Data Management

The Top 5 Data Operations (DataOps) Tools

Discover the top DataOps tools with features like data integration, analysis, cleaning, and automated testing.

Last updated on Jun 24, 2024

Written by Mirren McDade

Technical review by Laura Iannini

The Top 5 DataOps Tools include:

1. Atlan Active Data Governance
2. DataKitchen
3. Hitachi Pentaho Intelligent DataOps Platform
4. IBM DataOps Platforms
5. Nexla Unified Data Operations

Better data management leads to better – and more readily available – data. Increased quantity and improved quality of data results in superior analysis, which translates into more in-depth business insights, comprehensive business strategies, and better profitability. The goal of data operations is to nurture collaboration between data scientists, engineers, and technologists to ensure that all teams are working in sync to leverage data quicker and more appropriately.

The term DataOps combines ‘data’ and ‘operations’, and represents an agile, collaborative, and automated methodology. These platforms are designed to streamline the process of data management, allowing teams to rapidly access, clean, and analyze vast data sets, thereby enhancing decision-making and fostering innovation.

DataOps platforms prioritize the security and governance of data, implementing robust access controls, encryption standards, and audit trails. This is especially pertinent in an era where regulatory scrutiny around data protection and usage is intensifying. Many organizations are subject to stringent data protection laws, and DataOps tools can assist in maintaining compliance while ensuring data is readily available for analysis.

As the demand for real-time data insights grows, so does the need for efficient DataOps tools. Given the vast and varied nature of the data landscape, it is unsurprising that there are many excellent vendors offering data operations solutions, each with their own strengths and feature sets. To help you navigate this ever-evolving ecosystem, we have compiled a list of the top DataOps tools. Throughout this guide, we will delve into the standout features of each tool, drawing on in-depth market research and user feedback.

Atlan Active Data Governance

Atlan Active Data Governance streamlines and simplify the complex processes often associated with data governance. Users can automatically identify sensitive data that falls under regulations like PII, HIPAA, and GDPR, and tag these data assets with custom classifications. Additionally, the platform offers capabilities for the customization of access policies, allowing for the creation of rules that reflect the individual roles and project contexts within your business.

Atlan also includes capabilities for managing metadata policies, data policies, and glossary policies, helping to define who can view, query, or edit various data assets and metadata. Policy management extends to role-based access, where personalized user roles can be established to manage access and permissions more effectively.

Atlan Active Data Governance facilitates collaborative efforts through its requests feature, where members can suggest modifications or improvements and admins have the discretion to approve or reject these suggestions. Atlan enables the development of compliance-based access policies, making it easier to secure data according to specific classifications and ensuring adherence to regulatory requirements.

DataKitchen

DataKitchen’s DataOps platform streamlines and automates of data operations. This platform facilitates the journey of data both in development and production phases. The core features of the DataKitchen platform encompass data observability and automation functionalities. It grants detailed insights into the state of data operations within your organization, tracking process metrics such as collaboration enhancement, productivity improvement, error reduction, and timely project completion. The solution automatically identifies and resolves data errors and bottlenecks permanently and automates various functions across testing, deployment, and orchestration for different technologies.

DataKitchen features a flexible integration system, compatible with a vast range of native tooling integrations, and offers methods for incorporating new tools as they become available. This ensures that teams can utilize the tools they are accustomed to, whilst enhancing their capacity to deliver accurate and quick customer insights. The solution’s observability solution provides a comprehensive Mission Control Center, providing a centralized view to monitor and manage all organizational data pathways, from data sourcing to value delivery, thereby reducing errors and streamlining processes.

Hitachi Pentaho Intelligent DataOps Platform

Hitachi’s Pentaho Intelligent DataOps Platform streamlines data management and operational processes for multicloud environments. This platform facilitates intelligent data management, fostering enhanced business optimization and improved customer experiences by providing a unified solution for data discovery, observability, and classification. It provides secure access to trusted data, promoting data connectivity and trustworthiness.

This solution includes multiple key DataOps features, including automated data discovery, which aims to lower risks and costs associated with data privacy and compliance. The Pentaho Data Integration & Analytics feature allows for seamless data management, facilitating integration and embedding powerful analytics into daily workflows. The Pentaho Data Catalog feature enables profiling, discovery, privacy, and search features on trusted data. Pentaho Data Storage Optimizer works to minimize infrastructure costs by employing intelligent data tiering solutions, while the Hitachi Content Intelligence component specializes in unstructured data discovery and transformation, aiming to unveil insights swiftly to enhance business intelligence.

IBM DataOps Platforms

IBM’s DataOps Platforms are suite of tools engineered to streamline the data acquisition and management process for organizations. These platforms foster a harmonious collaboration between data-focused and development-focused teams, ensuring the seamless flow and effective management of data throughout its lifecycle. Through the facilitation of real-time goal-setting and performance updates, the platforms work to enhance the agility, accuracy, and efficiency of your data.

