Code-free data pipeline definition – Spectrum provides a completely graphical user experience for creating and defining data pipelines without coding to speed data engineering processes.
Reuse – Data pipeline components are reusable and extensible, enabling teams to share vetted logic components to further speed data pipeline creation.
Collaboration – Data engineering and analytics teams can interactively collaborate around data pipeline definition to ensure requirements are properly met and analysts can create their own extensions to vetted data pipelines.
Self-service UX – Spectrum has an Excel-like spreadsheet-style UI with point-and-click functions easily usable by analysts facilitating analyst self-reliance.
Easy productionizing – Spectrum data pipelines can be productionized by graphically setting the production job parameters and can be moved between development, test, and production servers.
Flexible delivery and consumption – Spectrum supports the delivery of data pipeline datasets to a large number of analytical data stores and directly to many leading BI tools, to accommodate easy consumption.
Scalable execution engines – Spectrum operates its own elastic Spark-based compute cluster under the covers to give jobs the scale and performance they need automatically.
Performance optimization – Spectrum uses a patented Smart ExecutionTM optimizer, to intelligently break down and parallelize jobs as well as minimize data movement.
Scalable governance – Spectrum contains a complete suite of data governance capabilities to ensure data governance processes scale as data pipeline volume and diversity grow.
ML-assisted data quality functions – Spectrum contains integrated ML-assisted functions to filter, de-duplicate, replace, and cleanse data to ensure high data quality.
Data quality analysis – Spectrum provides simple, highly accessible visual data profiling and data statistics-driven workbook health checks can detect dirty, corrupt, or invalid data early and auto-detect and quantify calculation errors.
Data usability – Spectrum offers a rich array of data shaping, organization, and aggregation functions to structure data effectively and produce highly usable datasets.
Data completeness – Spectrum deep set of unification and data enrichment functions allow the combination of diverse datasets and insert value-added calculated columns to produce highly complete datasets.
End-to-end, granular data lineage – Spectrum captures the complete data lineage of a data pipeline that can be drilled down all the way to each transformation that builds confidence and trust in the results.
Integrated, comprehensive governance – Spectrum contains a complete, integrated suite of data governance capabilities that allow teams to ensure proper data security, governance, and privacy.
Complete catalog and metadata – Spectrum provides a detailed catalog of information about data pipelines and datasets to help drive governance.
Enterprise-level security – Spectrum provides fine-grained access controls, enterprise security integration, end-to-end encryption, and uses secure protocols for data transmission.
End-to-end, granular data lineage – Spectrum’s complete data lineage features facilitate comprehensive governance and regulatory controls around data privacy.
Detailed auditing – All relevant user and system events in Spectrum are automatically and transparently logged and are completely auditable.
Reliability & Predictability
Automated operations – Spectrum contains a complete, automated job execution cockpit and engine to ensure the smooth execution of data pipelines and the continuous delivery of data.
Data retention and archiving – Spectrum supports flexible data retention rules and policies that are easily configured.
End-to-end, granular data lineage – To ensure reliability and predictability, Spectrum’s data lineage can be used to isolate and fix problems within data pipelines.
Data pipeline monitoring – The Spectrum job execution cockpit allows data teams to continuously monitor data pipeline jobs to ensure their continued operation.
Granular logging – Spectrum provides granular job execution logs, which can be used to quickly identify, drill down into, and correct problems.
Change auditing – Spectrum logs any changes to logic in a data pipeline at a detailed level and allows teams to audit these change logs to isolate and fix potential errors and problems.
Problem alerts – Users can specify notifications on various detectable errors in data pipeline jobs to alert data teams to the problems so they can be addressed swiftly.