What is Robotic Data Automation (RDA)?
Robotic Data Automation (RDA) is a new paradigm to help automate data integration and data preparation activities involved in dealing with machine data for Analytics and AI/Machine Learning applications. RDA is not just a framework, but also includes a set of technologies and product capabilities that help implement the data automation.
RDA enables enterprises to operationalize machine data at scale to drive AI & analytics driven decisions.
RDA has broad applicability within the enterprise realm, and to begin with CloudFabrix took the RDA framework and applied it to solve AIOps problems — to help simplify and accelerate AIOps implementations and make them more open and extensible.
RDA automates repetitive data integration, cleaning, verification, shaping, enrichment, and transformation activities using data bots that are invoked to work in succession in“no-code" data workflows or pipelines. RDA helps to move data in and out of AIOps systems easily, thereby simplifying, and accelerating AIOps implementations that otherwise would depend numerous manual data integrations and professional services activities.
Why RDA is Needed?
Artificial Intelligence for IT Operations (AIOps) requires processing vast amounts of data obtained from various hybrid IT data sources, that are spread across on-premises, cloud, and edge environments. This data comes in various formats and delivery modes.
Additionally, results and outcomes of such data processing need to be also exchanged with other tools in the IT ecosystem (Ex: ITSM/Closed loop automation/Collaboration Tools and BI/Reporting tools).
All of this requires integrating, ingesting, preparing, verifying, cleaning, transforming, shaping, analyzing and moving data in and out of AIOps systems in an efficient, reusable, and scalable manner. These essential tasks are most often overlooked in AIOps implementations and cause significant delays and increase costs of AIOps projects.
Challenges
Let us understand what some of the key challenges in data preparation & data integration activities are, when implementing AIOps projects.
■ Different data formats (text/binary/json/XML/CSV), data delivery modes (streaming, batch, bulk, notifications), programmatic interfaces (APIs/Webhooks/Queries/CLIs)
■ Complex data preparation activities involving integrity checks, cleaning, transforming, and shaping the data (aggregating/filtering/sorting)
■ Raw data often lacks application or service context, requiring real-time data enrichment bringing in context from external systems
■ Implementing data workflows require specialized programming/data science skill set
■ Changes in source or destination systems require rewriting/updating connectors
Traditional Approach of Data Handling in AIOps
In the traditional approach, AIOps vendors provide a set of out-of-the-box integrations and once you connect AIOps software to your data sources, you are now pretty much at the mercy of how your data gets utilized, processed for producing results & Outcomes.
■ Black box approach of data acquisition, processing, and integration
■ Use cases and scenarios limited to what the platform supports
■ Integrations mostly predefined/hard coded limiting reuse
■ Complex scripting modules or cookbooks requiring specialized/programming skills (Javscript, Python etc.)
■ Difficult to bring in external integrations for intermittent data processing (ex: enrichment)
■ Difficult to access data in a programmatic way for complementary functions (ex: data access for scripting, reporting, dashboarding, automation etc.)
These are all inhibitors to effective AIOps implementations by way of adding delays & costs (manual data prep/handling activities)
Robotic Data Automation (RDA), a key enabler for AIOps 2.0
RDA automates DataOps, similar to what RPA did to automate business processes. RDA is integral part of AIOps solution that provides augmented data preparation and integration capabilities. RDA is both a data automation framework and a toolkit to accelerate and simplify all data handling in AIOps implementations.
Highlights
■ Implement No-code Data Pipelines using Data bots
■ Native AI/ML bots
■ CFXQL — Uniform Query Language
■ Inline Data Mapping
■ Data Integrity Checks
■ Data masking, redaction, and encryption
■ Data Shaping: Aggregation/Filtering/Sorting
■ Data Extraction/Metrics Harvesting
■ Synthetic Data generation
Benefits
■ Simplify and Accelerate AIOps implementations
■ Reduces time/effort/costs tied to data prep and integrations
■ Suitable for DevOps/ProdOps personnel (no need of data scientist skills)
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