Compare the Top Data Modeling Tools that integrate with Sifflet as of October 2025

This a list of Data Modeling tools that integrate with Sifflet. Use the filters on the left to add additional filters for products that have integrations with Sifflet. View the products that work with Sifflet in the table below.

What are Data Modeling Tools for Sifflet?

Data modeling tools are software tools that help organizations design, visualize, and manage data structures, relationships, and flows within databases and data systems. These tools enable data architects and engineers to create conceptual, logical, and physical data models that ensure data is organized in a way that is efficient, scalable, and aligned with business needs. Data modeling tools also provide features for defining data attributes, establishing relationships between entities, and ensuring data integrity through constraints. By automating aspects of the design and validation process, these tools help prevent errors and inconsistencies in database structures. They are essential for businesses that need to manage complex datasets and maintain data consistency across multiple platforms. Compare and read user reviews of the best Data Modeling tools for Sifflet currently available using the table below. This list is updated regularly.

  • 1
    dbt

    dbt

    dbt Labs

    dbt helps data teams transform raw data into trusted, analysis-ready datasets faster. With dbt, data analysts and data engineers can collaborate on version-controlled SQL models, enforce testing and documentation standards, lean on detailed metadata to troubleshoot and optimize pipelines, and deploy transformations reliably at scale. Built on modern software engineering best practices, dbt brings transparency and governance to every step of the data transformation workflow. Thousands of companies, from startups to Fortune 500 enterprises, rely on dbt to improve data quality and trust as well as drive efficiencies and reduce costs as they deliver AI-ready data across their organization. Whether you’re scaling data operations or just getting started, dbt empowers your team to move from raw data to actionable analytics with confidence.
    Starting Price: $100 per user/ month
    View Tool
    Visit Website
  • 2
    Looker

    Looker

    Google

    Looker, Google Cloud’s business intelligence platform, enables you to chat with your data. Organizations turn to Looker for self-service and governed BI, to build custom applications with trusted metrics, or to bring Looker modeling to their existing environment. The result is improved data engineering efficiency and true business transformation. Looker is reinventing business intelligence for the modern company. Looker works the way the web does: browser-based, its unique modeling language lets any employee leverage the work of your best data analysts. Operating 100% in-database, Looker capitalizes on the newest, fastest analytic databases—to get real results, in real time.
  • 3
    Amazon QuickSight
    Amazon QuickSight allows everyone in your organization to understand your data by asking questions in natural language, exploring through interactive dashboards, or automatically looking for patterns and outliers powered by machine learning. QuickSight powers millions of dashboard views weekly for customers such as the NFL, Expedia, Volvo, Thomson Reuters, Best Western and Comcast, allowing their end-users to make better data-driven decisions. Ask conversational questions of your data and use Q’s ML-powered engine to receive relevant visualizations without the time-consuming data preparation from authors and admins. Discover hidden insights from your data, perform accurate forecasting and what-if analysis, or add easy-to-understand natural language narratives to dashboards by leveraging AWS' expertise in machine learning. Easily embed interactive visualizations and dashboards, sophisticated dashboard authoring, or natural language query capabilities in your applications.
  • 4
    Apache Spark

    Apache Spark

    Apache Software Foundation

    Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.
  • Previous
  • You're on page 1
  • Next