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Table of contents
1.
Introduction
2.
Big Data WorkFlows
3.
The workload in the Business Problem
4.
Practices for Understanding WorkFlows
5.
Frequently Asked Questions
5.1.
Define the term Process in Big Data WorkFlows.
5.2.
What do you mean by WorkFlows?
5.3.
Define the best practice for the need for Big Data WorkFlows?
5.4.
What is Data in WorkFlow?
5.5.
Where do the data processing methods fail?
6.
Conclusion
Last Updated: Mar 27, 2024

Understanding Big Data WorkFlows

Author Prachi Singh
1 upvote
Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

To understand the basics of big data workflows, we must understand what a process is and how it co-relates to the workflow in data-active environments. Methods tend to be designed as high-level, end-to-end structures useful for decision making and normalizing how things get done in a business or organization. In contrast, workflows are task-dependent and require more accurate data than processes. Processes consist of one or more workflows relevant to the overall objective of the process.

Big Data WorkFlows

In many ways, big data workflows are pretty similar to standard ones. Data is necessary for any workflow in the various steps to accomplish the given tasks. Let us consider the workflow in the preceding healthcare example. One elementary workflow is the task of “drawing blood.” Drawing blood is a necessary task needed to complete the entire diagnostic process. If some tragedy happens and blood has not been removed, or the data from that blood test has been lost, it will directly impact the integrity or truthfulness of the overall activity. 

What happens when users introduce a highly dependent workflow on a big data source? Although one might be able to use existing workflows with big data, one cannot assume that a process or workflow will work correctly by just substituting a big data source for an authoritative source. This scenario may not work because standard data-processing methods do not have the common strategies or performance to handle the variety of the big data.

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The workload in the Business Problem

The healthcare example concepts the need to analyze after the patient draws blood. In the standard data workflow, the blood is typed, and then specific chemical tests are performed based on the requirements of the healthcare practitioner. It is unlikely that this workflow understands the testing required for identifying particular biomarkers or genetic mutations. The workflow would fail if you supplied big data sources for biomarkers and mutations. It is not big data-aware and will need to be modified or rewritten to support big data.

Practices for Understanding WorkFlows

  • The best practice for understanding the need for workflows and the effect of big data is to identify the significant data sources one needs to use. 
  • Mapping the big data types to users' workflow data types. 
  • Ensure that users have the processing speed and storage access to support their workflow. 
  • Selection of the data store best suited to the data types. 
  • Modification of the existing workflow to accommodate big data or create a new big data workflow.

Frequently Asked Questions

Define the term Process in Big Data WorkFlows.

Processes consist of one or more workflows relevant to the overall objective of the process.

What do you mean by WorkFlows?

Workflows are task-dependent and require more accurate data than processes.

Define the best practice for the need for Big Data WorkFlows?

The best practice for understanding the need for workflows and the effect of big data is to identify the significant data sources one needs to use. 

What is Data in WorkFlow?

Data is necessary for any workflow in the various steps to accomplish the given tasks.

Where do the data processing methods fail?

Standard data-processing methods do not have the common strategies or performance to handle the variety of the big data.

Conclusion

So that's the end of the article Understanding Big Data Workflows.

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