SAN FRANCISCO, CA – Today marks a fundamental shift in how enterprises build and deploy data platforms. DataForge, a revolutionary data platform framework, enables enterprise teams to go from zero to production-ready data infrastructure in under 30 days—a process that traditionally consumes 18 months and millions in consulting fees.
The platform addresses a painful reality: 73% of enterprise data initiatives fail not because of technology limitations, but because of implementation complexity. Platform teams spend months evaluating vendors, quarters integrating solutions, and years maintaining fragmented systems. Meanwhile, product teams wait, innovation stalls, and competitors leveraging modern data capabilities pull ahead.
DataForge changes this equation entirely. Built on a foundation of pre-integrated, enterprise-proven patterns, it provides platform teams with a complete data ecosystem that deploys like a single product. Product teams get immediate access to real-time analytics, ML-ready pipelines, and governance controls without waiting for infrastructure buildout.
"We've watched too many enterprises struggle with the same pattern," says Sarah Chen, VP of Enterprise Platforms at a Fortune 500 financial services company. "They hire consultants, evaluate dozens of vendors, spend months on POCs, and still end up with a fragmented mess. DataForge gave us in four weeks what we couldn't achieve in two years of traditional platform building."
The key innovation lies in DataForge's Implementation Velocity Architecture™. Rather than providing yet another set of tools to integrate, DataForge delivers a complete, opinionated platform with every decision pre-made based on proven enterprise patterns. Teams don't choose between streaming frameworks—DataForge provides the optimal one, pre-configured for enterprise scale. They don't design data models—DataForge includes industry-standard schemas ready for extension.
Early adopters report transformational results. A major retail chain reduced their data platform deployment from a planned 18 months to 26 days. A healthcare system went from data silos to unified patient analytics in five weeks. A financial services firm launched real-time fraud detection in half the time of their original estimate.
DataForge is available immediately for enterprise deployment, with a unique success guarantee: full production deployment in 30 days or your investment returned.
Q: Who exactly uses DataForge within an enterprise organization?
The beauty of DataForge lies in how it serves three distinct user groups simultaneously, each experiencing the platform differently. Platform teams interact with DataForge as infrastructure architects, using our control plane to manage deployments, set policies, and monitor system health. They see DataForge as a force multiplier—one platform engineer can now accomplish what previously required a team of five. Product teams interact through our developer SDK and self-service interfaces, treating DataForge as a set of building blocks for their applications. They don't need to understand the underlying complexity; they simply request data capabilities and receive them instantly. Data teams work within DataForge's notebook environments and pipeline designers, experiencing it as a familiar yet dramatically more powerful version of tools they already know.
The convergence happens in production. A product manager requests a new customer segmentation feature. A data scientist prototypes the model in DataForge's notebook environment, leveraging pre-built feature stores and training pipelines. A platform engineer reviews the resource requirements and approves deployment with a single click. The entire cycle—from idea to production—happens in days instead of months because all three teams work within the same unified platform, speaking the same language, sharing the same context.
Q: What does the first week with DataForge actually look like?
Day one begins with what we call the Platform Genesis—a four-hour session where DataForge automatically discovers your existing data landscape. Our discovery engine connects to your current databases, data lakes, and applications, mapping data flows and identifying integration points. By lunch, you have a complete visualization of your current state and a generated implementation plan. This isn't a consultant's PowerPoint; it's an executable blueprint with specific tasks, timelines, and success metrics.
Days two and three focus on core platform deployment. Your platform team runs our deployment automation, which provisions the entire DataForge infrastructure in your chosen cloud environment. This includes compute clusters, storage layers, networking configuration, security policies, and monitoring systems. The automation handles the thousands of configuration decisions that typically consume months of planning. By end of day three, you have a functioning DataForge platform processing your actual data.
Days four and five shift to enablement. Your first product team builds their initial data pipeline using our templates. They'll typically choose something meaningful but manageable—perhaps replacing a problematic batch job with a real-time stream, or consolidating scattered reports into a unified dashboard. The success of this first project creates organizational momentum. Other teams see real data flowing through real pipelines, producing real business value. The platform isn't theoretical anymore; it's operational.
By week's end, you've achieved what traditional approaches accomplish in months: a functioning data platform with active users, processing production data, and delivering business value. More importantly, your teams understand the platform's potential and are planning their next implementations.
Q: How does DataForge handle our existing data investments and technical debt?
