‘Data360’ is a new term and is being used to represent a one-stop shop for all your Big data processing needs.
Enterprise IT teams are faced with the challenge of choosing one vendor for data ingest, another for data wrangling, a third one for machine learning/analytics and yet another for visualization.
Shouldn’t it be really easy to do all this in a unified way, especially if you have already chosen to go with Spark as your Big Data platform? It can be; however the powerful usage of Spark still needs very skilled Scala/Java programmers. A different approach is needed.
During this webinar you will get to know about:
- A powerful all-in-one Apache Spark strategy for the enterprise and an implementation approach for end-to-end big data analytics processing
- The elements of a real-time Data360 solution – Ingest, Cleanse, Transform, Blend, Analyze, Load and Visualize
- A combination of tools and tactics used for Data360 on streaming and historical data, using Apache Spark and Apache Spark Streaming
- How use cases like anomaly detection, customer 360, IoT and log analytics, fraud and security analytics and many more can be achieved using this approach
The only all-in-one data pipeline platform
- One platform to do it all - ETL, ELT, ingestion, CDC, ML
- Self Service, zero-code, drag and drop interface
- Built-in DataOps, MLOps, and DevOps tools
- Cloud-agnostic and interoperable