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roadmap.md

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🚦 Coming soon

Features

These are the main features that we will enable soon:

  • Anomaly detection
    • Sub-Dimensional Anomaly Alert
    • Enable Anomaly w/o Sub-Dimension
  • Code/API level configuration for
    • Hyperparameters for any algorithms
    • Subpopulation calculation logic 
  • Alerting
    • Backoff for alerting to minimize repeated alerts

New Connectors 

We will be enabling the following new connectors soon:

  • Redshift
  • Hubspot
  • Salesforce
  • Google Analytics Custom Report (will likely already enabled)
  • CSV

Scalability & Robustness Improvements

We are working on the following to improve & support data at larger scale:

  • Data ingestion
    • Support for materialized views across dataware houses for large scale processing
    • Support for OLAP cube support where available
  • Data processing
    • Distributed Sub-Dimensional Anomaly Detection
    • Higher level of sub population support 
  • Robustness
    • More Robust & Fault Tolerant Task Scheduler
    • Robust Error Messages that aid in debugging

🚧 Coming in 6-12 weeks

Features

These are the main features that we will enable in the next quarter

  • Correlation - AutoRCA
  • KPI Import
    • Metabase (partially done)
    • Superset
    • Looker
    • Dbt
  • API support for KPI management & analysis  
  • Anomaly detection
    • Investigate Anomaly List
    • More granular runtime frequency - currently 1 day
    • Model/Params change
  • Data quality
    • Deeper DQ metrics which account for data distribution
    • Ability to define arbitrary DQ metrics with Great Expectations
  • KPI definition catalog
    • Single data source
    • Multi data source
  • Forecast as input for RCA 
  • K8 configuration for horizontal scaling

Connectors

We will enabling the following new connectors:

  • S3
  • Data lake support including Delta Lake
  • Google Playstore
  • RPA based connectors platform (longer term)

Scalability & Robustness Improvements 

We are working on the following to improve & support data at larger scale:

  • Data scalability 
    • Distributed Pandas support - Koalas, Dask
    • Compressed Data/ Metrics Store (longer term)
  • Interactive analysis at scale
    • Implementation of Pinot & Druid based data store to enable interactive analysis 
  • ML scalability
    • Model warm start where possible for heavier models
    • Feature & model store