Top 10 Data Management Tools in 2025

A significant obstacle for any business expanding is managing its database as it scales. A few problems one might encounter while trying to expand their Database are storage issues, complex management challenges, and difficulties in locating, sharing, and verifying isolated data. To steer growth, a company needs to have the correct data management strategy and the right data management tools.

So, what are data management tools? The primary objective of data management tools is to provide software that facilitates the organization, storage, retrieval, and manipulation of data in various formats. These technologies guarantee their effectiveness, accuracy, and security throughout the data’s lifetime.

In this article, we will introduce you to the concept of data management and data management tools and discuss its importance and key features to your business strategy. The article concludes with a comprehensive list of the best tools for data management to watch out for this year.

What are Data Management Tools?

Data management is the process by which your company handles, organizes, and protects its data throughout its entire lifecycle. This involves acquiring data, storing it correctly, ensuring it’s easily accessible, and maintaining its quality over time. Effective data management ensures that your company can make informed decisions, comply with regulations, and maintain operational efficiency. It focuses on enhancing the usability, security, and reliability of your data, ultimately enabling you to unlock valuable insights and stay competitive.

Data management tools are the software and platforms that help automate, streamline, and improve your data management processes. These tools support key areas like data storage, integration, security, and analysis. For example, databases and cloud storage tools help you securely store and access data, while ETL (Extract, Transform, Load) tools facilitate the efficient movement of data across systems. Analytics and visualization tools, such as Tableau or Power BI, enable you to analyze data and derive insights, making it easier to share valuable information throughout your organization. By utilizing these tools, your company can enhance data accuracy, mitigate risks, and improve decision-making, ultimately ensuring that your data management practices are efficient and effective.

Top 10 Data Management Tools You Should Know About

When selecting a data management tool for your company, it is crucial to consider all available data management tools. These tools will serve as a great addition to your enterprise workflow pipeline. Let’s take a peek at a few of the best data management tools on the market..

1) Hevo Data

Hevo is a zero-maintenance data pipeline platform that autonomously syncs data from 150+ sources, encompassing SQL, NoSQL, and SaaS applications. Over 100 pre-built integrations are native and tailored to specific source APIs. With the help of Hevo, you can gain control over how data lands in your warehouse by performing on-the-fly actions such as cleaning, formatting, and filtering without impacting load performance. Some key features of Hevo include:

• Hevo incorporates a streaming architecture that can automatically detect schema changes in incoming data and replicate them to your destination.
• Monitor pipeline health and gain real-time visibility into your ETL with intuitive dashboards, revealing every pipeline and data flow stats. Utilize alerts and activity logs for enhanced monitoring and observability.
• With Hevo, you can process and enrich raw data without coding.
• Hevo offers a top-tier fault-tolerant architecture that scales seamlessly, ensuring zero data loss and low latency.

Cons of Hevo Data

• No native integration between Hevo and dbt; dbt must be configured and managed separately.
• Lack of built-in orchestration to trigger dbt transformations after data ingestion; requires manual scheduling or external workflow tools.
• Debugging becomes fragmented, as errors must be traced across two platforms with separate logging systems.

Hevo Pricing

Hevo was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with Hevo as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve.

– Juan Ramos, Analytics Engineer, Ebury

2) Stitch Data

Stitch Data is a Cloud-based ETL (Extract, Transform, Load) data management tool. It enables you to integrate data from multiple sources into a data warehouse, providing you with analysis-ready data. The eye-catching features of Stitch Data are as follows:

• Pre-integrated with multiple data sources and can move data into Amazon Redshift, Amazon S3 (Simple Storage Service), Google BigQuery, and PostgreSQL, to name a few.
• It provides easy scheduling for data replication.
• Provides error handling and alerting with automated resolution.
• It is a managed cloud service with automatic scaling and enterprise-grade service-level agreements.
• An API and JSON framework that lets you push data into a data warehouse.

