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A top-25 U.S. bank shatters the limitations of siloed data and delivers exceptional results.
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Built for petabyte-scale ambition. Proven at enterprise scale. Deploy on infrastructure you control.
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The majority of financial institutions say siloed data is holding back their fight against financial crime. Yet the appetite for agentic AI has never been higher. Will your investment in data architecture create the foundation for AI, or another layer it has to work around?
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DataWalk brings graph, relational and search analytics together in one memory-efficient, distributed platform. Analyze connected data at enterprise scale without assembling separate analytical engines. Deploy on infrastructure you control.
What happens when your graph reaches terabyte or even petabyte scale?
The infrastructure cost of growing data populations, connected relationships and analytical workloads.
02 The cost of complexity.The integration and operating burden of multiple analytical engines and data representations, with more potential failure points to manage.
03 The cost of change.The engineering work required when definitions, sources, relationships and requirements evolve.
As connected data grows, the compute and memory needed for complex analysis can become too expensive. Teams limit the questions, or the queries never finish.
DataWalk stores relationships once and reuses them, so queries need less compute and memory than rebuilding connections each time.
That means larger, faster analysis with less infrastructure cost.
DataWalk calculates and stores relationships as data enters the knowledge graph, then keeps supported relationships up to date. Queries use those established connections instead of reconstructing them for every question.
Traversals work on compact sets of references and fetch attributes only when results are used, so the full graph never has to sit in memory.
How storage and execution are separated
Holds the full graph: its data and relationships.
Keeps frequently used data close to each worker.
Runs queries, calculations, imports and refresh.
These are resource roles, not stages that data passes through in sequence. Storage, cache, and compute are sized for the workload.
Separate analytical stores mean more pipelines, more copies to reconcile, and more potential failure points for your team to manage.
Graph, relational and search analytics use the same data. Start with a search, follow connections across sources, and count or filter what you find in the same analysis. The search index lives inside the DataWalk database, so your team has no separate search service to run or keep in sync.
Fewer systems to integrate. Fewer copies to reconcile. Less engineering to maintain.
When graph, relational, and search engines each maintain their own analytical copy, engineers manage change-data-capture pipelines, reconciliation, backfills, permission enforcement, and recovery workflows to keep them aligned.
DataWalk removes the synchronization needed solely to keep those separate analytical stores consistent. Ingestion from source systems, caches, backups, and external integrations still require operation.
A new question, data source, or business definition can mean pipeline changes, custom refresh scripts, and engineering work before analysis can begin.
DataWalk lets your team change queries and configure the ontology. Graph Consistency manages supported dependencies as the data changes.
Less engineering to maintain. Less rebuilding when requirements change. More control for your team.
A new requirement does not always need a new data project. When the data and relationships are already available, your team can answer a different question by changing the query. When data or metadata must change, the engineering work matters.
Data changes
New and updated data can trigger chains of dependent relationships, rules, and calculations. Engineers must maintain custom scripts, coordinate execution order, and rerun calculations whenever upstream data changes.
DataWalk’s Graph Consistency manages declared dependencies and keeps supported derived relationships and facts up to date. Your team has less custom refresh logic to build and maintain.
Metadata changes
Adding attributes, changing definitions, or evolving the ontology can force engineers to modify ingestion code, rebuild mappings, and reprocess existing data.
Your team can often add an attribute or change a business definition directly in DataWalk’s ontology and keep using the graph you’ve already built. The work is limited to the mappings and calculations that depend on the change.
In an alternative stack, the same change can require engineers to rewrite ingestion code, rebuild mappings, and reprocess existing data.
Review the measured results and test conditions, then evaluate the workloads that matter to your organization.
Objects loaded and indexed in a single environment
To load and index 13 TB, end to end
Memory use stayed below 5% on each server during a 20-query test that built a 360° view of 2,500 objects.
Request-latency result, relationship depth and test conditions are published with the approved traversal benchmark.
Run DataWalk in your cloud, on premises or in an air-gapped environment. Your team controls where the data resides, how the platform is secured and how the infrastructure is operated. DataWalk does not collect telemetry from your deployment, and the platform sends no telemetry back to DataWalk. Size compute and storage for your workloads.
Prepare Kubernetes clusters, compatible object storage, a container image registry and an operations team with Kubernetes experience. Plan at least one non-production environment alongside production.
Sizing depends on data volume, query workload and concurrency. DataWalk provides reference sizing for development, test and production during deployment planning.
DataWalk supplies container images and Helm charts. Your team stages the images in your registry, then installs and upgrades DataWalk in your Kubernetes environment using DataWalk’s installation guidance.
In the standard model, your organization manages the Kubernetes infrastructure, deployment automation and ongoing operations, including monitoring, backups and upgrades. DataWalk provides installation guidance and administrator training.
Perpetual KYC. Calculated attributes feed automated risk rating and machine learning.
Cryptocurrency investigations. Runs fully air-gapped, correlating blockchain data with internal case data.
The architecture you choose determines how far you can explore, how much engineering it takes to get there, and how easily you can adapt when requirements change. Put DataWalk to the test with your data, your workloads and your infrastructure. Find out what’s possible without the usual limits on scale, complexity and change.
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