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Cogility’s Cogynt is a continuous intelligence software platform for real-time data stream processing and behavioral analytics. The integrated platform approach addresses the inefficiencies and high costs associated with conventional, ad hoc tooling to build in-house real-time situational awareness and decision support applications.

By combining stream data processing from high volume and diverse sources and employing a patented expert-system-based AI (one of the earliest true AI systems designed to mimic the decisions made by a human expert) in an integrated package, Cogynt allows organizations to deliver continuous intelligence solutions faster and with minimal engineering overhead. The no-code model authoring allows subject matter experts to improve continuous intelligence product efficacy and delivery. Its standout feature, hierarchical complex event processing (HCEP), transforms raw data streams and complex behavioral analytics into predictive and actionable intelligence, setting it apart in the sector.

Cogynt’s key components include:

• Flexible data integration engine: Aggregates data from diverse sources for real-time analytics.
• Hierarchical complex event processor (HCEP): Levels of stateful, computational pattern-matching logic to determine high-confidence intelligence with full traceability.
• No-code authoring: GUI experts, business analysts, and data scientists visually create models of computational logic that streamline development and are self-documenting.
• Visualization and reporting tools: Provides a user-friendly interface for visualizing complex data and generating BI dashboards and reports.
• AI and machine learning: Enhances data processing with sophisticated insights.

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There are a number of frameworks and/or modes according to which data pipelines can operate. These include:

• ETL (extract, transform, load): Data is first extracted from its source system(s) and consolidated; next, any number of transformations are applied, based on the envisioned analysis and requirements of the intended destination system; and finally, the transformed data is loaded into the target system and stored and persisted accordingly. The overall category of data pipelines originates from this framework. (See Figure 1.)
• ELT (extract, load, transform): Data is first extracted as-is from its source system and loaded into a target system, remaining unmodified as it moves. Transformations are applied to the data after it has been loaded—often by leveraging a tool such as the data build tool (dbt). Transformations can be applied and reapplied to the raw data on an as-needed basis, without any need to re-ingest the data. (See Figure 2.)
• CDC (change data capture): Changes to data and metadata are continuously captured, in real-time or near-real-time increments, and replicated from source to destination. This continuous approach to data movement differentiates CDC from the ETL and ELT frameworks, through which data extraction and movement is often (but not always) done in batches, at scheduled intervals, or on demand.

Today’s data pipeline solutions can support one or a combination of these modes.

Figure 1: ETL Process

Figure 2: ELT Process

Business Imperative
Building a data pipeline from scratch is time-consuming and code-intensive. Data engineers have often disproportionately shouldered the burden of building and maintaining custom pipelines, along with much of the related data transformation and data preparation tasks. Since today’s organizations must work with immense volumes of data from a great variety of sources, the magnitude of the resulting work required can stretch resources to the limit.

Data pipeline tools have evolved to meet these needs. These platforms automate the creation and maintenance of pipelines, and they automate the repetitive tasks involved in cleansing and preparing data. They also allow the automatic scheduling of loads and tasks to run at predetermined intervals. In doing so, data pipeline solutions offer the potential to increase efficiency, improve collaboration among technical and non-technical users, and reduce complexity and redundancy.

Sector Adoption Score
To help executives and decision-makers assess the potential impact and value of a data pipeline solution deployment to the business, this GigaOm Key Criteria report provides a structured assessment of the sector across five factors: benefit, maturity, urgency, impact, and effort. By scoring each factor based on how strongly it compels or deters adoption of a data pipeline solution, we provide an overall Sector Adoption Score (Figure 3) of 4.2 out of 5, with 5 indicating the strongest possible recommendation to adopt. This indicates that a data pipeline solution is a credible candidate for deployment and worthy of thoughtful consideration.

The factors contributing to the Sector Adoption Score for data pipelines are explained in more detail in the Sector Brief section that follows.