What is DataOps?

DataOps is a way to manage data that uses a DevOps approach to get deep insights into a company’s data. With this approach, DevOps and data scientists combine forces to better manage data and develop analytics that support rapid innovation.

How does DataOps work?

With the rise of cloud computing, exponential data growth, and artificial intelligence, organizations need to radically simplify data and infrastructure management. Many companies facing these challenges realized that the only solution was to break down the barriers between data creators and data consumers; The two collaborating leads to the development of an overarching data management and orchestration structure that effectively uses data for business intelligence and driving company success.

Traditionally, data management and DevOps reside in two separate departments, each with their own challenges. And while both departments face increasingly complex tasks, they don’t combine efforts to find an efficient way to collaborate. In addition, their responsibilities don’t overlap. Specifically, developers focus on quality code and data teams address integration, data quality, and governance.

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While DataOps is a discipline that is still evolving, it has become the single-most valuable process that helps organizations make the transition to becoming truly data-driven. By building and deploying analytics models efficiently, users can more easily generate real value from their data assets.

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Why do organizations need DataOps?

A majority of organizations struggle with data management and have limited visibility into what data is stored, copied, and protected. For decades, data has also been confined to different repositories, making integration all but impossible. Moreover, the process of managing data—including maintenance, testing, data models, documentation, and logging—is still completed manually.

At the same time, these organizations lack a central perspective on operations and infrastructure management, meaning that infrastructure tasks like storage management—deployment, provisioning, and updating—remain in a reactive, admin-intensive process where optimizing performance and resources is time-consuming and costly.

All of these issues can waste an organization’s time and money, while growing their risk. Failure to get a handle on them leaves IT professionals in the trenches fighting fires and unable to innovate for the organization. Data growth from edge to cloud is only exacerbating this problem.

In addition, while all organizations have massive amounts of data, few truly begin the process of analyzing that information. Data scientists, for example, are still spending about 45 percent of their time on data preparation tasks, including loading and cleaning data. And when organizations can derive intelligence or insight from their data, it is often backward focused. Data collected through batch processing and stored in a database has traditionally been useful in generating reports—but only about the past.

What are the benefits of DataOps?

DataOps is solely focused on creating business value from Big Data. As an agile approach to building and maintaining a distributed data architecture, it provides significant benefits to the organizations that adopt the strategy.

DataOps can help you control data sprawl, ensure data security, and create revenue streams quickly. It enables you to ingest, process, store, access, analyze, and present massive volumes of data from a single well to accelerate digital transformation. Transitioning to a DataOps strategy can bring an organization the following benefits:

· Provides real-time data insights

· Reduces cycle time of data science applications running on Big Data processing frameworks

· Standardizes repeatable, automated, and consolidated processes

· Encourages better communication and collaboration between teams and team members

· Increases transparency by using data analytics to predict all possible scenarios

· Builds processes to be reproducible and reuse code whenever possible

· Ensures higher data quality

· Increases the ROI of data science teams by automating the process of curating data sources and managing infrastructure

· Ensures data is secure and in compliance with data protection laws through automated governance

· Enables scaling data delivery, both internally and externally

With a DataOps approach, organizations have the means to use their data—from different sources, in a variety of formats—to learn from and do much more in real time.

What problem is DataOps trying to solve?

Because data drives everything that an organization does, the storm of massive data generated by IoT and artificial intelligence presents a challenge like nothing that has come before. For organizations to remain competitive, they need to solve the problem of storing and making sense of this huge volume of data.

To do that, companies need to completely change their approach. They need to shift from manual, repetitive data management and inefficient storage infrastructure to a DataOps mindset that homes in on the power of harvesting real value from the data. This may be the only way to increase business agility and speed, while reducing the overhead and costs of managing infrastructure.

That’s because as the volume of data continues to grow exponentially, straining workloads, testing storage capacity, and obscuring data visibility, the data burden ends up dragging performance and resource optimization to a crawl. Some of the issues are:

· Collecting data from more and more disparate sources: how does it get organized without duplication?

