If you want to improve the speed, quality, and reliability of your data analytics and decision-making, you’ll need the right stack of DataOps tools. The best DataOps tools in 2023 are designed to enable your data teams to deliver business value faster, break down data silos, and increase the accuracy of data analysis.
DataOps tools help to streamline and automate the entire data lifecycle—from collecting, preparing, analyzing, and migrating data to delivering data and making it visible on dashboards.
Some of these DataOps tools are open source, others are paid services. All of them can be tailored to your needs to optimize data pipelines, improve data quality, and enable faster and more accurate decision-making.
Shipyard—Data orchestration tool
Shipyard is an orchestration platform designed for data professionals of all technical backgrounds. It seamlessly connects different tools and simplifies workflows. With Shipyard, there's no need to write complex code. It offers templates you can use to create your data pipelines with little to no code needed.
If the templates don't fit your needs, Shipyard lets you automate scripts in your preferred programming language. This means you can add any process you want to your workflows, even if it's from outside or inside your company.
The team has worked with big brands and understands the challenges of handling lots of data. They included features that proactively spot problems and keep your data safe.
With Shipyard, you don't need to rely on other teams or worry about the quality of your data infrastructure.
- Use cases: Workflow management, data pipeline orchestration, and task scheduling
- Pros: Cloud-based, user-friendly interface, and support for containerized tasks
- Cons: Focus on orchestration and scheduling, may require integration with other tools for complete data pipeline management
Pricing: Shipyard has a free developer plan, a paid team plan that scales based on need, and customer enterprise plans.
Apache NiFi—Data integration tool
Apache NiFi is an open-source, Java-based data integration and processing tool that provides a web-based interface for designing, managing, and monitoring data flows. It's capable of handling various data types, sources, and sinks, making it a versatile DataOps tool.
- Use cases: Data ingestion, data routing, data transformation, and data lineage
- Pros: Open-source, highly customizable, and scalable
- Cons: Steeper learning curve and limited built-in data governance features
Pricing: Free (open-source) with potential costs for support, maintenance, and infrastructure
Databricks—Data analytics tool
Databricks is a unified data analytics platform that combines the best of data warehouses and data lakes. It offers a collaborative environment for data engineering, data science, and machine learning, built on top of Apache Spark. Databricks helps organizations process large-scale data, build models, and derive insights more efficiently.
- Use cases: Big data processing, machine learning, and advanced analytics
- Pros: Integrates with Apache Spark, supports various programming languages, and offers a collaborative workspace
- Cons: Can be expensive and requires knowledge of Spark for optimal performance
Pricing: Databricks offers a pay-as-you-go pricing model based on the resources used (e.g., virtual machines, storage). They provide different tiers, such as the Community Edition (free with limited resources), Databricks Standard, and Databricks Premium. The latter two tiers have custom pricing based on your organization's specific needs. Contact Databricks for a custom quote.
dbt (data build tool)—Data transformation tool
dbt is an open-source data transformation tool that enables data analysts and engineers to write, test, and maintain SQL-based data transformations. dbt helps organizations build modular, reusable, and version-controlled data pipelines, ensuring data quality and consistency.
- Use cases: Data transformation, data modeling, and data testing
- Pros: SQL-based, open-source, and integrates with various data warehouses
- Cons: Limited to SQL-based transformations, which may not suit all use cases
Pricing: dbt Core, the open-source version, is free to use. For additional features and support, dbt offers a commercial product called dbt Cloud, which includes a web-based interface, scheduling, and collaboration tools. dbt Cloud pricing starts with a free tier for smaller teams and scales up based on the number of users, jobs, and features required.
NOTE: dbt's pricing has changed twice in 10 months as of this posting. There's been fierce user backlash about the pricing. Ask other customers what they're paying for the solution that interests you, and work to understand if the costs are reasonable based on what you want to achieve or if there are other less expensive and potentially better alternatives.
Atlan—Data lineage tool
Atlan is a collaborative data lineage tool designed to enhance data understanding, governance, and collaboration within organizations. With Atlan, teams can visualize the movement and transformations of data across systems, enabling efficient tracking of data flow and ensuring accurate insights.
