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Understanding Data Operation (DataOps) Tools and Best Practices

Acknowledging Data Management
          Best Practices with DataOps

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DataOps - Data Operations for Analytics

DataOps, aka Data Operations, combines people, processes, and products that enable consistent, automated, and secure data management. It is a delivery system based on joining and analyzing large databases. Since Collaboration and Teamwork are the two keys to a successful business and under this idea, the term “DataOps” was born. DataOps's purpose is to be a cross-functional way of working in terms of the acquisition, storage, processing, quality monitoring, execution, betterment, and delivery of information to the end-user. It harnesses the individuals’ capacities of working for the common good and business development. Consequently, DataOps calls for the combination of software operations development teams, which is also DevOps. This new emerging discipline made up of engineers and data scientists, advocates sharing the expertise of both and inventing the tools, methodologies, and organizational structures for better management and protection of the organization. The main objective of DataOps is to improve the company’s IT delivery outcome by bringing data consumers and suppliers closer.
DataOps as a Service is a combination of a multi-cloud big-data/data-analytics management platform and managed services for processing the data. Source: DataOps as a Service

The thought for the DataOps concept draws heavily from the source of DevOps, according to which infrastructure and development teams should work together so that projects can be managed efficiently. DataOps focuses on multiple subjects within its field of action, for example, data acquisition and transformation, cleaning, storage, backup scalability, governance, security, predictive analysis, etc.

Do not confuse DataOps with Data Operations.

Most of us who are new to DataOps often get confused between DataOps and Data Ops. So, DataOps is basically the famous movement level of operators and data trades with Data Ops. The term Data Ops corresponds to a "hub for collecting and distributing data,” which provides controlled access to systems to record customer data and marketing performance. Moreover, it ensures confidentiality, limitations on use along with data integrity."

What is the Benefit of DataOps?

The main aim of DataOps is to make the teams capable enough to manage the main processes, which impact the business, interpret the value of each one of them to expel data silos, and centralize them even without giving up the ideas that impact the organization as one all. DataOps, a growing concept, seeks to balance innovation and management control of the data pipeline. Besides, the benefits of DataOps extend across the enterprise. For example:
  1. Supports the entire software development life cycle and increases DevTest speed by the fast and consistent supply of environments for the development and test teams.
  2. Improves the quality assurance and through the provision of "production-like data" that enables the testing to exercise the test cases before clients encounter errors effectively.
  3. It helps organizations move safely to the cloud by simplifying and speeding up data migration to the cloud or other destinations.
  4. Supports both data science and machine learning. Any organization’s data science and artificial intelligence endeavors are as good as the information available. So, DataOps ensures a reliable flow of the data for digestion and learning as well.
  5. Helps with compliance and establishes standardized data security policies and controls for the smooth flow of data even without risking your clients.

Only 22% of data professionals spend their working time to drive innovation with enhanced analytical insights. -Gartner Survey

What are the Principles and Components of DataOps?

  • Raw Source Catalog.
  • Movement/Logging/Provenance.
  • Logica Models.
  • Unified Data Hub.
  • Interoperable (Open, Best of Breed, FOSS & Proprietary).
  • Social (BI Directional, Collaborative, Extreme Distributed Curation).
  • Modern (Hybrid, Service Oriented, Scale-out Architecture).

How to Adopt DataOps Principles?

Add Data and Logic Tests - DataOp's duty is to interact every time a "Data Analytics Team" member makes a change. Add tests for that change. There are two types of tests -
  • Logic Tests cover the code in a Data Pipeline.
  • Data Tests cover the data as it flows by in production.
Put all steps to Version Control - There are lots of stages of processing that turn raw data into useful information for stakeholders. To be valuable, data must progress through these steps, linked together somehow, with the ultimate goal of producing a Data-Analytics output. Branch & Merge - Branching and merging are the main productivity boost for the Data Analytics Team to make any changes to the same source code files. Each team member controls the work environment space. Test programs, make changes, and take risks. Use Multiple Environments - Every Data Analytics team have tools in the laptop for development. Version Control tools allow working at a private copy of code while coordinating with other team members. It cannot be productive if it doesn't have the data required. Reuse and Containerize - In DataOps, the analytics team moves faster, like lighting speed, using highly optimized tools and processes. One of the Productivity tools is to Reuse and Containerize. Reuse Code means reusing Data Analytics components. Reuse code saves time also. Container means to run the code of the application. It a platform like Docker. Parameterize processing - Parameters allow code to generalize to operate on various inputs and respond to them. Parameters used for the improvement of productivity. In this, use program to restart at any specific point.

Why does DataOps Matter?

  • Collaborating throughout the Entire Data Lifecycle - Collaboration is the main part of both DevOps and DataOps. But DataOps involved many more desperate parties instead of the Software Development counterpart. That’s why DataOps is the entire data lifecycle of the organization.
  • Establishing Data Transparency while maintaining security - DataOps promote the data locally, team analysis uses computer resources near to data instead of moving the data required.
  • Utilizing Vision Control for Data Scientist Projects - DataOps use this concept in Data Science. They use this concept when hundred of Data Scientists work together or separately on many different projects. When Data Scientists work on their local machines, then data saved locally, which slowdowns the productivity. To reduce this, make a common repository that solves this problem.
Java vs Kotlin
DataOps Methodology is a new and independent view approach to data analytics based on the whole data life cycle. Implement DataOps Architecture in AWS, Azure, and GCP

What are the Best Practices of DataOps?

  • Versioning
  • Self-service
  • Democratize data
  • Platform Approach
  • Be open source
  • Team makeup and Organisation.
  • Unified Platform for all data- historical and Real-Time production.
  • Multi-tenancy and Resource Utilisation.
  • Access Model and Single Security for governance and self-service access.
  • Enterprise-grade for mission-critical applications and Open source tools.
  • Run Compute on data platform- leverage data locality.
  • Automation saves time. Thus, automate wherever possible
Read the complete article on Data Management Best Practices with DataOps

What are the Tools For DataOps?


Summing up

Companies nowadays are investing a lot of money to execute their IT operations in a better way. DataOps is an Agile method that emphasizes interrelated aspects of engineering, integration, and quality of data to speed up the process. Main highlights of this article:
  1. Data Operation focuses on collaborating agility to Machine Learning and Analytics project.
  2. Data Operatconnectsnect key data sources right away, unlike Salesforce, customer record data, or what we've identified.
  3. DataOps simplifies the processes and maintains a continuous delivery of insights.
  4. DataOps themes: Databases, IoT (Internet of things), Automation, Big data, Machine learning, Smartphones, Blockchain, Cloud, and much more!
Before executing, you are advised to have a look at DataOps Solutions for Enterprises!
Java vs Kotlin
DataOps Platform Solutions for data operations and agile development for data analytics enable effective enterprise data management and Governance. Explore DataOps Solutions for Enterprises

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