Best Data Analysis Software for Datagaps ETL Validator

Compare the Top Data Analysis Software that integrates with Datagaps ETL Validator as of November 2024

This a list of Data Analysis software that integrates with Datagaps ETL Validator. Use the filters on the left to add additional filters for products that have integrations with Datagaps ETL Validator. View the products that work with Datagaps ETL Validator in the table below.

What is Data Analysis Software for Datagaps ETL Validator?

Data analysis software allows the organization, inspection, analysis and interpretation of data in order to process it for decision-making purposes. Compare and read user reviews of the best Data Analysis software for Datagaps ETL Validator currently available using the table below. This list is updated regularly.

  • 1
    Tableau

    Tableau

    Tableau

    Gain, generate, and analyze business data and meaningful insights with Tableau, an integrated business intelligence (BI) and analytics solution. With Tableau, users are able to collect data from different sources such as spreadsheets, SQL databases, Salesforce, and cloud apps. Tableau provides users with real-time visual analytics and interactive dashboard that enables them to slice and dice datasets for making relevant insights and look for new opportunities. Tableau also allows users to customize the platform to serve different kinds of industry verticals like banking, communication, and more.
  • 2
    Oracle Analytics Cloud
    Oracle Analytics Cloud provides the industry’s most comprehensive cloud analytics in a single unified platform, including everything from self-service visualization and powerful inline data preparation to enterprise reporting, advanced analytics, and self-learning analytics that deliver proactive insights. With support for more than 50 data sources and an extensible, open framework, Oracle Analytics Cloud gives you a complete, connected, collaborative platform that brings the power of data and analytics to every process, interaction, and decision in every environment – cloud, on-premises, desktop and data center. Preparing and cleansing your data is an important step before visualizing a data set. For example, the set might have sensitive data such as customers' social security numbers that you don't want to expose.
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