Top 6 DataOps Tools in 2024: In-Depth Guide

Most data-driven companies struggle with data quality issues. High volumes of data, changing data storage requirements as a result of company policies and government regulations, and various types of data sources and formats make data management difficult for companies. DataOps tools automate and simplify all data life cycle phases. It improves data management agility for businesses and speeds up data analytics for users.

What are the five types of DataOps tools?

All-in-One Tools

These tools focus most of the data management components such as data ingestion, data transformation, data analysis, and data visualization. It is ideal for companies that want to perform all data management processes in a single platform. It reduces costs and simplifies data management.

DataOps Orchestration Tools

Orchestration tools enable businesses to manage complex data pipelines in a centralized manner. It generates visually designed workflows that include each step of the end-to-end data pipeline automatically. It organizes and coordinates data flow across data pipelines.

Component Tools

These are tools that undertake a single or a couple of tasks within a data lifecycle such as data storage, data sharing, etc. 

Case-Specific Tools

These tools are used in specific DataOps subdomains such as data warehousing (DW automation), cloud migration (CloudOps), etc.

The most popular DataOps tool examples

Datafold

• Datafold is a platform for data observability. 
• It is used to prevent data outages and other potential data quality issues before data enters production.
• It allows users to trace data flows across datasets, columns, and BI dashboards using column-level lineage.  
• Viewing data drift makes it easier to determine which fields are affected after making any improvements/additions to data sources. If there is a data quality issue, it determines the root cause of the problem.

Composable DataOps Platform

• Composable is an enterprise-grade DataOps platform that provides data orchestration, automation, and analytics services.
• It is used to process large amounts of data, regardless of the source, structure, or format of data. 
• It automates data integration and data preparation processes.

K2View Fabric

• It generates rule-based services in order to apply data governance policies to new data pipelines.
• It ingests customer data from all systems and then transforms and orchestrates ingested data. 
• It organizes customer information in a centralized way, and stores data in a single repository to make data available to any consuming application.

HighByte Intelligence Hub

• HighByte Intelligence Hub is the first industrial DataOps solution. It is intended for use by the operational technology team and is ideal for manufacturers and industrial companies.
• It organizes and merges industrial data, as well as creates data flows for raw data.

Delphix

• Its database virtualization technology provides users with quick and flexible access to virtual data.
• Delphix’s data masking feature conceals sensitive organizational data, such as customer information, from unauthorized users.

Tengu

• Tengu is a DataOps orchestration platform used by data-driven businesses to maximize the value of their data.
• It doesn’t require any technical knowledge to use.
• It creates a unique graph view to help companies understand data environments.

DataKitchen DataOps Platform

• DataKitchen DataOps platform orchestrates and automates all production workflows, from data access to value delivery.
• It improves the data quality by automatically monitoring and testing data pipelines.

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