An unsung hero of IT could very well be the batch job. Used across most industries, batch is a method for processing high volumes of data with little user interaction. Because batch jobs can consume a great deal of resources and take a long time, they are typically run at night when there are few interactive users.
However, the process of setting them up—and the reality of maintaining them—can be quite daunting considering these common pain points:
- Complexity and Noise
Typically, medium to large-sized batch jobs involve a high degree of heterogeneity: different schedulers, a variety of dependencies, and so forth, across a number of geographies. This leads to a complexity and scope that can be difficult to manage.There also tends to be a lack of transparency into the batch job processes given the large amount of data that is processed without much user interaction. This can lead to inaccurate alert configurations, which then trigger false alerts—“noise” that can take up a lot of an IT team’s time.
While batch jobs are running, unexpected issues—such as outages, delays, or SLA violations—can occur, often due to a lack of visibility and an inability to prioritize actions.When a job fails, it is difficult to assess the impact of the failure because its underlying dependencies, and their scale, are not transparent. It is difficult to plan for what you cannot see, and this snowball effect leads to insufficient time allotted to take corrective actions.
- Difficulties in Assessing Impact
Most importantly, the complexities and lack of transparency described above make it difficult to assess the impact of batch jobs. It is even more difficult to determine the business impact of changes to processes and technologies, especially when they affect dependencies to the batch jobs themselves. This increases the probability of a significant negative business impact that causes overall instability.
- Complexity and Noise
What can be done to mitigate these issues and help you effortlessly run successful batch jobs? Enter ignio from Digitate.
- Improve Transparency
First, ignio builds a comprehensive “blueprint” of the environment that reveals the connections among different business units, batch schedulers, and technologies. That way you know exactly what is being run and the dependencies at each step of the process. Everything that is included in the blueprint is also analyzed (using time series, etc.), allowing ignio to understand its “normal” behavior.
- Eliminate Risks and Surprises
To avoid unpleasant surprises, ignio constantly assesses the probabilities of job delays or failures. It also forecasts potential risks, which allows it to prioritize actions, predict potential problems, and suggest preventive actions.
- Plan for Tomorrow
Without a view of potential problems, it is nearly impossible to plan ahead. ignio provides automated virtual prototyping and the ability to conduct “what if” analyses, allowing users to introduce hypothetical scenarios and receive answers in real-time. Users can assess the impact of changes before the changes are actually implemented, and fix potential problems before they occur.
Check back next week when we take a deeper dive into batch jobs!