· Data governance and ownership: who has oversight and responsibility?

· Data integration: how to smooth the flow of data across legacy systems, databases, data lakes, and data warehouses?

So how does an organization unearth the insights buried in the piles and piles of data to transform their business and develop a competitive advantage? That’s where DataOps comes in.

The core idea of DataOps is to solve the challenge of managing multiple data pipelines from a growing number of data sources in a way that provides a single source of truth to make decisions and run the business. It creates a cohesive view of data from multiple sources, makes data available throughout the enterprise, and improves data governance.

What are the principles of DataOps?

Fundamentally, DataOps works to streamline the lifecycle of data aggregation, preparation, management, and development for analytics. It substantially improves data management in terms of the agility, utility, governance, and quality of data-enhanced applications.

When developing the concept of DataOps, data scientists agreed to several principles to govern the process as part of The DataOps Manifesto. Central principles include:

· Working performance: Evaluating data analytics performance looks at the efficiency of incorporating accurate data on robust frameworks and systems.

· Analytics is code: Describing what to do with the data is fundamental to analytics and the code generated determines what insights can be delivered.

· Make it reproducible: Every aspect of the process must be versioned, from the data, to the hardware and software configurations, to the code that configures each tool.

· Disposable environments: By performing work in isolated, safe, and disposable technical environments that are easy to build, costs can be minimized, while mirroring the production environment.

· Simplicity and efficiency: Technical excellence, good design, and streamlined work lead to greater flexibility and effectiveness.

· Analytics is manufacturing: To deliver analytics insight effectively, analytics pipelines must focus on process-thinking, much like lean manufacturing.

· Quality is paramount: To avoid errors (poka yoke), operators need continuous feedback and analytics pipelines that automatically detect abnormalities (jidoka) and security issues in code, configuration, and data.

· Monitoring is critical: To detect unexpected variation and derive operational statistics, performance, security, and quality must be monitored continuously.

· Improve cycle times: Delivering useful analytics products should be completed quickly and easily throughout the process, from the idea to development and release, with repeatable production processes that ultimately reuse that product.

HPE and DataOps

Unified DataOps by HPE comes to life in our Intelligent Data Platform, which enables IT to manage data and infrastructure through a SaaS-based control plane that abstracts data and infrastructure control from the physical infrastructure.

This architectural approach eliminates the complexity, fragmentation, and costs of managing and maintaining on-premises software and makes the deployment, management, scaling, and delivery of data and infrastructure services invisible to organizations. Additionally, this approach automates management at scale through single-click policies and application programming interfaces (APIs) across globally distributed data infrastructure.

Delivered through HPE GreenLake, this is a unique cloud-native architecture that provides a new data experience, bringing cloud operations to wherever data lives and setting the foundation for unifying data management. Key innovations include:

· Data Services Cloud Console: This console brings cloud agility to data infrastructure wherever it’s located by separating the control plane from the underlying hardware and moving it to the cloud. With unified management under a single web interface, the console offers global visibility and a consistent experience from edge to cloud. Abstracting control in this way enables a suite of data services that radically simplifies how customers manage infrastructure at scale and across the lifecycle.

· Cloud Data Services: This suite of software subscription services uses an AI-driven, application-centric approach that enables global management of data infrastructure from anywhere. Subscribers benefit from its self-service and on-demand provisioning, which eliminate guesswork and optimize service-level objectives at scale.

· HPE Alletra: This is a new portfolio of all-NVMe cloud-native data infrastructure. Managed natively by the Data Services Cloud Console, HPE Alletra delivers the cloud operational experience on demand and as a service. It features a portfolio of workload-optimized systems designed to deliver the architectural flexibility to run any application without compromise.

· HPE InfoSight: This is the industry’s most advanced and mature AIOps platform. It eliminates headaches and wasted time fighting fires with AI-powered autonomous data operations that optimize performance, availability and resource management—and makes infrastructure invisible.