Cons of Stitch Data

• It is not recommended for real-time data replication.
• Limited customizability in data transformations compared to other tools.
• Pricing can become expensive as data volume increases.
• Does not support advanced transformation logic natively; requires additional tools for support.

Stitch Pricing

Following a 14-day free trial, Stitch charges $100 per month for the Standard Plan, which covers up to 5 million rows. Up to 100 million rows are covered by the $1,250 monthly Advanced Plan, while up to 1 billion rows are covered by the $2,500 monthly Premium Plan. For larger volumes, custom enterprise pricing is also offered.

3) Fivetran

Fivetran is a fully managed data management tool with pre-built connectors that enable you to work with analysis-ready schemas, automatically adapting to changes in your sources. You can also check the best data pipeline tools list. Here are a few main features  of Fivetran:

• The data can be moved from Point A to Point B by the caching layer without needing to store a copy on the application layer.
• Your company’s data can be centralized by leveraging Fivetran as part of the workflow. It can also integrate all sources to determine the relevant KPIs (Key Performance Indicators) for the company.
• The caching layer can also be used to send data over a direct, secure connection, providing direct integration at the same time.

Cons of Fivetran

• Requires a well-structured schema for smooth operations.
• High costs for large-scale data integration, especially for growing companies.
• Limited flexibility in handling custom transformations; relies on external tools like dbt.
• Dependency on pre-built connectors results in lesser flexibility for niche sources.

FiveTran Pricing

For a maximum of 500,000 Monthly Active Rows (MAR), Fivetran provides a Free Plan. While the Enterprise Plan starts at $667 per million MAR, the Standard Plan starts at $500 per million MAR. Starting at $1,067 per million MAR, the Business Critical Plan includes extra features for compliance and data protection.

4) Talend

Talend is one of the top data management tools, providing enterprises with tools for data integration, integrity, preparation, and quality assurance. Companies may share, clean, and alter data. Here are some key features of Talend:

• Data transformation features offered by Talend include filtering, flattening, normalizing, aggregating, replicating, looking up, joining, and temporal windowing.
• To Live preview of sample data
• You can use the same connections and graphical elements to construct batch and streaming pipelines inside the same interface.
• Capacity to enrich, clean, and standardize data in the pipeline.

Cons of Talend

• Steeper learning curve for new users due to its complex interface.
• Performance issues can arise with large datasets, requiring the use of significant hardware resources.
• High cost for enterprise editions, making it less accessible for small businesses.
• Depends on additional components for seamless cloud integration.

Talend Pricing

The monthly cost of Talend Cloud Data Integration is $1,170. Depending on features and scalability, enterprise solutions such as Talend Data Fabric might cost anywhere from $13,500 to $77,500 annually. A rudimentary version of Talend Open Studio is also available for free.

5) Amazon Web Services

Amazon Web Services offers numerous data management tools that can be combined into a comprehensive cloud data management solution. It is a subsidiary of Amazon which provides you with an on-demand cloud computing platform and APIs on a pay-as-you-go basis. A few vital services offered by Amazon Web Services are as follows:

• Amazon Redshift for data warehousing.
• Amazon Athena for SQL-based analytics. 
• Amazon Quicksight for dashboard construction and data visualization.
• Amazon Glacier for long-term backup and storage. 
• Amazon S3 (Simple Storage Service) for temporary and/or intermediate storage.

Cons of Amazon Web Services

• Requires manual setup and configuration; dbt does not offer a plug-and-play experience with most AWS services.
• Limited out-of-the-box support for AWS-native orchestration tools like AWS Step Functions or AWS Glue Workflows.
• Logging and monitoring are fragmented across AWS CloudWatch, dbt logs, and the data warehouse, making troubleshooting more complex.
• dbt needs to be self-hosted or integrated with dbt Cloud, which adds deployment and infrastructure overhead in an AWS environment.

Amazon Web Services Pricing

The price for AWS is variable and dependent on the implementation.