- Use cases: Data lineage visualization, understanding data transformations, ensuring data quality, supporting compliance efforts
- Pros: Intuitive data flow visualization, collaborative features for teams, assists in compliance and governance, facilitates communication among data stakeholders
- Cons: Specific features and limitations would depend on Atlan's offerings, which might evolve over time
Pricing: Pricing details for Atlan can be found on their official website. Different pricing tiers may be available based on factors such as organization size, features required, and level of support.
Informatica—Data governance tool
Informatica Data Governance is a pivotal component of the Informatica platform, designed to enhance data control, visibility, and collaboration across organizations. It empowers you to govern data effectively, ensuring data quality, compliance, and trustworthy insights.
- Use cases: Data governance, data quality management, metadata management, compliance, and regulatory adherence
- Pros: Integrated suite for comprehensive data governance, robust metadata management, collaborative features for data stewardship, and alignment with data compliance requirements
- Cons: Potential complexity in implementation and configuration, may require training for optimal usage
Pricing: Informatica offers customized pricing models tailored to the specific needs and scale of each organization.
Dataiku—Data collaboration tool
Dataiku is an end-to-end data science and machine learning platform that enables collaboration between data engineers, data scientists, and business analysts. It provides a user-friendly interface for data ingestion, processing, modeling, and deployment, with support for both code-based and visual workflows.
- Use cases: Data preparation, machine learning, data pipeline automation, and collaboration
- Pros: Collaborative workspace, visual data pipeline builder, and support for custom code
- Cons: High licensing costs and may require additional tools for complete data pipeline management
Pricing: Dataiku offers different pricing tiers based on the features, users, and support required. There is a free version called Dataiku Free Edition with limited functionality. For more advanced features and scalability, they provide the Dataiku Team Edition, Dataiku Business Edition, and Dataiku Enterprise Edition. Pricing details for these paid tiers are not publicly disclosed and can be obtained by contacting Dataiku directly.
Microsoft SSIS (SQL Server Integration Services)—Data automation tool
Microsoft SQL Server Integration Services (SSIS) is a powerful data integration and ETL (Extract, Transform, Load) tool that enables users to create, manage, and automate data workflows. SSIS is designed to facilitate the extraction of data from various sources, perform transformations on the data, and load it into target destinations, often databases.
- Use cases: Data extraction, transformation, and loading (ETL), data integration, data warehousing, and business intelligence
- Pros: Seamless integration with Microsoft SQL Server databases, intuitive visual design interface for creating data workflows, robust set of data transformation and manipulation tasks, ability to schedule and automate tasks
- Cons: Primarily works within the Microsoft ecosystem, may require familiarity with SQL and database concepts, might have a steeper learning curve for complex workflows
Pricing: SSIS is included as part of the Microsoft SQL Server suite. Pricing varies based on the edition of SQL Server chosen (e.g., Standard, Enterprise), which comes with different features and capabilities.
Elasticsearch—Data monitoring and observability tool
Elasticsearch is an open-source search and analytics engine known for its powerful capabilities in indexing, searching, and analyzing large volumes of data. It's designed to handle unstructured and semi-structured data, making it suitable for a wide range of use cases, including search engines, log monitoring, and data observability.
- Use cases: Full-text search, log and event data analysis, monitoring and observability, business intelligence, and content discovery
- Pros: Highly scalable and distributed architecture, real-time data indexing and search capabilities, supports complex querying, versatile application across different industries
- Cons: Requires configuration and optimization for specific use cases, complex setup for distributed clusters, can consume significant system resources
Pricing: Elasticsearch is open source, and you can freely download and use the software. However, if you require additional features and support, the company behind Elasticsearch, Elastic, offers commercial solutions with various pricing tiers.
DataRobot—Machine learning tool
DataRobot is an AI-driven, automated machine-learning platform that simplifies the process of building, deploying, and maintaining machine-learning models. It helps businesses accelerate data-driven decision-making by automating model development, providing explainable AI, and offering built-in data governance.
