A Detailed Guide To DataOps
What is DataOps?
● DataOps is a set of practices combining data analytics, operations
management, machine learning, and automation engineering to improve
the quality, speed, and efficiency of data-driven decisions.
● Most programs developed by data pipeline developers come under four
segments-- Big data, data science, self-service analytics, and data
● DataOps tools focus on collaboration and communication that's vital to
scale development, enhance productivity and output, minimise errors, and
speed up the time to market.
● You'll typically find five categories of DataOps tools popular in the market
● tools, orchestration tools, case-specific tools, component tools, and
Use the correct IT management tool and DataOps framework to integrate
DataOps smoothly into your organisational workflow.
Challenges Solved By DataOps
Big data made an imported promise- Deliver fast and reliable data-driven, critical and
actionable business insights. However, the promise remains unfulfilled due to various
challenges that can be classified into three categories:
● Organisational challenges
● Technical hurdles
● Manual errors
DataOps leverages the principles of Agile development, DevOps and Lean
manufacturing to address these challenges. The most important challenges resolved by
DevOps include the following:
Modern organisations source their data from various sources. Additionally, data is
stored in various forms.
However, cleaning and improving such a huge variety of data can be complicated and
time-consuming. Sometimes, the insights generated after processing such complex
data becomes irrelevant or invalid due to extensive time requirement.
DataOps strikingly enhances the speed of processing data and delivering insights.
Sometimes organisational data sets include unstructured format. Extracting information
from such data formats can be a challenging task.
However, search data sources are business-critical and might provide valuable insigh