Introduction

Data is the new norm when it comes to adapting to new technology and innovations. It happens to be the “Mya” of innovations and acts as leverage in having a jump-start ahead of the competitors in the industry.

However, few enterprises find it difficult to derive actionable insights as there happen to be discrepancies between the organization’s goal and the quality of data they produce. This is where DevOps first came into place, solving organizational problems with technical solutions i.e. bringing together people who build software to people who develop and run it.

This solved one side of the problem but as big data emerged, millions of records were collated of which few were disorganized, incomplete, and few even made no sense. This complexity in data grew as it was diverse and came uncleaned, and disorganized from different sources. To top this, enterprises started working with a lot of data and BI tools, and the people who work on it were diverse and came from different backgrounds.

Brining order, speed, and legitimacy towards the entire organization starting from the data operations to business reporting is why DataOps came into existence.

What is DataOps?

DataOps has grown to be the new independent approach for data analytics. Bringing together a number of tools and various levels of people in the organization into a common ground for better organization and development of data is called DataOps.

DataOps is mostly about the interconnected nature from design to development of data. This process involves a proper framework of operations between Data Analysts, Data Scientists, Developers, and Operationalists with the transformation of data, and delivering fast and insightful analytics.

The Ideologies behind DataOps

• Agile methodology
• Agile refers to several methodologies that focus on the step by step, iterative process. These experimentations would expect teams to get tangible products and features out quickly at the end of every sprint. This strategy has been widely implemented in various domains and similarly, data analytics also seems to have benefited from it. It is not a prescriptive solution for enterprises rather it’s a strategy of working with the data.
• With Agile methodology, the users are in line with the data and development teams. These iterations are kept short, consistent validation from the users and stakeholders in the form of feedback for every iteration helps teams to never drift away from the target.
• One of the key traits of Agile analytics is automating any process that is done more than once. This involves test automation, which enables users to revalidate that everything is running as expected and build validation, which enables users to revalidate new versions of the software or feature in an automated fashion.
• Lean manufacturing
• Lean manufacturing is deriving raw data and transforming them into data of high efficiency. A statistical process control method where the quality of data is improved exponentially by filtering out the data that serves no purpose.
• The more useless data is eliminated, the more legitimate data identified. This helps the data team avoid unnecessary efforts on data cleansing, transformations, modeling, analysis, and also on the analytics part. This saves time and extensively increases the reliability of the data and the insights it produces.
• These data quality checks can be automated, the data citizens in your organization can model a filter on what sort of data can enter the system, this automatically allows just the valid data that’s been defined by the data team.
• DevOps
• DevOps is a practice where development, operation, and business teams work in parallel to extract the best quality of outcome in a shorter span of time. A methodology that helps enterprises meet rapidly changing market demands.
• DataOps applies DevOps technologies to transform data insights into production deliverables. These technologies include having real-time monitoring which helps in optimizing the data pipelines. This seamlessness in implementing the inputs provided by users and business teams is with the help of DevOps principles.
• DevOps principles include aligning people with their goals and bringing automation throughout the development process. DataOps incorporates these principles to improve the efficiency of the data cycle and brings a goal-oriented approach throughout the organization by defining roles for every data citizen.

How DataOps works

DataOps combines DevOps and Agile processes to manage data to meet business goals. For instance, it can be used to improve the lead conversion rate by optimizing marketing and product recommendations. DevOps processes optimize code, product builds, and delivery, while Agile processes manage data governance and analytics.

DataOps consists of more than just writing code; improving and streamlining the data warehouse is just as essential. Like the Lean Manufacturing process, DataOps utilizes Statistical Process Control (SPC) to monitor and maintain the data analytics pipeline. This ensures the statistics remain within reasonable parameters, optimizes data processing, and enhances data quality. SPC also allows for immediately detecting anomalies and errors, alerting data analysts to act quickly.

Benefits of DataOps

Adopting a DataOps strategy can offer organizations many advantages, such as:

• Provides trustworthy real-time data insights.
• Reduces the cycle time of data science applications.
• Enhances team and individual interaction.
• Utilize data analysis to increase transparency and anticipate all potential outcomes.
• Establishes systems that can be repeated and reuse code whenever feasible.
• Ensures better quality data.
• Creates a unified, interoperable data hub.

The roles and people behind DataOps:

To begin a data-driven culture within the organization, the leaders who drive transformation must define the roles played by each and every employee, and how their contributions would reflect on the goals set towards a successful DataOps practice.

The contribution of data might be from various levels of teams across the organization in the form of data. However, Data Architect, Data Engineer, Data Analyst, and Business Users are the ones that play a vital part in DataOps practices, right from collating the raw data to transforming them into actionable insights.