Enterprises don't operate in greenfield environments, and DataForge acknowledges this reality through what we call Progressive Platform Migration. Instead of requiring a massive cutover, DataForge wraps around your existing systems like an exoskeleton, adding capabilities without disrupting current operations.
Consider a typical enterprise scenario: you have critical data in an old Oracle database that powers legacy applications, a data lake in AWS that holds historical records, a Snowflake instance for analytics, and various SaaS applications generating operational data. Traditional platform approaches would require you to migrate everything to a new system—a risky, expensive, time-consuming process that often fails.
DataForge takes the opposite approach. Our Transparent Gateway technology creates virtual unified access to all these systems while leaving them operationally unchanged. Your legacy applications continue running against Oracle. Your analysts continue querying Snowflake. But now, new applications built on DataForge can seamlessly access data from all systems through a single interface. More powerfully, DataForge maintains real-time synchronization between systems, ensuring consistency without requiring immediate migration.
Over time, you migrate systems to DataForge's native storage as it makes business sense. Perhaps you sunset the Oracle database when those legacy applications are retired. Maybe you consolidate Snowflake workloads during your next license renewal. The migration happens gradually, driven by business value rather than technical mandate. Throughout this process, DataForge maintains complete data lineage, showing how data flows between old and new systems, ensuring nothing gets lost in transition.
Q: What makes DataForge's architecture different from assembling best-of-breed solutions?
The fundamental difference lies in the elimination of integration complexity through what we call Coherent System Design. When enterprises assemble platforms from multiple vendors, they're not just integrating products—they're reconciling different philosophies, conflicting assumptions, and incompatible abstractions. Each component was designed in isolation, optimized for its narrow use case, indifferent to the broader platform context.
DataForge was designed as a single coherent system from the ground up. Every component knows about every other component. The streaming engine understands the storage layer's partitioning strategy. The query optimizer knows about the caching layer's eviction policies. The security system comprehends the data lineage graph. This isn't integration—it's orchestration at the architectural level.
This coherence manifests in profound ways during implementation. When you define a data schema in DataForge, that definition automatically propagates everywhere it's needed. The streaming system knows how to deserialize it. The storage layer knows how to partition it. The query engine knows how to optimize for it. The monitoring system knows what anomalies to detect. In a traditional integrated system, you'd define this schema multiple times, in multiple formats, hoping they stay synchronized. In DataForge, you define it once, and the system handles the rest.
The performance implications are dramatic. Traditional integrated platforms suffer from impedance mismatches at every boundary—data serialized and deserialized repeatedly, security checks duplicated at each layer, metadata synchronized through slow consensus protocols. DataForge's coherent design eliminates these boundaries. Data flows through the system in its native format. Security decisions are made once and enforced everywhere. Metadata updates propagate instantly through shared memory structures. The result is 10x performance improvement not through faster components, but through eliminated overhead.
Q: How does DataForge handle enterprise-scale data volumes and concurrent users?
Scale in DataForge isn't an afterthought or upgrade path—it's the fundamental design principle. We architected the system assuming enterprise reality: petabytes of data, thousands of concurrent users, millions of queries per day, and zero tolerance for downtime.
The architecture employs what we call Elastic Mesh Computing. Unlike traditional systems that scale vertically (bigger machines) or horizontally (more machines), DataForge scales in three dimensions simultaneously. Compute scales horizontally across nodes. Storage scales vertically within nodes through tiered memory hierarchies. And workloads scale temporally through intelligent scheduling that distributes processing across time to optimize resource utilization.
This manifests practically in how DataForge handles a typical enterprise scenario. Imagine it's 9 AM Monday, and your sales team launches their weekly pipeline that joins customer data with transaction logs—a 10TB operation. Simultaneously, your data science team starts training models on the same dataset. In a traditional platform, these workloads would compete for resources, degrading performance for both.
DataForge's mesh architecture handles this elegantly. The platform recognizes that both workloads need the same base data and creates a shared computation graph. The data is read once, processed through common transformations, then branched to serve both use cases. The sales pipeline gets priority for customer-facing outputs, while model training uses spare cycles and cached intermediate results. Neither team knows this optimization is happening—they just experience consistent, fast performance.
The system scales dynamically based on workload patterns. During your end-of-quarter reporting surge, DataForge automatically provisions additional compute nodes, redistributes data partitions for optimal locality, and adjusts query plans for parallel execution. When the surge ends, resources contract automatically. You pay for what you use, when you use it, without manual intervention.
Q: What about data governance, security, and compliance in a platform this integrated?
