Find out what your peers are saying about Databricks, Knime, Amazon Web Services (AWS) and others in Data Science Platforms.
Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.
Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.
What are the key features of Databricks?
What are the benefits or ROI to look for in Databricks reviews?
Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.
Zepl enables data science teams to rapidly explore, analyze and collaborate around their cloud data. In just minutes, Zepl brings machine learning at scale to data scientists, data engineers, data analysts, team managers and executives. Used by high tech, financial services, pharmaceutical, IoT, and automotive companies, Zepl changes the game by automating model-driven insights in a highly secure manner. Zepl rapidly accelerates experimentation, frictionless collaboration, training of ML models, and transforms customers from reactive to proactive enterprises through the use of powerful machine learning insights. Try Zepl for free today at www.zepl.com.
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