DataOps 101
An educational resource provided by data experts at Zaloni
What is DataOps?
People, process, technology. DataOps draws inspiration from lean manufacturing and the agile nature of DevOps. It is a method of data management focused on optimizing the end-to-end data supply chain.
DataOps is
- Agile and extensible, bringing together 1st and 3rd party data sources and tools
- A cycle that scales and improves over time through automation and ML
- A “single pane of glass” that connects people, processes, and platforms through one collaborative view
Learn more with these resources
DataOps Maturity Model
Where does your organization land on the maturity model?
Learn more in the blog, DataOps Maturity Model: The Journey to DataOps Success
Key Benefits of a modern
DataOps platform
Visualize the entire data supply chain to quickly identify and resolve bottlenecks and inefficiencies
Streamline data processes and gain deeper insights with automated data profiling
Discover trusted data quickly and easily in a self-service data marketplace
See how a Top 10 Global Bank achieved this ➤
Collaborate with other users by sharing, tagging, and updating data entities
Watch Now: The Dawn of Modern DataOps
Save Your Seat
The DataOps Virtual Event
August 25, 2021
2PM ET
2PM ET
Common DataOps Terms and Definitions
*Click each term to read definition
Big data refers to an extraordinarily large amount of structured and unstructured data that is processed and used for analytics.
A 360 degree view of a single customer’s data which may be used to improve marketing and sales effectiveness and customer experience.
The ability for users to access data in a self-service manner, typically in an authorized, secure way with the proper governance controls in place.
The process of generating or bringing data into an organization from an external source.
Automates data management processes for storing and processing data.
Provides an inventory of an organization’s data sets that are available for analytics, reporting and data science. Data catalogs provide detailed metadata and tools that help users search, find and understand data sets.
A process for organizing data into defined categories. Data classification may also be used to flag sensitive data to improve data security and privacy.
The act of cleaning data to identify and correct any inaccurate or incomplete records within a data set, table or database.
Ensures that sensitive data is appropriately managed and governed in a way that enables compliance with an organization’s legal requirements and governmental regulations.
The process of transforming data from one formation to another.
When data is collected for multiple sources, the data curation process takes place to integrate and organize that data in the given environment while maintaining and preserving data quality.
The process of controlling the integrity, security, and availability of data across an entire enterprise to ensure accuracy and proper usage.
Data is moved from one or multiple sources to another environment where it can be stored and analyzed.
The practice of collecting data from disparate sources and consolidating it into a single, accessible data set.
A centralized repository that stores structured and unstructured raw data in its native format.
From creation to consumption, data lineage tracks and records actions taken upon data throughout its life cycle.
A person who can interpret, analyze, create and communicate the information stored with a data point or set.
Referred to the administrative processes of preserving, authenticating, storing, processing, and collecting data to ensure accessible and reliable data for consumers.
Augmented Catalog
End-to-end Governance
Intelligent Provisioning
Multi-cloud Data Management
Multi-Cloud Data Delivery
Cloud Partners
Unified Views
Customer Golden Records
Rich Marketplace
Endless Collaboration
Analytic Sandboxes
Data Shopping Cart
Detailed Lineage
Data Security
Evidence-based Reporting
Automated Data Discovery
Quality Checks
Data Preparation
Metadata Management