Requirement: Data platform should have out-of-the-box capability of processing the data (structured, semi-structured, unstructured) in batch mode. These includes data cleansing, validations, transformations, aggregation, enrichment.
BDB Response: BDB's data pipeline is an event based serverless architecture, which can handle any type of data continuous or asynchronous, real-time, or batched or both. Data may be ranging from UI activities, logs, performance events, sensor data, emails, social media to organizational documents, BDB's Lambda architecture saves users from the nitty-gritty of data interaction and facilitates smooth data ingestion. User just need to specify the Invocation type that if your data is real-time/batch. BDB Data Pipeline supports basic and advanced level data transformations through in-built components and integrated Data Preparation scripts to enhance data insight discovery.
Requirement: Support incremental (Change Data Capture) and ad-hoc data processing capabilities.
BDB Response: BDB pipeline supports incremental and ad-hoc data processing.
Requirement: Provide scheduling capabilities with respect to batch processing jobs. Also support scheduling capabilities with 3rd party scheduling tools if required.
BDB Response: BDB pipeline has built-in scheduler for batch data processing.
Additional SDK’s/API’s can be exposed to 3rd party scheduling tools
Requirement: Support integration with Open-Source tools and technologies.
BDB Response: Yes, BDB Platform supports integration with Open-source tools and technologies.
Requirement: Support Edge processing capabilities for large datasets hosted on On-Premises.
BDB Response: Yes, BDB Platform supports edge version of platform installation over edge server to process data from Edge.
Last updated 1 year ago
