It seems counterintuitive that too much data could be a problem for the big data stack, but that’s exactly the challenge that DevOps and DataOps teams are facing.
When approaching big data, more data seems better than less data, to see the bigger picture and execute better analysis of business or technical issues. However, it is also true that more data means more dependencies, more points of failure and more management.
So, let’s take a closer look at some of the overlapping challenges that DevOps and DataOps teams are facing, to see why more data isn’t always the answer.
It is an open secret that data professionals of all kinds are in short supply. The implications of this are obvious. The lack of availability of the right people to set up or manage big data projects means that projects don’t happen, don’t happen quickly enough or are more likely to fail.
Therefore, throwing more data at a team which doesn’t have the resources to handle it, is a recipe for disaster. A better approach for many teams might be to streamline the data sources and dependencies to create a smaller and more realistic view of the most critical systems and data sources. Call it “slightly smaller data”, maybe! But if ambitions cannot be scaled back for critical data projects, then, of course, another strong option is to seek outside support from data engineering professionals.
Lack of cloud
By now, most technology professionals understand the benefits of the cloud. And yet, many organizations do not host their big data applications in the cloud, with its ability to scale for any storage or compute requirement. In many cases, enterprises have legacy solutions like relational database management systems or statistics and visualization software that are inadequate for managing big data. The result is that DevOps and DataOps teams are burdened with data applications that demand more storage server space and need reconfigured clusters to ensure database optimization.
For DevOps and DataOps teams, data that isn’t hosted in the cloud is more difficult to scale at will. Conversely, moving infrastructure to the cloud improves scalability and provides virtually unlimited capacity, while also reducing cost and improving the performance of big data applications.
It’s also important for DataOps and DevOps to contend with data siloes that are created as different departments and teams build data pools with individualized and narrowly optimized processes. While many teams will view their processes as sacrosanct, each silo is a barrier to success for implementing better data management throughout the organization.
As such, merging data sources and cataloging data is a must, and the data pipeline has to be monitored, updated and maintained to ensure against run-time errors in applications. These most often occur when the pipeline has changed, moved, been reconfigured or is starved of computing resources. Troubleshooting such events is time intensive and complex, and data siloes only add complexity and make this important task more difficult.
These are just some of the major problems facing DevOps and DataOps teams as they navigate their unique environments and business challenges. But if you’re ready to explore some solutions, then complete the form below.
Big data, data science, analytics, BI…data has been subjected to buzzword bingo over the years. Perhaps because of the hype, the results of big data initiatives often do not...
“It is a capital mistake to theorize before one has data.” - Arthur Conan Doyle, author of Sherlock Holmes Despite the advice of Arthur Conan Doyle, theorizing to a greater...
Data is the foundation for digital transformation, and data is what powers every sort of digital initiative. At times over-hyped, almost always under-used, data has steadily grown...