6) Microsoft Azure

Microsoft Azure’s platform provides several ways to set up a cloud-based data management system. It also provides handy analytic tools that can be used on Microsoft Azure-stored data. Microsoft Azure allows for multiple database/ data warehouse styles with a good set of tools to manage them. Here’s a list of salient services provided:

• Standard SQL databases and virtual machine-based SQL servers.
• Blob Storage.
• Private Cloud Deployments.
• NoSQL-style table storage options.
• Azure Data Explorer (ADX) enables a user to perform real-time analysis of very large streaming data without the need for preprocessing.

Cons of Microsoft Azure

• No native integration between dbt and Azure Data Factory or other Azure orchestration tools, requiring custom scheduling or external orchestration setups.
• dbt must be hosted separately (e.g., in dbt Cloud or on Azure VMs/Containers), adding infrastructure and deployment overhead.
• Limited out-of-the-box compatibility with Azure Synapse Serverless SQL Pools; most support is focused on dedicated SQL pools.
• Azure services often require complex authentication setups (e.g., Azure Active Directory, service principals), which can complicate dbt configuration and access control.

Microsoft Azure Pricing

The pricing for Microsoft Azure, similar to Amazon Web Services, is also variable and depends on the user’s implementation.       

7) Google Cloud Platform

The Google Cloud Platform offers a large set of data management tools for Cloud-based Data Management. It is handy as it comes along with a workflow manager that ties the different components together. Here are a few key services offered by the Google Cloud Platform:

• Google Data Studio for GUI (Graphical User Interface)-based analysis and dashboard construction.
• Cloud Datalab for code-based data science.
• ML (Machine Learning) Engine for advanced analysis through Machine Learning and Artificial Intelligence. 
• Google BigQuery for tabular data storage.
• Cloud Pub and Cloud Data Transfer for the intake of data.
• Cloud BigTable for NoSQL database-style storage.
• Connections to BI tools like Tableau, Looker, and Power BI.

Cons of Google Cloud Platform

• While dbt integrates well with BigQuery, integration with other GCP services (e.g., Dataflow, Dataproc, Pub/Sub) is limited or non-existent.
• Orchestration requires external tools like Cloud Composer (Airflow on GCP) or Workflows, which adds setup and maintenance overhead.
• dbt must be self-hosted or run via dbt Cloud; native support within GCP ecosystem tools is minimal.
• Authentication and service account configuration can be complex when deploying dbt within GCP infrastructure, especially for multi-project setups

Google Cloud Platform Pricing

Google can also connect to a large number of other data sources. The pricing for Google Cloud Platform depends on the implementation opted for by the user and hence is flexible.

8) Profisee

Profisee is a data management tool that builds and delivers trusted and relevant information across the entire enterprise. The prominent features of Profisee’s Master Data Management are as follows:

• It cleans, matches, and standardizes source data without a single line of code.
• Data Stewardship is enabled within the organization to manage Master Data with reliable feedback from analytics.
• Events are distributed to subscribing systems by Profisee.
• It acts as an integrator, federating master data for global enterprises.
• It also provides real-time bi-directional integration.
• Integration of custom applications is enabled.

Cons of Profisee

• Profisee is focused on master data management and governance, not on analytics-focused SQL transformations like dbt, making direct integration impractical.
• No native support or connector between Profisee and dbt; any data handoff requires intermediate steps, such as exporting to a warehouse or staging area.
• Profisee typically operates in an operational data environment, while dbt is designed for analytic data modeling in data warehouses—this creates architectural disconnects.
• Manual orchestration is required to align Profisee’s master data updates with dbt transformations, increasing the complexity of pipeline timing and dependencies.

Profisee Pricing

Profisee pricing is fixed per hour for a user.

9) Ataccama ONE

Ataccama One offers an automated data management tool that can be run in the Cloud, on-premise, or in a hybrid setup. It is a platform for collective curation that provides solutions for both data governance and master data management. Here are a few salient features of Ataccama ONE:

• Ataccama ONE is a tool that is GDPR (General Data Protection Regulation) ready.
• It provides connectors for a wide range of data types and sources.
• ONE provides automated project configuration and metadata discovery.
• The data curation, cleaning, and classification carried out are based on Machine Learning.
• ONE is typically domain-agnostic but it does come with a set of pre-configured rules for MDM for domains like Customer, Contact, and Product.

