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Data management tools are software or applications that help organizations store, organize, and manage their data. It enables users to perform various tasks such as data input, validation, cleaning, transformation, and retrieval.

What are data management tools?

Data management tools can be classified into different categories:

  • Cloud data management and warehousing: These tools are used to store and analyze large amounts of data from various sources and provide insights to support business decision-making. Examples include Distilled, Redshift, Snowflake, Google Cloud, Panoply, etc.
  • Database management systems (DBMS): These tools are used to manage and organize relational or non-relational data in a structured way. Examples include MySQL, MongoDB, PostgreSQL, etc.
  • Data integration tools: These tools are used to extract, transform, and load data from various sources into a central repository. Examples include Fivetran, Stitch, Amazon AppFlow, etc.
  • Data transformation and quality tools: These tools are used to ensure that data is accurate, transformed, catologed, and consistent. Examples include dbt, Dagster, Datafold, etc.

In 2023, most data management tools can be, or at least should be, cloud-based, depending on the organization’s needs and preferences.

Data management tools are an essential part of any organization’s operations and data stack. They provide a variety of benefits that help companies make better use of their data and improve their decision-making process. Additionally, data management tools provide various security features such as encryption and access controls to help protect sensitive data from unauthorized access and breaches. Data management tools also provide advanced analytics and reporting capabilities that can help organizations gain insights and make better business decisions.

In this article, we will outline the top 14 data management tools in 2023 that the Untitled team recommends and utilizes within our modern data stack services.

Top cloud data management tools and warehousing


1. Distilled

Distilled is a managed data stack platform that allows organizations to adopt the full modern data stack through a monthly subscription plan. Distilled is highly user-friendly and straightforward to use for SMBs, scaleups and business users. With over 180+ data source connections, ETL, data warehousing and transformations happen effortlessly.

Key features include:

  • A large selection of native data sources that can be connected rapidly
  • Secure data warehousing backed by the cloud for increased agility, top security and resilience, auto-scaling storage, and real-time computing
  • Automated data transformations supported by dbt for accessible and staged data sets
  • Available connection string to connect to the most widely adopted BI tools like Sisense, Tableau, PowerBI, Looker and more
managed data integration within distilled and fivetran integrations

2. Redshift

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It allows you to easily analyze large amounts of data using your existing business intelligence tools.

Key features include:

  • Can handle petabyte-scale data warehousing and allows you to easily scale your data warehouse up or down as needed
  • Uses advanced compression and query optimization techniques to deliver fast query performance
  • A pay-as-you-go pricing model and allows you to start small and scale as needed, so you only pay for what you use
  • Easily integrated with other AWS services, such as S3, EMR, and Athena, to allow for a seamless data pipeline

3. Snowflake

Snowflake is a fully managed, cloud-based data warehouse service that allows you to store, analyze, and query data using SQL. It supports both structured and semi-structured data and can handle a variety of data sources, including JSON, Avro, and Parquet.

Key features include:

  • Easily scale your data warehouse up or down as needed, and can automatically adjust resources to meet changing performance needs
  • Uses advanced query optimization and columnar storage to deliver fast query performance
  • Snowflake is multi-cloud data platform, allows you to easily move your data and workloads between different cloud providers, such as AWS, Azure and GCP
  • Provides built-in security features, such as encryption and role-based access control, to help keep your data secure and compliant

4. Google Cloud

Google Cloud is a suite of cloud computing services offered by Google that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube.

Key features include:

  • Allows users to scale resources up or down as needed, making it easy to handle fluctuating workloads
  • Offers a 99.95% uptime SLA for many of its services, ensuring that your applications are always available
  • Has a global network of data centers, allowing users to store and process data closer to their users for low latency and high performance
  • Provides a range of security features, including encryption and authentication, to help protect users’ data and applications
google cloud platform services

5. Panoply

Panoply is a turn-key solution that makes data integration easier. With ETL and a cloud-data warehouse, the tool is designed for teams with a variety of skillsets, including the business user.

Key features include:

  • An extensive selection of data sources to connect within your plan
  • In-browser SQL editor for data analysts
  • Automated scaling of cloud databases for low manintence data warehousing
  • Connectors for common data visualization platforms like Tablue, Looker and PowerBI

Top cloud database management tools


6. MySQL

MySQL is a popular open-source relational database management system (RDBMS). It is widely used for web applications and data warehousing.

Key features include:

  • Free to use, distribute and modify, which makes it accessible to a wide range of users and organizations
  • Known for its fast performance, even when handling large amounts of data
  • Has a reputation for being a stable and reliable database management system
  • MySQL can run on a variety of operating systems including Windows, Linux, and macOS

7. MongoDB

MongoDB is a popular open-source NoSQL database management system. It uses a document-oriented data model, which allows for more flexible and scalable management of unstructured data.

