Users achieve faster time-to-value with Databricks by creating analytic workflows that go from interactive exploration and ETL through to production data products.
AI applications are simpler to explore and transition
to production as the
one platform is used by data scientists and data engineers. Users can quickly prepare clean data at
massive scale, and continuously train and deploy state-of-the-art ML models for best-in-class AI
Databricks makes it easier for its users to focus on their data by providing afully managed, scalable, and secure cloud infrastructure that reduces operational complexity and total cost of ownership.
Data lakehouses are enabled by a new system design: implementing similar data structures and data management features to those in a data warehouse, directly on the kind of low-cost storage used for data lakes. Merging them together into a single system means that data teams can move faster as they are able to use data without needing to access multiple systems. Data lakehouses also ensure that teams have the most complete and up to date data available for data science, machine learning, and business analytics projects.
A Lakehouse has the following key features:
No more malformed data ingestion, difficulty deleting data for compliance, or issues modifying data for change data capture. Accelerate the velocity that high quality data can get into your data lake, and the rate that teams can leverage that data, with a secure and scalable cloud service.
Data is stored in the open Apache Parquet format, allowing data to be read by any compatible reader. APIs are open and compatible with Apache Spark™.
Data lakes often have data quality issues, due to a lack of control over ingested data. Delta Lake adds a storage layer to data lakes to manage data quality, ensuring data lakes contain only high quality data for consumers.
Handle changing records and evolving schemas as business requirements change. And go beyond Lambda architecture with truly unified streaming and batch using the same engine, APIs, and code.
• Develop, test, execute and monitor batch ETL jobs
• Implement data streaming ingestion or analytics job
• Collaborate on code,notebook and jobs
• Monitor machine learning process
• Develop production machine learning pipelines
• Explore machine learning models
• Perform data analysis using SQL at scale
• Explore datasets visually and interactively in a notebook environment
The Real Time Streaming and analyzing of Big Data can help companies to uncover hidden patterns, correlations and other insights. Companies can get answers from it almost immediately being able to upsell, and cross-sell clients based on what the information presents.
The existence of Real Time Streaming data technology brings the type of predictability that cuts costs, solves problems and grows sales. It has led to the invention of new business models, product innovations, and revenue streams.