Cons of Ataccama One

• No native integration between Ataccama ONE and dbt; connecting them requires intermediate layers such as a data warehouse or custom-built workflows.
• Ataccama operates on governance, profiling, and master data management, while dbt focuses solely on transformations in SQL-based warehouses, resulting in architectural and functional separation.
• Orchestration between Ataccama data flows and dbt transformations must be custom-developed using external tools or scripting, adding complexity.
• Real-time or rule-based data validation and quality checks in Ataccama are not automatically synchronized with dbt’s transformation logic, leading to potential gaps in data trust.

Ataccama Pricing

The pricing for Ataccama is also available on request.   

10) Informatica

Informatica is among the popular enterprise data management tools on the market. It offers excellent features and data integration. Additionally, each application has a notably distinct user interface, making it challenging for business users to switch between them, but Informatica’s increasingly diversified data management toolkit helps solve this issue. This increases the steepness of the already lengthy learning curve and encourages users to hunt for Informatica substitutes.

• Informatica excels in ETL, whereas DBT concentrates on data warehouse conversions.
• For clean, consistent data coming into DBT models, Informatica guarantees data governance and quality.
• DBT and Informatica both scale well in on-premises and cloud settings.

Cons of Informatica

• To manage two systems, coordinate their interconnections, and troubleshoot across multiple environments, running both platforms simultaneously increases operational overhead.
• DBT is primarily batch-based, which makes it unsuitable for real-time data pipelines without additional specialized solutions, even if Informatica effectively manages both batch and real-time data ingestion.
• Higher overall expenses can result from maintaining both dbt (which may require a separate infrastructure if using dbt Core) and Informatica, a generally more expensive enterprise product, particularly when scaling.

Informatica Pricing

The pricing for Informatica is available on request.  

What Challenges Can You Face in Data Management?

Up until now, we have provided you with a summary of some of the top data management tools on the market, along with information on their main characteristics and costs. However, there are also difficulties with data management. The expanding amount of data is generally the cause of data management issues. The following is a list of challenges that organizations might encounter while trying to incorporate data management solutions into their workflow:

• Unclear goals and objectives: A significant challenge in data management is the lack of clarity regarding what an enterprise seeks to achieve with the processed data. In the absence of a clear goal for acquiring the correct data and analyzing it to inform data-driven business decisions, the full potential of data management tools cannot be realized.
• Meeting compliance requirements: Organizations must regularly review their data and practices to stay up-to-date with the constantly evolving compliance regulations, ensuring alignment with new or updated requirements.
• Multiple data storage options: Data is stored across multiple platforms, making it challenging to analyze since it is not available in a single format or repository. Data must be transformed into a unified format to allow for easy analysis.
• Sparse utilization of data management: With massive amounts of data to account for, companies struggle to fully understand the location, volume, and utilization of their enterprise’s data.  
• Extracting value that solves a specific problem: The main challenge lies in making sense of data collected from numerous sources. This involves understanding that data management and data analytics work hand-in-hand to extract maximum value from data, yielding actionable insights.  

Additional Resources on Data Management Tools

• Data Mapping Tools
• Big Data Tools
• Data Profiling Tools
• Data Integration
• Data Pipelines

Conclusion

In this article, you have learned how to use data management tools effectively and why any business requires a solid data management strategy. You’ve examined the various types of instruments on the market and the unique qualities that distinguish them. Now you can assess your demands, try the available tools, and select the ones that best suit your objectives.

Extracting complex data from a diverse set of data sources can be a challenging task, especially for a beginner. Building an in-house ETL pipeline will consume a significant amount of time and resources. Hevo Data provides one of the fastest automated No-code data management pipelines that empowers you to overcome the above-mentioned limitations. Hevo caters to 150+ data sources and can seamlessly ETL your data in real time.

Sign up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

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