
DataOps: When DevOps meets Data Processing
Abstract
Data is considered the most valuable resource in the modern world since all industrial sectors and business aspects are running based on different kinds of data. Before all of us or machines can use and analyze the data, it is undoubted that data needs to be driven through a set of processing processes, because data is not readable by humans, or unstructured that even machines cannot understand it correctly. Therefore, the effectiveness of industrial and business operations and management also depends on the quality of the data processing steps. That leads to a concern that how we can assure the data processing quality when there are too many different unexpected events such as a larger amount of data than expected comes to the system, or computing resources are suddenly not available anymore. In addition, the development and deployment scripts for processing data can cause hazardous events if engineers make irreversible mistakes.
—
Cloud computing and DevOps methodology are the rising technologies and terms in recent years. Cloud computing offers fast start-up, flexible, scalable, and elastic computing resources based on the user demand to enhance business operation, while DevOps provides the engineer’s automation features for software development and deployment to reduce human efforts and mistakes.
As a result, with the help of cloud computing technology and DevOps methodology, the data processing process can be designed and implemented with the most efficient and economic solutions. That leads to the birth of the term DataOps.
Why you should join our DataOps Webinar?
We not only provide you with a clear concept of DataOps, also give you a detailed explanation on how to apply DataOps and why you should use DataOps as a new technology solution for your company.
- Rapid Results: By iteratively installing your corporate analytics platform, you may reduce the data collecting process from months to weeks. Data may be moved through your pipeline in minutes and hours rather than weeks or months.
- Greater Precision: Utilize data stewardship services to detect data quality concerns before they surface on your executive dashboards and obtain remedial advice.
- Ongoing Monitoring: Check to see whether you’re making progress toward your data quality improvement objectives.
- Cost Savings: Reduce development and support expenses by up to 300 percent by utilizing fewer resources to create and maintain code.
- Enhanced Security: Securely send data to dedicated database settings by encrypting it.
Agenda:
- Introduce the speakers and the company
- Concept of DataOps
- Use Case: image and video data type processing
- Use case: ETL
- DataOps in different industries: Automotive; Healthcare & Banking
- Q&A
Our Speakers
Do you want to watch our webinar?
Leave your information in the box below. We will send you the record of our ‘DataOps’ webinar accordingly.
READ OUR SUCCESSFUL CASE STUDIES

Case Study – Implementing a monitoring system for the client’s machine learning model on the cloud
Case Study CUSTOMERS The client who wants to install a monitoring system for their machine learning model on the cloud. BACKGROUND The client wishes to

Case Study – Design and implement data science in full body anonymization
Case Study CUSTOMER The client who wants to localize and obfuscate (i.e. hides) sensitive information in images/videos in order to preserve the individuals’ anonymity. CHALLENGE