Key features include:

  • MongoDB’s document-oriented data model allows for more flexible and efficient management of unstructured data
  • Highly scalable, allowing for easy horizontal scaling by sharding data across multiple servers
  • Known for its high performance and ability to handle large amounts of data
  • Has a rich query language that allows for flexible and powerful data querying and aggregation

8. PostgreSQL

PostgreSQL is an open-source relational database management system known for its stability, data integrity, and performance. It’s often used for complex and demanding applications that require advanced features such as full-text search, geospatial data, and JSON data processing.

Key features include:

  • Has a wide range of advanced features, such as full-text search, geospatial data, and JSON data processing, that are not always available in other open-source RDBMS
  • Known for its high performance, even when handling large amounts of data
  • Robust data integrity features, such as support for transactions and constraints, that help ensure data consistency and accuracy
  • Allows developers to define their own data types, operators, and functions, making it highly extensible and customizable to specific needs

Top data integration tools


9. Fivetran

Fivetran is a cloud-based data pipeline service that automates the process of extracting, loading and transforming data from various sources into a data warehouse. It allows businesses to easily access, analyze and visualize their data.

Key features include:

  • Automates the process of data pipeline, eliminating the need for manual work and reducing the chances of errors
  • Easily integrate data from various sources such as databases, SaaS applications, and cloud services
  • Ensures the data quality by providing out-of-the-box data validation and transformation rules, it also allows for custom rules
  • With their PBF offering, Fivetran can be embedded and white labeled inside of your own application, allowing your users to on-board their own data

10. Stitch

Stitch is another cloud-based data pipeline service similar to Fivetran that aims to simplify the process of extracting, loading and transforming data from various sources into a data warehouse. It allows businesses to quickly and easily access and analyze their data from various sources.

Key features include:

  • Designed to be user-friendly and easy to use, with a simple and intuitive interface
  • Automates the process of the data pipeline, eliminating the need for manual work and reducing the chances of errors
  • Easily integrate data from various sources such as databases, SaaS applications, and cloud services
  • Designed to handle a high volume of data, it can easily handle large data sets and allows for easy scalability as data volume grows

11. Amazon AppFlow

A newer application on this list, Amazon AppFlow can automate bi-directional data flows between SaaS applications and AWS services in just a few clicks. Run the data flows at the frequency you choose, whether on a schedule, in response to a business event, or on demand.

Key features include:

  • Embed data connectors inside of your own application
  • Transfer data at scale without the need to provision system resources
  • Automate cataloging your data to discover and share across AWS analytics and machine learning services
  • Enrich data for different SaaS services, such as pulling Salesforce data into Amazon SageMaker’s custom ML mode

Top data transformation and quality tools


12. dbt

DBT (Data Build Tool) is an open-source tool developed by Fishtown Analytics that helps organizations build, test, and maintain their data infrastructure. It provides a way for data analysts and engineers to write SQL code in a more maintainable and modular way, making it easier to collaborate on and manage data projects.

Key features include:

  • Allows multiple team members to work on the same data project at the same time, without overwriting each other’s changes. This makes it easier to manage and maintain data pipelines and workflows
  • Provides features for testing and validating data, which helps ensure that data is accurate and reliable. It also provides a way to document and track changes to data pipelines and workflows, which is important for data governance
  • Provides a command-line interface and a web-based UI that make it easy to run and manage data pipelines and workflows
  • Supports a wide range of data sources, including SQL databases, CSV files, and cloud-based data warehouses like Amazon Redshift and Google BigQuery

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13. Dagster

Dagster is an open-source data orchestration platform that makes it easy to build, run, and operate data pipelines. It is designed to help data engineers, data scientists and data analysts build and manage complex data workflows, in a way that is maintainable, scalable, and robust.

Key features include:

  • Dagster’s pipeline modeling paradigm allows for arbitrary directed acyclic graphs (DAGs) of operations, making it easy to represent complex data workflows, and allowing for high degree of flexibility and customization
  • Automatically tracks lineage and provenance information for each pipeline run, which makes it easy to understand the origin and dependencies of data
  • Designed to handle large scale data processing workloads, it allows you to scale out your data pipelines horizontally by adding more resources to your cluster
  • Libraries are built on top of Python type-checking, which allows for catching errors early in the development process, making it easier to build and maintain pipelines with fewer errors

14. Datafold

Datafold is a data observability platform that helps users monitor the quality of their data through profiling, diffs and anomaly detection, integrated within their existing CI & infrastructure.

Key features include:

  • Automated metrics monitoring module within the software, lets users create a smart alert from any SQL query in a single click
  • Runs detailed analysis through the normal behavior of its user’s data, offering timely notifications on detecting anomalies
  • Users are allowed to adjust the sensitivity of anomaly detection
  • Integrates with a variety of convenient channels such as Slack, PagerDuty, Email or custom webhooks to send notifications to its users

If you’re looking for a managed data integration solution, consider Distilled by Untitled as your go-to platform. Regardless of the industry you’re in, this platform is flexible and customizable to meet your specific needs and requirements.

Kramer Caswell

Author Kramer Caswell

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