: Business Continuity and Humanitarian Aid
Director of Avenga Labs
Table of content
Data is considered the fuel for digital transformation.
First of all, it helps to know the past and current performance of different business processes. It assists in predicting the future and reacting accordingly. A better understanding of the market situation and trends enables efficient decision making on operational, tactical and strategic levels. Decisions are made faster and have more facts which improve their quality.
In this metaphor of ‘fuel’, we need to make sure to manage it efficiently.
Data-driven enterprises are more successful than those who do not embrace the data culture.
DevOps have helped to cross the boundaries between software development and IT operations. Now we need to cross another border and remove another silo. This is about removing another division line, this time between data teams and software teams.
The data area of activity is still often associated with static data which is waiting to be processed in large data lakes or warehouses. There’s plenty of time to analyze it, work on it, prepare reports, etc.
This appears to be the easiest part of the data processing space. With the meteoric rise of data streaming and real time data analytics, the time for processing data has been shortened from hours to milliseconds and the time to fix bugs in data processing pipelines was reduced from days to minutes.
Stream processing means that data is broadcasted 24/7 with a loose coupling between the data producers and consumers. Flexibility, when not managed properly, creates chaos and becomes a problem instead of being a solution.
In the center of DataOps, we usually place the workflow orchestration. It manages data flows, data processing nodes, their executions, logging and monitoring.
This makes it easier to see how the data is being processed from internal and external sources. Choosing the right workflow orchestration solution is a key decision in the design of the DataOps environment.
It also helps to collaborate between teams and is a practical manifestation of total automation and data, as a code trend.
→ Read about Star as code – Everything as code, the times of total automation
At Avenga, we do understand the paramount importance of data quality.
It is expected that the data correctness aspect is known during data ingestion and ideally before the data reaches the receiving nodes. We want to avoid poisoning the data of the target system when the quality of incoming data drops substantially.
Error counters from data processing nodes are often used to stop the pipeline, identify the breaking point, and figure out where the problem is.
First of all, fixing errors in data and its processing is identifying the point in the pipeline and the reason why it is wrong.
With an efficient CD pipeline for data processing, we should be able to do it very quickly.
Then we have multiple options, such as rejecting data from the broken pipeline and repairing the data, including the automated repairs by data augmentation and transformations. Afterwards, we should be able to replay the data in the stream to try again, with a selected set of repaired messages.
Processing time affects the data quality directly. The processing nodes may require data from multiple sources to be ready on time, otherwise the entire pipeline will be delayed so significantly that the entire processing won’t return the business viable results.
Monitoring timeliness is an important part of properly designed and implemented data processing environments.
Data is not just ones and zeroes representing various business entities.
Data usually contains sensitive information about people and business, which needs to be protected. More and more regulations are changing data storage and processing practices. They are impacting all the data processing procedures (unless ignored, which is a direct threat to business stability).
Security is a process and it is also represented in DataOps, as security is part of every pipeline and organization culture. In other words, DevSecOps should be enabled by default for DataOps.
→ Read more about DevSecOps – DevOps with security
DataOps is an implementation of the DevOps culture for data engineers, analysts and scientists.
But it’s not enough to make it successful.
The DevOps culture should be applied to entire IT organizations.
Without the proper mindset, no toolset nor methodology will work.
→ Look why To change or to pretend a change – that is the question
Data Fabric for the entire enterprise is the ultimate goal for DataOps. Data management as a platform means both the creation of the right solution and then running it and evolving it in the future.
The good thing about it is that it can be built gradually, step by step. The existing processing pipelines don’t have to be stopped to allow the building of another building block for the enterprise’s data platform.
In the true spirit of Data Mesh, data is finally becoming a product.
Another good thing is that it never ends, because data sources change, data processing capabilities evolve, and data quality requirements only increase.
Data democratization without DataOps is simply impossible.
Without proper data processing pipelines, the assurance of data quality, all in a continuous mode, is unreliable and no one can expect good results from the Machine Learning (ML) pipelines.
Jumping too quickly into machine learning models and advanced analytics won’t achieve the desired results, but only bring disappointments. We wish there was an easy workaround, but there’s none; other than a combination of collaboration, discipline, proper tools and constant improvement.
In other words, DataOps is an enabler for the Continuous Deployment of Machine Learning.
→ Explore Continuous Delivery for Machine Learning (CD4ML)
DataOps is an inevitable evolution in the data area, which is becoming more important than ever. Maybe the name will change, but the idea will certainly last.
In data space, all the patterns and technologies will continue to evolve and work together: traditional warehouses, data lakes, etc.
New trends are on the rise, such as Data Fabric and Data Mesh.
There are people or vendors who claim it’s just a matter of the question of rolling out their product, but we know the topic is much deeper and complex.
Our DevOps and data experts are here to help you plan, design and optimize your DevOps processes and toolsets.