Reliable AI starts with reliable data. The 26.3.0 and 26.4.0 releases advance Acceldata on three interconnected fronts: making pipelines understandable and actionable at enterprise scale, extending lineage from raw sources through to business outputs, and expanding integration coverage across the tools that matter most to modern data teams. The goal throughout is giving teams the context to understand what happened, why it happened, and what to do next.
01 - Pipeline Enhancements
At enterprise scale, pipelines grow to thousands of nodes spanning multiple systems and feeding dozens of downstream consumers. When something fails, the questions — where did this start, what changed, what else is affected — are hard to answer without the right tooling. The 26.4 release delivers two capabilities that shift the experience from detecting failures to understanding and resolving them.
Redesigned Pipeline Detail View
The enhanced Pipeline Detail View is built for the scale enterprise pipelines actually reach — 2,000 to 3,000 nodes and beyond — making the right information findable without navigating a monolithic graph.
- Search and jump directly to any task, dataset, or transformation by name — no manual scanning
- Failures and alerts are highlighted directly in the DAG, keeping investigations anchored to the pipeline structure
- Upstream and downstream paths are traversable from any node to trace dependencies and identify failure origins
- Related pipelines are displayed alongside the current DAG, providing immediate cross-pipeline context without switching views
Copilot Support for Pipeline Analysis
Better navigation reduces the time to locate an issue. Copilot reduces the time to understand it — bringing execution logs, lineage, and anomaly signals together to answer direct questions about what happened.
- Ask questions like “Why did this pipeline fail?” or “Which upstream task caused this issue?” and get grounded, contextual answers
- Copilot correlates pipeline structure, execution metadata, lineage relationships, and anomalies to produce a single unified explanation
- Root cause identification surfaces the most likely cause — not just symptoms
- Downstream impact assessment traces affected datasets, pipelines, and reports to establish the full blast radius
- Column-level lineage tracing enables precise debugging of specific data transformations
Key shift: From detecting that a pipeline failed to understanding why it failed and what it affects — without switching tools or context.
dbt Cloud in the Pipeline View
dbt Cloud pipelines are now automatically discovered and synchronized within the Acceldata pipeline view — including run history, execution metadata, and lineage — giving teams a unified picture across orchestration and transformation layers. Full connector details are in the Integrations section below.
02 - Lineage — Connected from Source to Business Output
Most tools trace lineage at a single level — tables, jobs, or reports — without connecting them into a coherent end-to-end view. At enterprise scale, data flows across schemas, pipelines, and BI layers before it reaches any decision. These releases extend lineage to cover that full journey.
Schema-Level and Collection Lineage
Rather than viewing lineage one asset at a time, teams can now see aggregated lineage across all assets within a schema, or across user-defined collections spanning multiple schemas — defined by filters, tags, or labels. This aligns lineage with how data actually moves: across functional domains and business workflows, not just technical asset boundaries.
- Aggregated upstream and downstream lineage across all assets in a schema
- Collection-based lineage across schemas, defined by tags, filters, or labels
- Function-level lineage nodes for deeper traceability
- Interactive graph and table views with node-level search
Power BI Lineage — Extending to the Business Layer
Lineage that stops at data assets misses the layer where decisions are actually made. The 26.3.0 release delivers end-to-end lineage across the full Power BI hierarchy — from source tables through dataflows, semantic models, and reports to individual dashboard tiles.
- Column-level lineage tracing for Snowflake, BigQuery, and Redshift down to individual columns in semantic models and dataflows
- Real-time impact propagation: when an upstream dataset changes, affected reports and dashboards are highlighted immediately
- Data quality scores travel from source tables through to BI assets, giving reports and dashboards the same observability context that exists at the pipeline layer
- Full Power BI hierarchy supported: Workspace → Dataflow → Semantic Model → Report → Dashboard → Tiles
Why this matters: When a source schema changes or data quality degrades upstream, the business impact is immediately visible — not discovered when a user notices something looks wrong.
SnapLogic–Snowflake Lineage
The 26.4 release closes two specific gaps in SnapLogic observability: lineage extraction for Snowflake assets within SnapLogic pipelines, and visibility into nested pipeline relationships.
- Snowflake asset lineage extracted via custom extractors, bypassing SnapLogic’s stale native lineage API
- Inter-pipeline linkage tracks parent-to-child pipeline relationships at any depth of nesting
- End-to-end tracing across orchestration and transformation layers without manual reconstruction
03 - New Integrations — Expanding Observability Coverage
These releases bring five integrations to General Availability, extending Acceldata’s coverage across real-time databases, federated query engines, integration pipelines, transformation layers, and BI reporting.
🚀 SingleStore (MemSQL) — GA • 26.3.0
Full Tier 1 observability for real-time, high-performance databases. SingleStore is built for speed — but data issues in a real-time system propagate instantly. GA includes flexible crawling and profiling, rule- and SQL-based quality monitoring, full and incremental reconciliation, schema and data drift detection, and deep data exploration. Cadence and Freshness policy support added in 26.4.
→ Read the full SingleStore announcement →
⚡ Trino Data Source Integration — GA • 26.3.0
Active crawling and direct profiling of data assets underneath Trino — not log inference. GA delivers automated metadata indexing, column-level profiling, anomaly detection, schema drift monitoring, reconciliation, and freshness tracking across Spark and Pushdown execution engines. Trino’s federated architecture makes it hard to observe; this integration closes that gap with direct, policy-driven observability.
→ Read the full Trino announcement →
🔗 SnapLogic Pipeline Integration — Enhanced • 26.4.0
Two targeted enhancements close the most significant observability gaps for SnapLogic workloads: Snowflake asset lineage extracted via custom extractors (bypassing SnapLogic’s unreliable lineage API), and inter-pipeline linkage for nested pipeline chains. Teams can now trace data across SnapLogic–Snowflake boundaries and through multi-hop pipeline relationships.
→ Read the full SnapLogic announcement →
🛠 dbt Cloud Connector — GA • 26.4.0
A fully redesigned dbt Cloud connector, rebuilt to address fragmentation in the previous implementation. Each dbt Cloud job is now a single unified pipeline in Acceldata. Lineage is reconstructed from dbt artifacts directly (manifest.json, run_results.json, sources.json) rather than Open Lineage events. All dbt resource types are supported. Test failures surface with affected row counts and compiled SQL. Circuit breaker behavior — downstream skipping on upstream failure — is now visible in the pipeline view.
→ Read the full dbt Cloud announcement →
📊 Power BI Lineage — GA • 26.3.0
End-to-end lineage across the full Power BI hierarchy, from source tables to individual dashboard tiles. GA includes column-level lineage for Snowflake, BigQuery, and Redshift; real-time impact propagation; quality score propagation into BI assets; and enterprise-scale crawler controls including multi-workspace support and regex-based filtering. Requires Data Plane v4.6.0.
→ Read the full Power BI Lineage announcement →
Across 26.3.0 and 26.4.0: pipelines that are navigable and explainable, lineage that spans from source to business output, and integrations that close the gaps in today’s most widely used platforms. All capabilities are available now — reach out to your Acceldata Customer Success contact or visit the documentation portal for setup guides and connector details.
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