DataOps Tools: Key Capabilities & 5 Tools You Must Know About

DataOps, short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. DataOps tools are software solutions designed to simplify and streamline the various aspects of data management and analytics, such as data ingestion, data transformation, data quality management, data cataloging, and data orchestration.

These tools help organizations implement DataOps practices by providing a unified platform for data teams to collaborate, share, and manage their data assets. By using DataOps tools, organizations can break down silos, reduce time-to-insight, and improve the overall quality of their data analytics processes.

DataOps tools can be categorized into several types, including data integration tools, data quality tools, data catalog tools, data orchestration tools, and data monitoring tools. Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively.

In this article:

• Why Are DataOps Tools Important?
• Key Features of DataOps Tools 
• Notable DataOps Tools
• Conclusion

Here are some of the reasons why DataOps tools are important:

Improved Collaboration

DataOps tools enable better collaboration between data teams, including data engineers, data scientists, and data analysts. These tools provide a common platform for these teams to work together, share insights, and make collective decisions. Improved collaboration leads to faster decision-making and more accurate insights, ultimately driving better business outcomes.

Accelerated Data Analytics

DataOps tools help automate and streamline various data processes, leading to faster and more efficient data analytics. By automating time-consuming tasks like data ingestion, data transformation, and data quality management, DataOps tools enable organizations to reduce the time it takes to generate insights from data, to make faster, more informed decisions.

Enhanced Data Quality

Data quality is a critical factor in the success of data analytics projects. Poor data quality can lead to incorrect or misleading insights, which can have significant consequences for an organization. DataOps tools help ensure data quality by providing features like data profiling, data validation, and data cleansing. By improving data quality, organizations can generate more accurate and reliable insights from their data analytics processes.

Learn more in our detailed guide to DataOps architecture (coming soon)

DataOps tools come with a variety of features designed to optimize and streamline data processes. Some DataOps tools focus on one of these capabilities, or several of them, while others aim to provide a unified platform that addresses all of them.

Data Integration

Data integration is the process of collecting, transforming, and consolidating data from various sources. DataOps tools should provide seamless data integration capabilities to help organizations manage their data pipelines more effectively. Key features to look for in data integration tools include support for various data sources, data transformation capabilities, and data ingestion options, such as batch or real-time processing.

Data Quality Management

Data quality management features help organizations maintain the accuracy, consistency, and completeness of their data. DataOps tools should offer robust data quality management capabilities, such as data profiling, data validation, and data cleansing. These features enable organizations to identify and resolve data quality issues, ensuring the reliability of their data analytics processes.

Data Cataloging

Data cataloging is the process of organizing and categorizing data assets to make them easily discoverable and understandable for data teams. DataOps tools should provide a comprehensive data cataloging solution that allows organizations to create a centralized repository of their data assets, complete with metadata, data lineage information, and data samples. This enables data teams to quickly and easily find the data they need for their analytics projects.

Data Orchestration

Data orchestration refers to the coordination and management of data workflows, from data ingestion to data processing and analysis. DataOps tools should offer powerful data orchestration capabilities, allowing organizations to build, schedule, and monitor data workflows with ease. Key features to look for in data orchestration tools include support for various data processing engines, visual workflow builders, and real-time monitoring and alerting.

Data Monitoring

Data monitoring is essential for ensuring the ongoing health and performance of data processes. DataOps tools should provide comprehensive data monitoring capabilities, enabling organizations to track the performance of their data workflows and identify issues before they impact business operations. Key features to look for in data monitoring tools include real-time monitoring, customizable dashboards, and alerting and notification options.

Learn more in our detailed guide to DataOps framework (coming soon)

DataOps features: data monitoring, data validation, anomaly detection, integration with data processing frameworks.

IBM Databand is a powerful DataOps tool designed to help data teams manage, monitor, and optimize their data pipelines. With its advanced data monitoring capabilities, Databand enables users to track data quality, performance, and resource utilization across all stages of the data lifecycle.

One of the key features of IBM Databand is its ability to automate data validation and anomaly detection. This helps data teams identify issues in their data pipelines before they escalate into bigger problems. Additionally, Databand’s integration with popular data processing frameworks, such as Apache Spark and Apache Flink, allows data teams to leverage their existing technology stack while benefiting from Databand’s advanced monitoring and optimization capabilities.

DataOps features: data monitoring, data validation, anomaly detection, integration with data processing frameworks.

IBM Databand is a powerful DataOps tool designed to help data teams manage, monitor, and optimize their data pipelines. With its advanced data monitoring capabilities, Databand enables users to track data quality, performance, and resource utilization across all stages of the data lifecycle.

One of the key features of IBM Databand is its ability to automate data validation and anomaly detection. This helps data teams identify issues in their data pipelines before they escalate into bigger problems. Additionally, Databand’s integration with popular data processing frameworks, such as Apache Spark and Apache Flink, allows data teams to leverage their existing technology stack while benefiting from Databand’s advanced monitoring and optimization capabilities.

DataOps features: data orchestration, resource management for big data jobs, extensibility, REST-ful APIs for job submission and management.

Netflix’s Genie is a platform that provides REST-ful APIs to run a variety of big data jobs like Hadoop, Pig, Hive, and Presto. It’s like an abstraction layer between computation frameworks and the underlying infrastructure, making it easy to manage and control distributed data processing jobs. The primary use of Genie is to manage the running of Hadoop jobs and similar workloads on cloud resources.

Genie manages and allocates resources for big data jobs. It decides where and how to run the job based on the availability of resources and the requirements of the job. Genie has been built with extensibility in mind. It can be integrated with other services and can be used to run a variety of big data jobs. Genie also exposes REST-ful APIs for job submission, management, and other operations. This allows it to be easily integrated with other services and systems.

Source: Netflix Tech Blog

Airflow

DataOps features: data orchestration, task dependency management, integration with various data sources, extensible platform.

Apache Airflow is an open-source DataOps tool that aims to simplify the process of orchestrating complex data workflows. It allows data teams to design, schedule, and monitor data pipelines using a programmable, extensible platform.

One of the key advantages of using Airflow is its ability to manage dependencies between tasks in a data pipeline, ensuring that tasks are executed in the correct order and that any failures are properly handled. Airflow also provides a rich set of operators for working with various data sources, such as databases, file systems, and APIs, making it a highly flexible and versatile tool for managing data workflows.

Source: Apache Airflow

K2View

DataOps features: data integration, data transformation, data governance, real-time data delivery, data-as-a-service technology.

K2View is a data management platform that combines data integration, data transformation, data governance, and data delivery capabilities in a unified solution. It is designed to help organizations create a “single source of truth” for their data by consolidating data from multiple sources and making it available to users in real-time.

One of K2View’s standout features is its patented Data-as-a-Service (DaaS) technology, which allows users to access and manipulate data in a virtualized environment without the need for physical data movement or duplication. This reduces the complexity of managing data and also helps maintain data privacy and regulatory compliance.

Source: K2View

Census

DataOps features: data synchronization from data warehouse to business tools, wide range of data connectors, user-friendly interface for data mappings and transformations.

Census is a DataOps tool that focuses on helping data teams operationalize their data insights by seamlessly syncing data from their data warehouse to various business tools, such as CRMs, marketing automation platforms, and customer support systems.

Census offers a wide range of data connectors for popular data warehouses, such as Snowflake, Redshift, and BigQuery, as well as integrations with various business tools, making it easy for data teams to set up and maintain data syncs between their data warehouse and downstream applications. Additionally, Census provides a user-friendly interface for configuring data mappings and transformations, ensuring that data is accurately synced and formatted for each target system.

Source: Census