Best Data Catalog Software for Apache Spark

Find and compare the best Data Catalog software for Apache Spark in 2025

Use the comparison tool below to compare the top Data Catalog software for Apache Spark on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Databricks Data Intelligence Platform Reviews
    The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.
  • 2
    Alteryx Reviews
    Alteryx AI Platform will help you enter a new age of analytics. Empower your organization through automated data preparation, AI powered analytics, and accessible machine learning - all with embedded governance. Welcome to a future of data-driven decision making for every user, team and step. Empower your team with an intuitive, easy-to-use user experience that allows everyone to create analytical solutions that improve productivity and efficiency. Create an analytics culture using an end-toend cloud analytics platform. Data can be transformed into insights through self-service data preparation, machine learning and AI generated insights. Security standards and certifications are the best way to reduce risk and ensure that your data is protected. Open API standards allow you to connect with your data and applications.
  • 3
    Amundsen Reviews
    For your analysis and modeling, discover and trust data. Breaking down silos can help you be more productive. See how others are using the data to get context. A simple text search can be used to find data within your organization. A PageRank-inspired search algorithm suggests results based upon names, descriptions, tags and querying/viewing activity at the table/dashboard. Automated and curated metadata, descriptions and columns of tables and columns, other frequent users, the date the table was last updated, statistics and a preview of data, can help build trust in data. Easy triage by linking ETL job and the code that generated the data. Update tables and columns with descriptions to reduce back-and-forth about which table to use or what a column contains. Find out what data coworkers use, own, or have saved. You can see dashboards created on a table to show you the most common queries.
  • 4
    Sifflet Reviews
    Automate the automatic coverage of thousands of tables using ML-based anomaly detection. 50+ custom metrics are also available. Monitoring of metadata and data. Comprehensive mapping of all dependencies between assets from ingestion to reporting. Collaboration between data consumers and data engineers is enhanced and productivity is increased. Sifflet integrates seamlessly with your data sources and preferred tools. It can run on AWS and Google Cloud Platform as well as Microsoft Azure. Keep an eye on your data's health and notify the team if quality criteria are not being met. In a matter of seconds, you can set up the basic coverage of all your tables. You can set the frequency, criticality, and even custom notifications. Use ML-based rules for any anomaly in your data. There is no need to create a new configuration. Each rule is unique because it learns from historical data as well as user feedback. A library of 50+ templates can be used to complement the automated rules.
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