Today’s organizations are tasked with managing multiple data types from countless varied sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that in order to deliver insights in a timely manner, they need a data storage and analytics solution that offers more agility and flexibility than traditional data management systems.
One of the challenges around building a data lake is keeping track of all of the raw assets as they are loaded into the data lake, and then tracking all of the new data assets and versions that were created by data transformation, data processing, and analytics. Thus, an essential component of a data lake is the data catalog.
Advanced Analytics pipelines often requires multiple steps in a complete workflow to be run in a coordinated sequence. Automating these steps and orchestrating them across multiple services along with error handling, parameter passing, state management and a visual console that lets you monitor the end-to-end flow are really crucial capabilities for a succesful lasting big data analytics implementation.
Autonomous or semi-autonomous examination of data or unstructured content, using sophisticated techniques and tools, beyond those of traditional business intelligence, to discover deeper insights, make predictions, produce advanced visualizations or generate recommendations.