DataOps - Solutions from Infra to Application
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.
The thought for its 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. It 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 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 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 DataOps Principles?
Steps to adopt DataOps Principles are:
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 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.
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.
Why DataOps is important for Enterprises?
- 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.
What are the 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:
- 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.
- Improves quality assurance through the provision of "production-like data" that enables the testing to exercise the test cases before clients encounter errors effectively.
- It helps organizations move safely to the cloud by simplifying and speeding up data migration to the cloud or other destinations.
- 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.
- 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:
- Simplify complex data analytics orchestrations and operations.
- Automated processes and attain more value from them.
- It connects organizations in two ways from development to operations and from localized development to centralized development.
- Reduce the life cycle of data processing, cleaning, and loading.
- Increases the value of data by increasing the quality of data.
- Reduce time and cost of data by automating the redundant and reusable process.
What is the framework of DataOps?
DataOps combines Agile methodologies, DevOps, and lean manufacturing concepts. Let's see how these concepts relate to the DataOps framework.
Agile Methodology
This methodology is a commonly used project management principle in software development. Agile development enables data teams to complete analytical tasks in sprints. Applying this principle to his DataOps allows the team to re-evaluate
their priorities after each sprint align with business needs, thus delivering value much faster. This is especially useful in environments with constantly changing requirements.
DevOps
DevOps is a set of practices used in software development to shorten the application development and delivery lifecycle to deliver value faster. This includes collaboration between development and IT operations teams to automate software delivery from code to execution. But DevOps involves two technical teams, whereas DataOps involves different technical and business teams, making the process more complex.
Lean Manufacturing
Another component of the DataOps framework is lean manufacturing, a way to maximize productivity and minimize waste. It is commonly used in manufacturing operations but can also be applied to data pipelines.
Lean manufacturing allows data engineers to spend less time troubleshooting pipeline issues.
Best Practices for Effective DataOps Framework
Organizations should embrace the best data pipeline and analytics management practices to make the DataOps architecture effective. This includes:
- Automation
- Implementing continuous integration and delivery procedures
A critical best practice in DataOps, continuous integration and delivery (CI/CD) enables enterprises to test and apply improvements to their data operations rapidly and efficiently.
CI/CD enables teams to make data-driven decisions more quickly while also assisting organizations in reducing errors and improving data quality.
- Managing Data Quality Metrics
Data quality management is another practice in DataOps, as it ensures that data is accurate, complete, and relevant for decision-making and analysis. Data quality management helps organizations to improve data quality, reduce errors, and make more informed data-driven decisions.
- Data Security
Since extensive data often contain sensitive and private information, data security is an essential best practice in big data dataops. Data security enables businesses to safeguard their information against illegal access, modification, and misuse while ensuring that data is maintained and used per organizational, legal, and regulatory standards.
A new and independent view approach to data analytics based on the whole data life cycle. Taken From Article, Implementing DataOps Architecture in AWS, Azure, and GCP
What is the architecture of DataOps?
DataOps is an approach to data management that emphasizes collaboration, automation, and the continuous delivery of high-quality data to stakeholders. A DataOps architecture typically includes the following components:
- Data Ingestion: This component involves collecting data from various sources such as databases, APIs, and streaming platforms. Data acquisition can be done by batch processing or real-time streaming.
- Data Processing: After data is ingested, it must be transformed into a form that can be analyzed. This component includes data cleaning, filtering, aggregation, and enrichment.
- Data Storage: After processing, data should be stored to allow for efficient retrieval and analysis. Data storage can be done using various technologies, such as data warehouses, data lakes, and NoSQL databases.
- Data Quality: This component includes ensuring the accuracy, completeness, consistency, and timeliness of data. Data quality can be maintained through profiling, validation, and monitoring.
- Data Governance: Data governance involves establishing policies, procedures, and standards for data management. This component includes data security, privacy, and regulatory compliance.
- Data Analysis: The final component involves analyzing data to generate insights and support decision-making. This can be done using various analytical tools and techniques such as data visualization, machine learning, and statistical analysis.
Overall, the DataOps architecture emphasizes a collaborative and automated approach to data management that ensures timely and efficient delivery of high-quality data to stakeholders.
What are the Best Practices of DataOps?
- Versioning
- Self-service
- Democratize data
- Platform Approach
- Be open source
- Team makeup and Organisation.
- Unified Stack 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 Stack- leverage data locality.
- Automation saves time. Thus, automate wherever possible.
What are the latest trends of DataOps?
- Self-service predictive analytics
- Security and data governance
- DataOps and Data Mesh
- Automation and hyper-automation
What are the different DataOps Tools?
Some of the best DataOps tools are:
Composable.ai
DataOps is an Analytics-as-a-Service 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 with AI.
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 services 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.
Makes sure data follows both internal and external mandates and data is secure, private, accurate, available, and usable. Taken From Article, Data Governance
How DataOps helps Enterprises to build Data-Driven Decisions?
DataOps as a Service is offered as a combination of multi-cloud big-data/data-analytics management and managed services around harnessing and processing the data. It provides a scalable, purpose-built big data stack that adheres 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 the 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 Stack services. For example, it provides infrastructure automation for
- Data warehouses
- BI Stack
- Data lakes
- Data catalogs
- Machine learning infrastructure
Orchestrate Multi-layered Data Architecture
Modern-day AI Led Data stacks are complex with different needs, so it’s essential to align your data strategy 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.
Summing up
Companies nowadays are investing a lot of money to execute their IT operations in a better way. It 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:- Data Operation focuses on collaborating agility to Machine Learning and Analytics projects.
- Data Operatconnectsnect key data sources right away, unlike Salesforce, customer record data, or what we've identified.
- It simplifies the processes and maintains a continuous delivery of insights.
- 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 Architecture and Best Practices of Master Data Management
- Explore more about Master Data Management in Banking Sector
- Know more about DataGovops Advantages and Key Components