Implementing DataOps helps enterprise overcome these challenges

• Inefficiencies in the data
• Error-free data gives error-free analytics. Before carrying on with the analytics, the collated data needs to be checked and made sure if it’s legit or not. This is possible only by cleansing, organizing, transforming, and modeling the data to see if they produce insight of any use.
• In order to tackle the collection of unnecessary data, the garnered data can be put under a series of data quality checks which filters out data that serve no use to the pipeline flows and models the organization works with. DataOps’s Lean principles help to decrease the volume of data collated and also improves the quality of it.
• Deployment difficulties due to limited collaboration
• Too often, the development teams solely face the burden of fixing bugs and deploying changes as it is a time-critical process. In such scenarios, limited collaborations result in siloed communication and sending requests back and forth between teams which cause operational delays.
• DataOps practice enables the Data team, Development team, Engineering team, and IT operations team to work together. Managing tickets based on priority and frequent deployment with real-time feedback within the teams and also with the users leads to successful DataOps practice.
• Asynchronous goal-setting
• When implementing DataOps’s Agile practice, working with new product updates and user tickets are performed in the form of sprints. These sprints are scrutinized every now and then where constant feedback is given from both the management heads and also from the users.
• Post the sprints, as spontaneous the organization understands the issues, as easy it would be to make few tweaks or push the same practice to a greater extent. This real-time feedback loop helps organizations study and rectify errors as soon as possible. This not only hands the users a working feature or fixes some bugs after every sprint, but this also allows teams to re-evaluate these changes and set goals in real-time.

The top 10 Dataops tools in 2023

Census

Census is the ideal platform for operational analytics, with reverse ETL (extract, transform, load), offering businesses a secure source for their warehouse data to be used in day-to-day applications. It integrates with existing data ops tools, eliminating the need for custom scripts or IT intervention. Its security, performance, and dependability is why many modern organizations are turning to Census.

Delphix

Delphix is one of the top 10 data ops tools that offers a comprehensive, intelligent data platform. It helps leading companies worldwide accelerate their digital transformations by supporting mainframes, Oracle databases, ERP applications, and Kubernetes containers. Delphix also automates data compliance for privacy regulations, like GDPR, and provides a wide range of data operations to facilitate modern CI/CD workflows.

Tengu

Tengu empowers enterprises to become data-driven and boost their business by improving dataset usability, access, and efficiency. It supports scientists and engineers in speeding up the data-to-insights cycle, allowing them to manage the complexity of a data-driven company. With Tengu listed among the top data ops tools, companies can easily manage their data.

SuperbAI

Superb AI's machine learning data platform accelerates AI development with fewer resources. The enterprise SaaS Suite supports ML engineers, product teams, researchers, and data annotators to create productive training data workflows, maximizing efficiency and minimizing cost.

Unravel

Unravel simplifies data operations across Azure, AWS, GCP, and on-premises environments – optimizing performance, automating troubleshooting, and controlling costs. Monitor, manage, and improve your data pipelines in the cloud and on-premises to make your applications more reliable. Unravel provides a unified view of your entire data stack, collecting performance data from all platforms, systems, and applications on any cloud and modeling your data pipelines with agentless technologies and machine learning.

Mozart Data

Mozart Data is an easy-to-use data platform that simplifies data organization and prepares it for analysis without specialized knowledge. It can take any size or complexity of disorganized, isolated, and chaotic data and prepare it for use. It also provides a web-based interface allowing data scientists to work with data in CSV, JSON, and SQL formats.

Databricks Lakehouse Platform

The Databricks Lakehouse Platform is one of the best data management solutions, featuring a unified platform for data warehousing and AI applications. Accessible through web-based, command-line, and SDK interfaces, this comprehensive suite includes five modules: Delta Lake, Data Engineering, Machine Learning, Data Science, and SQL Analytics. Data scientists, engineers, and analysts can collaborate efficiently in this single workspace.

Datafold

Datafold prevents data catastrophes in businesses. Its exclusive technology detects, evaluates, and investigates data quality issues before they affect productivity. Datafold also monitors data in real-time to identify problems early and stop them from becoming disasters.

dbt

dbt is an open-source transformation workflow that enables enterprises to deploy analytics code quickly, leveraging software engineering best practices such as modularity, portability, CI/CD, and documentation. It is a command-line tool that enables users familiar with SQL to create high-quality data pipelines.

Apache Airflow

Airflow is an open-source platform used to create, schedule and monitor workflows. Its modular architecture supports a message queue, enabling the scaling of pipelines to an infinite level. Pipelines are designed in Python code, allowing users to construct pipelines dynamically.

DataOps trends and future outlook

DataOps' future is being propelled by three trends: integration, augmentation, and observability.

Increased integration with other data disciplines. DataOps are becoming more interconnected, with Gartner promoting its related data management practices - MLOps, ModelOps and PlatformOps. MLOps focuses on machine learning development and versioning, whilst ModelOps centres around model engineering, training, experimentation and monitoring. Gartner views PlatformOps as a holistic AI platform that entails elements of DataOps, MLOps, ModelOps and DevOps.

Augmented DataOps. AI is enabling efficient data infrastructure management. Augmented data catalogs and analytics, infused with AI, are replacing traditional versions. This technique will eventually be used in all parts of the DataOps process.

Data observability. DevOps teams have been using APM tools enabled by observability infrastructure to identify and prioritize app issues for years. Vendors like Acceldata, Monte Carlo, Precisely, Soda and Unravel are now creating observability tools focused on data infrastructure. Further, DataOps tools utilise data observability to enhance DataOps processes through development, integration, partnerships, and acquisitions.