What is Data Operation (DataOps) ? Principles | Benefits | Tools

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. Its 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, it 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 it is to improve the company’s IT delivery outcome by bringing data consumers and suppliers closer.

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Do not confuse DataOps with Data Operations

Most of us who are new to it, often get confused between both terms. So, it 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 are the different DataOps Tools?

Some of the best DataOps tools are:

Composable.ai

Composable The only DataOps platform, DataOps is an Analytics-as-a-Service platform that offers an end-to-end solution for managing data applications. Its low-code development interface users can set up data engineering, integrate data in real-time from various sources, and create data-driven products using its AI platform.

K2View

To make the customer data easily accessible for analytics, this DataOps tool gathers it from various systems, transforms it, and stores it in a patented Micro-Database. These Micro-Databases are individually compressed and encrypted to improve performance and data security.

RightData

The data testing, reconciliation, and validation services offered by this DataOps tool are effective and scalable. Users can create, implement, and automate data reconciliation and validation processes with little to no programming knowledge to guarantee data quality, reliability, and consistency and prevent compliance issues. Dextrous and RDt are the two platforms that RightData uses for its tool.

Tengu

A low-code DataOps tool called Tengu is made for data experts and non-experts. The business offers services to assist companies in comprehending and maximizing the value of their data. To set up their workflows, Tengu also provides a self-service option for existing data teams. Additionally, users can integrate many tools thanks to its support. Both on-premises and in the cloud are options for this platform.

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

What are the Principles 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 its Principles?

Add Data and Logic Tests

• Logic Tests cover the code in a Data Pipeline.
• Data Tests cover the data as it flows 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 has tools in the laptop for development. Version Control tools allow working on 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 it, 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. Reusing code saves time also. Container means to run the code of the application. It is 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 the program to restart at any specific point.

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Importance of DataOps

• Collaborating throughout the Entire Data Lifecycle - Collaboration is the main part of both. But it involved many more desperate parties instead of the Software Development counterpart. That’s why it is the entire data lifecycle of the organization.
• Establishing Data Transparency while maintaining security - It promotes the data locally, and team analysis uses computer resources near to data instead of moving the data required.
• Utilizing Vision Control for Data Scientist Projects - It uses 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 is saved locally, which slowdowns productivity. To reduce this, make a common repository that solves this problem.

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What DataOps as a Service Offers?

DataOps as a Service is offered as a combination of a multi-cloud big-data/data-analytics management platform and managed services around harnessing and processing the data. It provides scalable, purpose-built big data platforms that adhere to best practices in data privacy, security, and governance using its components.

Data Operations as a service means providing real-time data insights. It reduces the cycle time of data science applications and enables better communication and collaboration between teams and team members. Increasing transparency by using data analytics to predict all possible scenarios is necessary. Processes here are built to be reproducible and reuse code whenever possible and to ensure higher data quality. This all leads to the creation of a unified, interoperable data hub.

Enable Deeper collaborations 

As business knows what data represents, it thus becomes crucial to collaboration between It and business. It enables the business to automate the process model operations. Additionally adds value to the pipelines by establishing KPIs for the data value chain corresponding to the data pipelines. Thus enabling businesses to form better strategies for their trained models. It brings the organization together in different dimensions. It helps bring localized and centralized development together as a large amount of data analytics development occurs in different corners of enterprises that are close to business using self-service tools like Excel.

The local teams engaged in distributed analytics creation play a vital role in bringing creative innovations to users, but as said earlier, lack of control pushes you down towards the pitfall of tribal behaviors; thus, centralizing this development under it enables standardized metrics, better data quality, and ensures proper monitoring. Too much rigidness chokes creativity, but with it, it is easy to move into and for motion between centralized and decentralized development; hence, any concept can be scaled more robustly and efficiently.

Set up Enterprise-level DevOps Ability 

Most organizations have completed or are iterating over the process of building Agile and DevOps capabilities. Data Analytics teams should join hands and leverage the enterprise’s Agile and DevOps capabilities to Latest trends of DataOps.

• The transit from a project-centric approach to a product-centric approach (i.e., geared toward analytical outcomes).
• Establish the Orchestration (from idea to operationalization) pipeline for analytics.
• automated process for Test-Driven Development
• Enable benchmarks and quality controls at every stage of the data value chain.

Automation and Infra Stack 

One of the primary services that it provides is to scale your infrastructure in an agile and flexible manner to meet ever-changing requirements at scale. The integration of commercial and open-source tools and hosting environments enables the enterprise to automate the process and scale Data & Analytics platform services. For example, it provides infrastructure automation for

1. Data warehouses
2. BI platforms
3. Data lakes
4. Data catalogs
5. Machine learning infrastructure

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Orchestrate Multi-layered Data Architecture

Modern-day data platforms are complex with different needs, so it’s essential to align your data platform with business objectives to support vast data processing and consumption needs. One of the proven design patterns is to set up multi-layered architecture (raw, enriched, reporting, analytics, sandbox, etc.), with each layer having its value and meaning and serving a different purpose and increasing the value over time. It is also essential to register data assets across various stages to support and bring out data values by enabling enterprise data discovery initiatives. Enhance and maintain data quality at different layers to build assurance and trust. Protect and secure the data with security standards so providers and consumers can safely access data and insights. Scale services across various engagements and reusable services

Building end-to-end Architecture 

Workflow orchestration plays a vital role in binding together the data flow from one layer to another. It helps in the automation and operationalization of the flow. Leverage the modularized capabilities to Key pipelines supported by it are:

• Data Engineering pipelines for batch and real-time data
• Common services such as data quality and data catalog pipeline
• Machine Learning pipelines for both batch and real-time data
• Monitoring reports and dashboards for both real-time data and batch data

Monitoring and Alerting frameworks Provide provision of building monitoring and alerting frameworks to continuously measure how each pipeline reacts to changes and integrate them with infrastructure to make the right decisions and maintain coding standards.

Benefits of DataOps

The main aim of it 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. It is a growing concept that seeks to balance innovation and management control of the data pipeline. Besides the benefits, it extends 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 quality assurance 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, it 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.

Benefits of DataOps as a service

Listed below are the benefits:

1. Simplify complex data analytics orchestrations and operations.
2. Automated processes and attain more value from them.
3. It connects organizations in two ways from development to operations and from localized development to centralized development.
4. Reduce the life cycle of data processing, cleaning, and loading.
5. Increases the value of data by increasing the quality of data.
6. Reduce time and cost of data by automating the redundant and reusable process.

DataOps Methodology is a new and independent view approach to data analytics based on the whole data life cycle. Managed Services

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.

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 Latest trends of DataOps

• Self-service predictive analytics
• Security and data governance
• DataOps and Data Mesh
• Automation and hyper-automation

Summing up

1. Data Operation focuses on collaborating agility to Machine Learning and Analytics projects.
2. Data Operatconnectsnect key data sources right away, unlike Salesforce, customer record data, or what we've identified.
3. It 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 its Solutions for Enterprises!

• Read here about Data Management Best Practices with DataOps
• Explore more about Master Data Management in Banking Sector