DevOps started off as a methodology that integrates Developers and Operations teams to work in tandem in software development projects. It facilitates seamless coordination and communication between teams, reduces time from idea to market and significantly improves operational efficiencies while optimizing costs. Today, DevOps has rapidly evolved to include several other entities of IT systems. A new addition is Business intelligence.
DevOps jelled well with Big Data as both methodologies are contemporary and complement each other in managing of massive volumes of live data moving between development and production that is maintained relevant via seamless coordination between teams. When it comes to business intelligence, data warehousing and analytics are two important components that need to be managed. As BI deals with batches of data, it doesn’t easily integrate with the DevOps environment by default.
Managing Data Warehousing with DevOps
A data warehouse is a central data repository that collects data from various disparate data sources in and outside the organization and hosts them in a central location allowing authorized people and reporting and analytics tools to access it from any location. Managing a robust and sophisticated data warehouse is a challenge as multiple stakeholders are involved in making a change which makes deployments rather slow and time-consuming. Implementing DevOps here can be a revolutionary thing as you can combine data administration teams and data engineering teams to collaborate on data projects. While a data engineer informs potential features that are being introduced to the system, the data administrator can envisage production challenges and make changes accordingly. With cross-functional teams and automated testing in place, production issues can be eliminated. Together, they can build a powerful automation pipeline that comprises data source analysis, testing, documentation, deployment etc.
However, introducing DevOps for data warehouse management is not a cakewalk. For instance, you cannot simply backup data and revert to the backup as and when required. When you revert to a last week’s backup, what about the changes made to the data by several applications?
DevOps for Analytics
The analytics industry is going through a transformation as well. Contrary to the traditional analytics environment that uses a single business intelligence solution for all IT needs, modern businesses implement multiple BI tools for different analytical purposes. The complexity is that all these BI tools share data between them and there is no central management of BI tools. Another issue is that data scientists design models and algorithms for specific data sets to gain deeper insights and offer predictions. However, when these data sets are deployed to the production environment, they serve a temporary purpose. As data sets outgrow, they become irrelevant which means continuous monitoring and improvement is required. The rate at which the data drifting happens is enormous and traditional analytics solutions are inefficient to manage this speed and diversity. This is where DevOps comes to the rescue.
DevOps helps businesses integrate data flow designs and operations to automate and monitor data enabling them to deliver better applications faster. Automation enables organizations to build high performing and reliable build-deploy iterative data pipelines for improving data quality, accelerate delivery and reduce labor and operational costs. Monitoring data for health, speed and consumption-ready status enable organizations to reduce blindness and eliminate performance issues. It means a reliable feedback loop is created that covers data health, privacy and data delivery for ensuring smooth flow of operations for planned as well as unexpected changes.
The Bottom Line
Bringing DevOps into the BI realm is not an easy task as BI environments are not suitably designed for DevOps. However, businesses are now exploring this option. Bringing DevOps into the BI segment gives situational awareness to businesses as they can make informed decisions when they gain insights into relevant data added from multiple sources. Moreover, it brings great collaboration between teams, allows better integration between different application layers while helping businesses to explore and quickly tap into new markets. Most importantly, it makes your business future-proof.
