What is DataOps?

DataOps combines users, processes and technologies to create a reliable, high-quality data pipeline that any user can easily translate into insights. It applies lessons learned from DevOps to data management and analytics.

Initially, DataOps was introduced as a set of best practices, but has now advanced to be an integral part of data analytics and can be considered as an independent approach to data analytics. Effective deployment of DataOps has shown to accelerate time to market for analytic solutions, improve data quality and compliance, and reduce cost of data management.

  • Rapid innovation and experimentation
  • Collaboration across people, technology, and environments
  • Measurement, monitoring, and transparency of results

As data and analytics pipelines become more complex and development teams grow in size, organizations need to apply standard processes to govern the flow of data from one step of the data lifecycle to the next – from data ingestion and transformation to analysis and reporting. The goal is to increase agility and cycle times, while reducing data defects, giving developers and business users greater confidence in data analytics output. This is the vision of DataOps. The goal is to foster greater collaboration among development, test, operations, and business teams and create a culture of continuous improvement.

DataOps combines five essential elements that range from technologies up to full-on culture change.

  1. Enabling DataOps platform and technologies to manage your data pipelines.
  2. Adaptive architecture to support continuous innovations in  technologies, services and processes.
  3. Data enrichment and transformation for accurate analysis.
  4. DataOps methodology to build and deploy your analytics and data pipeline, following your data governance and model management.
  5. Culture and people change to fulfill the potential of DataOps by putting the right data in the right place at the right time.

With DataOps you get transparency into your operations and analytics development. DataOps manages dynamic manufacturing operations that processes raw data into valuable data for insights and applications in order to shorten the analytics cycle time.

DataOps is based on Agile, Lean, DevOps, and Total Quality Management disciplines.

Like Agile, DataOps emphasizes the use of self-organizing teams with business involvement and short development sprints that deliver fully tested code. Like Devops, DataOps optimizes the software development pipeline. Like Lean, DataOps focuses on efficiency, using version control systems and code repositories that foster parallel development and code reuse. And like Total Quality Management, DataOps espouses continuous testing, monitoring, and benchmarking to detect issues before they turn into major problems.

Data issues solved

  • Inflexible data architecture
  • Waiting for data access
  • Process bottleneck
  • Long analytics cycle times
  • Poor teamwork
  • Hidden data
  • Non-GDPR compliant data flows
  • Lack of business service integration
  • Contra-productive developers
  • Lack of data security

DataOps use cases

  • Application development
  • Data warehousing
  • Dashboard and reporting
  • Data science
  • Data brokerage
  • Data lake

DataOps platform

A DataOps platform is an assembling of many data technologies and practices into a single integrated system to manage your data pipelines and flows from your data sources through a data refinery and a data repository to data consumption. The GetDataInsight DataOps Platform unifies the end-to-end workflow and processes related to data analytics planning, development and operations into a single, common framework, improving overall collaboration. 

The GetDataInsight DataOps platform can be part of a DataOps practice, by adding microservices, orchestration, and data flow management across the business. The platform  acts as a digital control room where all data sources and processes are monitored, managed, and tuned. This reduces the complexity of managing complex data pipelines in a heterogeneous environment.

GetDataInsight DataOps platform components

  • Application and data catalog for automated data discovery
  • Data processing and integration for data wrangling and movement
  • Self-service for decentralized operations with governance
  • Data access protection with authentication and auditing for security operations
  • App topology, data observability, monitoring and alerting for data visuality
  • Data lineage, data masking and PII data discovery for data compliance

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