Best Data Preparation Software for ThoughtSpot

Find and compare the best Data Preparation software for ThoughtSpot in 2024

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

  • 1
    Datameer Reviews
    Datameer is your go-to data tool for exploring, preparing, visualizing, and cataloging Snowflake insights. From exploring raw datasets to driving business decisions – an all-in-one tool.
  • 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
    Teradata Vantage Reviews
    Businesses struggle to find answers as data volumes increase faster than ever. Teradata Vantage™, solves this problem. Vantage uses 100 per cent of the data available to uncover real-time intelligence at scale. This is the new era in Pervasive Data Intelligence. All data across the organization is available in one place. You can access it whenever you need it using preferred languages and tools. Start small and scale up compute or storage to areas that have an impact on modern architecture. Vantage unifies analytics and data lakes in the cloud to enable business intelligence. Data is growing. Business intelligence is becoming more important. Four key issues that can lead to frustration when using existing data analysis platforms include: Lack of the right tools and supportive environment required to achieve quality results. Organizations don't allow or give proper access to the tools they need. It is difficult to prepare data.
  • 4
    Lyftrondata Reviews
    Lyftrondata can help you build a governed lake, data warehouse or migrate from your old database to a modern cloud-based data warehouse. Lyftrondata makes it easy to create and manage all your data workloads from one platform. This includes automatically building your warehouse and pipeline. It's easy to share the data with ANSI SQL, BI/ML and analyze it instantly. You can increase the productivity of your data professionals while reducing your time to value. All data sets can be defined, categorized, and found in one place. These data sets can be shared with experts without coding and used to drive data-driven insights. This data sharing capability is ideal for companies who want to store their data once and share it with others. You can define a dataset, apply SQL transformations, or simply migrate your SQL data processing logic into any cloud data warehouse.
  • 5
    Kylo Reviews
    Kylo is an enterprise-ready open-source data lake management platform platform for self-service data ingestion and data preparation. It integrates metadata management, governance, security, and best practices based on Think Big's 150+ big-data implementation projects. Self-service data ingest that includes data validation, data cleansing, and automatic profiling. Visual sql and an interactive transformation through a simple user interface allow you to manage data. Search and explore data and metadata. View lineage and profile statistics. Monitor the health of feeds, services, and data lakes. Track SLAs and troubleshoot performance. To enable user self-service, create batch or streaming pipeline templates in Apache NiFi. While organizations can spend a lot of engineering effort to move data into Hadoop, they often struggle with data governance and data quality. Kylo simplifies data ingest and shifts it to data owners via a simple, guided UI.
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