The core offerings of the IBM DataOps platforms consist of a series of robust tools such as the IBM Knowledge Catalog, a data catalog that enables users to access data effectively for business operations and analytics. IBM DataStage is a scalable data integration tool adept at moving and transforming data and IBM Cloud Pak is a flexible multi-cloud data platform that integrates data securely from varied sources. IBM Databand is a cloud-native data observability platform, that assists data teams in detecting and resolving data incidents swiftly.

IBM’s DataOps platforms provide a unified, reliable view of data, facilitating superior data quality and enabling the transformation of raw data into usable information. Through automated metadata and policy management, data cataloging is simplified. Through IBM DataOps platforms, organizations can expect secure, compliant, and cost-efficient data management solutions, driving insightful business operations and analytics.

Nexla Unified Data Operations

Nexla provides streamlined data management through its comprehensive platform. This solution offers a seamless environment to integrate, transform, monitor, and provision data without the friction commonly encountered in traditional settings. The solution’s adaptive integration engine seamlessly manages data flows from multiple sources without necessitating the creation of new connectors with each addition. This engine not only recognizes and adapts to your data schemas, but also keeps track of their evolution, ensuring a consistent and high-quality data stream for your business.

Nexla’s Unfied Data Operations Platform provides continuous data validation and error management, with automated monitoring and notifications. This solution enhances the efficiency and scale of data teams through the implementation of low/no-code automated workflows, reducing dependence on data engineering teams for each request. The platform supports enterprise-level collaboration and user management, complete with features such as secure single sign-on, automated versioning, and comprehensive audit trails, providing an agile and governed approach to data operations.

The Top 5 Data Operations (DataOps) Tools

Everything You Need To Know About DataOps Tools (FAQs)

What Are DataOps Tools?

Data Operations (or DataOps) is the set of practices, processes, and technologies that enable consistent, automated, and secure data management. Data Operations takes an agile approach to designing, implementing, and maintaining a distributed data architecture capable of supporting a variety of open source tools and frameworks in production. The DataOps strategy was inspired by the DevOps movement, and aims to streamline the production of applications that run on big data processing frameworks. It also works to remove silos across IT operations, data management, and software development teams, as well as offer encouragement to line-of-business stakeholders to work with data scientists, data engineers, and analysists in order to ensure that the data from an organization can be utilized in the most effective and flexible manner to achieve strong business outcomes.

Data operations tools are software solutions or sets of tools that are designed to help facilitate and streamline an organization’s data management, data quality, data integration, and data analytics to effectively management the various aspects of the data lifecycle, from data ingestion right through to analytics and reporting. These tools are typically utilized by data engineers, data scientists, data analysists, and other data professionals. They use these solutions with the aim of improving data related processes, boosting data quality, and supporting the acceleration of data-driven decision making.

How Do DataOps Tools Work?

Data Operations tools merge DevOps and Agile methodologies in order to properly manage and align business goals. They aim to cultivate data management practices and processes that work together to improve the speed and accuracy of analytics, which include data access, automation, quality control, integration, and, ultimately, model deployments and management.

The DataOps frameworks is made up of four main phases, which include:

Data preparation
Data ingestion
Data processing
Data analysis and reporting

These tools play an important role in modern data management due to how they provide a way for organizations to harness the full potential of their data assets while ensuring that data integrity and compliance is maintained. At their core, DataOps tools work to align the way you manage your data with the goals you have for that data’s use.

What Features Should You Look For When Choosing A DataOps Tool?

Key features of DataOps include:

Data integration. This is the process of bringing data together from different sources into one singular unified view, which begins with the ingestion process and follows several steps including cleansing, ETL mapping, and transformation. Ultimately, data integration allows analytics tools to produce effective, actional business intelligence.
Real-time data processing. This deals with streams of data that are captured in real-time and processed with as little latency as possible to generate real-time (or as close as possible to real-time) reports or automated responses.
Automated testing. This is a technique for executing test cases without requiring any human intervention and help to test and compare the expected output with the projected output, as well as save time and increase test coverage. DataOps tools often provide testing frameworks for data pipelines, ensuring that no errors are included in the changes made.
Collaborative workflow management. This is where social software and service management (workflow) software converge, and is the combination of efforts from employees, managers and supervisors to reach a common objective. DataOps tools facilitate collaboration among data teams and support workflow orchestration, role-based access control, and collaborative features like notifications and comments.

Mirren McDade

Senior Journalist & Content Writer

Mirren McDade is a senior writer and journalist at Expert Insights, spending each day researching, writing, editing and publishing content, covering a variety of topics and solutions, and interviewing industry experts. She is an experienced copywriter with a background in a range of industries, including cloud business technologies, cloud security, information security and cyber security, and has conducted interviews with several industry experts. Mirren holds a First Class Honors degree in English from Edinburgh Napier University.

Laura Iannini

Cybersecurity Analyst

Laura Iannini is an Information Security Engineer. She holds a Bachelor’s degree in Cybersecurity from the University of West Florida. Laura has experience with a variety of cybersecurity platforms and leads technical reviews of leading solutions. She conducts thorough product tests to ensure that Expert Insights’ reviews are definitive and insightful.