Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Confluent and others in Streaming Analytics.
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?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.
Informatica Intelligent Streaming allows organizations to prepare and process streams of data and uncover insights while acting in time to suit business needs. It can scale out horizontally and vertically to handle petabytes of data while honoring business service level agreements (SLAs). Intelligent Streaming provides pre-built high-performance connectors such as Kafka, HDFS, NoSQL databases, and enterprise messaging systems and data transformations to enable a code-free method of defining your data integration logic.
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