- Use cases: Automated machine-learning, data preparation, and model deployment
- Pros: User-friendly interface, automated feature engineering, and extensive model library
- Cons: Limited customization of machine-learning models and potentially high costs for large-scale deployments
Pricing: DataRobot offers custom pricing based on the specific needs and requirements of each organization. Pricing details are not publicly available and can be obtained by contacting DataRobot directly for a custom quote.
Tableau—Data visualization tool
Tableau is a leading data visualization and analytics platform that empowers users to transform raw data into interactive and insightful visualizations and dashboards. It's designed to make data exploration and analysis accessible to both technical and non-technical users, enabling better decision-making and data-driven insights.
- Use cases: Data visualization, business intelligence, interactive dashboards, data exploration, and reporting
- Pros: Intuitive drag-and-drop interface, a wide range of visualization options, supports connecting to various data sources, facilitates collaboration through shared dashboards
- Cons: Can be resource-intensive for large datasets, advanced features may have a steeper learning curve, requires proper data preparation for optimal results
Pricing: Tableau offers several licensing options, including Tableau Desktop (for creating visualizations) and Tableau Server or Tableau Online (for sharing and collaborating on visualizations). Pricing details vary based on factors such as user count, deployment type, and features required.
These DataOps tools help your business ingest, transform, analyze, and store data more efficiently. But what’s the right combination of DataOps tools for your org? The best choice for your organization depends on your specific requirements, budget, and technical expertise.
How to choose the right DataOps tool?
Choosing the right DataOps tools for your business is a critical decision. It significantly impacts the efficiency and effectiveness of your data management, processing, and analytics capabilities.
When selecting a DataOps tool, take time to consider the following factors:
Business requirements: Start by identifying your organization's specific needs and goals in terms of data processing, analytics, and decision-making. Understand the types of data you work with, the data sources you need to integrate, and the desired outcomes of your analytics efforts. This helps you narrow down your options to tools that best align with your objectives.
Scalability: As your business grows and your data volume increases, you'll need a DataOps tool that can scale with your needs. Look for tools that can handle large datasets and offer flexible deployment options like cloud-based or hybrid solutions. This scalability ensures smooth performance as your data infrastructure evolves.
Integration: Your chosen DataOps tool needs to integrate seamlessly with your existing data ecosystem—including data storage, data processing, and analytics platforms. Evaluate tools based on their compatibility with your current technology stack and their ability to connect with a wide range of data sources and formats.
Ease of use: User-friendliness is an important consideration, as it affects adoption rates and productivity. Look for tools that offer intuitive interfaces, visual data pipeline builders, and no-code/low-code options to simplify the process for both technical and non-technical users.
Customization and extensibility: Every organization has unique data requirements so it's essential to choose a tool that offers customization and extensibility options. This could mean support for custom code, a wide range of pre-built connectors, or a robust API for building custom integrations.
Collaboration features: DataOps emphasizes collaboration between different teams, such as data engineers, data scientists, and business analysts. Choose tools that facilitate communication and collaboration—e.g., shared workspaces, version control, and real-time notifications.
Data governance and compliance: Ensuring data privacy, security, and compliance with regulations is crucial in today's data-driven world. Opt for tools that offer built-in data governance features—e.g., data lineage, data cataloging, and data quality management.
Pricing and budget: DataOps tools come with various pricing models, from open-source solutions to enterprise-grade platforms with custom pricing. Evaluate your budget constraints and the total cost of ownership (including implementation, training, and maintenance costs) when selecting a tool.
Keep in mind that the ideal solution may involve a combination of tools, each addressing a specific aspect of your data operations process.
What next?
The good news is that there are many great DataOps tools to choose from in 2023. The not-so-good news is that you still have to make a solid technology decision. One of the best ways to do that is by trying a DataOps tool for yourself.
That’s why Shipyard has a free developer plan that lets you launch data workflows right away. Sign up and try it for free today—no credit card required. Simply dive in on your own time and see how Shipyard can balance out your DataOps tech stack.
