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Pillars of SDP

SDP – Extract

SDP aggregates thousands of sources across domains to identify business data that meets the needs of clients using custom-made search queries, ranks them based on multiple parameters, and then ingests the data from these sources into the platform.

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SDP – Enrich

Straive provides data enrichment services for data with missing/invalid points through SDP with the help of ML-based models and human intervention if needed.

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SDP – Transform

SDP’s data transformation tools help with transforming raw data into clean, aggregated, analyzable data as they move from individual sources to an analytics warehouse or other enrichment processes downstream.

SDP – Deliver

SDP’s out-of-the-box feature enables data delivery in standard data formats such as XML, JSON, CSV, MS Word, and content such as taxonomies and ontologies in specialized formats such as RDF, SKOS, and OWL. In addition, SDP enables programmatic integration with clients’ CMSs via APIs and web services too.


Platform Highlights

Some of the salient features of the Straive Data Platform are:

  • It is built on open-source technologies, such as Angular JS, Python, PERL, and MongoDB, and the SQL Server
  • All of its’ functionalities can be deployed as microservices on AWS using Docker containers
  • The platform uses REST APIs to integrate multiple modules
  • It can be configured for various client-use cases and has been implemented at scale
  • The platform is managed through a cloud service with automatic scaling and enterprise-grade SLAs
  • The platform’s out-of-the-box connectors provide seamless connectivity and integration with all types of data sources and programmatic data integration via APIs and web services
  • It can be seamlessly integrated with third-party tools and products
  • The platform supports any unstructured content, including non-relational data, and can parse XML, JSON, PDF, emails, and other feeds
  • It provides for data scheme optimization - automated collection, detection, and preparation of data using optimal relational schema
  • The platform is focused on rapid data pipeline construction, data quality monitoring, and error handling
  • It provides flexibility in intervening with custom scripts to monitor, clean, and move data as needed
  • The platform includes convenient, customizable workflows for building modular transformation and enrichment

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