11 Best Tools for DataOps That Create Business Value from Data

11 Best Tools for DataOps That Create Business Value from Data
8 min read

The data horizons are expanding rapidly, and the amount of data being generated by organizations worldwide is increasing by a great margin. The need for analyzing data and extracting insightful information from it is also increasing by leaps and bounds. Data can be analyzed and optimally utilized through effective DataOps tools. 

DataOps is a modern-day concept that plays an important role to optimize and streamline data management approaches. And DataOps tools are the facilitators that help in implementing DataOps to its best. Before we go into details of the tools, let us quickly go through what is DataOps.

DataOps – A Quick Look

DataOps is a set of practices, processes, and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics.

DataOps, an abbreviated form for Data Operations, is a popular agile approach to designing, implementing, and maintaining a distributed data architecture, following several tools and frameworks, to get more business value from data. It works well to fasten the production of apps that are being implemented on huge frameworks.

It lessens the gaps between the teams – data management, software development, and IT teams to achieve fine utilization of the organizational optimal manner. The perception of DataOps is conceived on the basic four fundamental concepts of Lean, Product Thinking, Agile, and DevOps, for developing the performance, quality, and speed of data-driven activities.

An Interesting Read, To Know More:  DataOps: A Comprehensive Guide to Principles, Benefits, and More

Top DataOps Tools to Look For

  • Genie

Genie is a popular DataOps tool, created by Netflix. Being an open-source tool, it provides effective and distributed orchestration services. It offers different APIs, commands, and applications to manage the metadata of distributed processing clusters. It offers a great deal of scalability for its client resources. It makes the best use of a group of machines that can be updated based on the need of the hour.

Key Features

    • Scalable and flexible
    • RESTful APIs for big data jobs
    • Job and resource management
    • Abstracts physical details of resources
    • Configuration APIs for cluster registration
  • Datafold

Datafold is a known name in the world of DataOps tools and is well known for its data observability. It is one of the fastest mechanisms for the validation of model updates while different phases are on while the software development lifecycle goes on. It assists users in tracing data flows for avoiding data abnormalities and data outages before the production stage. 

Key Features

    • Tracing data flows across columns
    • Finding out data quality issues
    • Testing of ETL code and finding changes
    • Seamless integration with data warehouses
    • Pre-emptive data testing
  • StreamSets

StreamSets is a well-known data integration platform for enterprise organizations. It lets users create, design, and implement data pipelines for real-time data analytics. Users can get a live map along with their respective metrics and drill-down details. There is a facility for deployment on-premises or in the cloud with data streaming from disparate data sources. 

Key Features

    • Real-time data transformation
    • Supervising data pipeline performance
    • Collaboration between teams and design components
    • Data integration for hybrid & multi-cloud environments
    • Elimination of data integration conflict
  • Piper

Piper is an ML-based DataOps tool that operationalizes the data life cycle with data apps. It consists of a collection of apps that are meant for the modern-day data environment. It extracts good quality data with its inbuilt algorithms, especially for enterprises, smoothly and effectively. There is a seamless merger of batch and real-time data with optimal technical support. 

Key Features

    • Minimal data turnaround time
    • Pre-defined data apps
    • Flexibility to select own trained models
    • Dashboard with attractive chart facility
    • Data exposure through APIs
  • Chaos Genius

Chaos Genius is a strong DataOps observability tool that leverages ML and AI for accurate cost estimates and advanced metrics for data monitoring. It lessens Snowflake costs with query and workload optimization, maximizing its RoI and increasing the total performance of the warehouse. It is preferred by enterprises and businesses of all sizes because of its flexible way of working. 

Key Features

    • Cost allocation and visibility
    • Alerts and reporting
    • Database optimization
    • Augmented query performance
    • Reasonable model of pricing
  • Airflow

Powered by Apache, Airflow is a fully open-source DataOps tool that effectively creates, manages, and schedules complicated workflows as Directed Acyclic Graphs (DAG). Users can handle data pipelines with simplicity on macOS, Windows, and Linux. There is a huge community that supports and offers multiple plug-ins, integrations, and connectors making it a flexible tool.

Key Features

    • Dynamic pipeline generation
    • Usage of custom operators, plug-ins, executors
    • Inbuilt scaling of multiple tasks
    • Orchestration of data pipelines
    • Cloud-native data architecture
  • K2View Fabric

K2View Fabric is a popular DataOps tool that brings out rule-based services for applying data governance policies to newer data pipelines. There is an operational data fabric that makes the user experience a memorable one. The Data Product platform creates a novel dataset for all different entities in real time. It is in synchronization with the data sources and available to all users.

Key Features

    • Compressed and encrypted micro database 
    • Rule-based services for data governance
    • Personalized experience
    • Centralized information in a single repository
  • Tengu

Tengu is a leading DataOps orchestration platform for data-driven organizations that empowers data scientists and engineers to enhance their efficacy levels and understand business complexities. It helps businesses in boosting their revenue by accessing data in the right way. It offers data integration and ETL process with which users can do data monitoring. 

Key Features

    • Faster integration with disparate sources
    • Scheduled pipeline orchestration
    • Strong collaboration competencies
    • Insightful graphs 
  • Composable DataOps

Composable DataOps is a popular Analytics-as-a-Service tool and a DataOps tool that offers a comprehensive solution for data application management. Users can perform data integration in real time from disparate sources with its low code development platform. It can undergo robust analytics and transformation in the cloud with major cloud service providers.

Key Features

    • On-premises and cloud deployment
    • Automatic data pipelines
    • The interactive query for writing SQL code
    • Robust dashboard interface for visualization
  • RightData

RightData is a good choice for DataOps since it offers effective data testing, reconciliation, and validation. This ensures effective data reliability, quality, and stability. It is a self-service tool that does all data-related processes and offers great results. It does data transformation in real-time via data pipelines and avails of the latest data insights. 

Key Features

    • Data migration from legacy systems to modern sources
    • Real-time data transformation
    • Easy to access the latest information
    • Creating effective pipelines
  • Unravel

Unravel, as a DataOps tool, offers full-stack visibility for optimizing the performance of applications. It makes use of AI-driven components for comprehensive data observability. It supports Hadoop tools like Cloudera Manager etc. It helps teams better monitor and optimization of the complete data stack for enhanced performance. 

Key Features

    • Big data application performance monitoring
    • Cost optimization
    • Insight into key metrics and recommendations
    • Predictive suggestions

On a Concluding Note

Choosing the ideal DataOps tool depends on various factors including timelines, access to key resources, project deadlines, budget estimates, availability of resources, etc. Whichever one you choose; it depends upon the organizational requirements and the above list is based on that. 

Ridgeant’s data analytics consulting assists in unlocking actionable insights and evolving into a data-driven organization to optimize performance and enhance growth. As a competent IT service provider, we offer data visualization services, analytics and BI modernization, BI implementation support, data mining, self-service BI, and more.

Contact us for the implementation of the latest and trending procedures like DataOps; we will be happy to help.

 

Note: This Post Was First Published On https://ridgeant.com/blogs/dataops-tools/

 
 
 
 
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Dipak Shah 2
Joined: 11 months ago
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