DataForge treats governance not as a layer added on top but as a fundamental property woven throughout the system's fabric. We call this Governance by Design, and it represents a philosophical shift from traditional approaches that bolt security onto existing systems.
Every piece of data entering DataForge gets tagged with an immutable governance context that travels with it throughout its lifecycle. This context includes classification (PII, confidential, public), lineage (where it came from), ownership (who's responsible), and compliance requirements (GDPR, HIPAA, SOX). These aren't just metadata labels—they're active constraints that the system enforces automatically.
When a developer writes a query joining customer data with transaction logs, they don't need to remember that European customer data requires GDPR compliance. DataForge knows. The query optimizer automatically applies appropriate filters, the execution engine ensures data residency requirements, and the audit system logs the access. If the query would violate policy—say, joining PII with anonymous data in a way that could enable re-identification—DataForge blocks it before execution, explaining why and suggesting compliant alternatives.
This extends to machine learning workflows, where governance becomes even more critical. When a model trains on sensitive data, DataForge tracks not just the data used but the information learned. If a model trained on European customer data gets deployed to process American customers, DataForge can detect potential compliance violations. The system understands that models can leak information about their training data and enforces appropriate boundaries.
The audit trail DataForge maintains goes beyond traditional logging. Every data transformation, every access decision, every policy evaluation gets recorded in a tamper-proof ledger. But more than recording what happened, DataForge can explain why. When an auditor asks why a particular user could access specific data, DataForge doesn't just show the access log—it reconstructs the entire decision chain: the roles assigned, the policies evaluated, the data classifications considered, and the specific rule that granted access.
Q: How do developers actually build applications on DataForge?
Developers experience DataForge through what we call the Progressive Complexity Interface. At its simplest, building a data application requires just three lines of code. But as needs grow more sophisticated, DataForge reveals deeper capabilities without requiring a complete rewrite. This isn't abstraction for simplicity's sake—it's carefully designed to match how developers naturally explore and expand their implementations.
Let me show you through a real example. A developer needs to build a customer churn prediction feature. They start with the simplest possible implementation:
churn_risk = DataForge.predict('customer_churn', customer_id)This single line triggers an enormous amount of sophisticated processing. DataForge identifies relevant data sources, constructs feature vectors, selects appropriate models, generates predictions, and returns results. The developer doesn't need to understand any of this initially—they just get a churn risk score that works.
As requirements evolve, the developer needs more control. Perhaps they want to customize features or adjust model parameters. DataForge reveals the next level:
pipeline = DataForge.Pipeline('customer_churn')
pipeline.add_features(['recent_support_tickets', 'payment_delays'])
pipeline.set_model_params(threshold=0.8, lookback_window='30d')
churn_risk = pipeline.predict(customer_id)Still simple, but now with precise control. The developer hasn't had to rewrite anything—they've just expanded their initial implementation. DataForge handles the complexity of feature engineering, model retraining, and deployment.
When the application needs production-grade capabilities—real-time processing, A/B testing, custom algorithms—DataForge reveals its full power:
@DataForge.streaming_job
def churn_detection_system():
events = DataForge.stream('customer_events')
features = DataForge.feature_store('customer_360')
return (events
.join(features, on='customer_id')
.window('5m')
.aggregate(custom_churn_logic)
.alert_when(risk > 0.9)
.dashboard('executive_metrics'))This isn't a different system or API—it's the same DataForge, revealing more capability as the developer needs it. The simple prediction from line one and the sophisticated streaming job share the same infrastructure, the same data, the same governance. A developer can start simple Friday afternoon and have a production system Monday morning.
Q: How does DataForge accelerate development velocity compared to traditional platforms?
The acceleration comes from eliminating entire categories of development work through what we call Assumption-Driven Development. DataForge makes hundreds of correct default decisions, allowing developers to focus solely on business logic rather than infrastructure puzzles.
Consider a typical data pipeline development cycle in traditional platforms. A developer spends the first week setting up development environments, configuring connections, and wrestling with authentication. Week two goes to designing schemas, setting up test data, and building basic transformations. Week three involves debugging serialization issues, optimizing performance, and handling edge cases. Week four focuses on deployment pipelines, monitoring setup, and production hardening. After a month, they have a simple pipeline that might work in production.
With DataForge, that same pipeline deploys in three days. Day one: the developer describes what they want in business terms using our declarative interface. DataForge generates the implementation, complete with error handling, retry logic, and monitoring. Day two: they test with production data clones, refine business logic, and validate results. Day three: they deploy to production with a single command, confident that DataForge handles scaling, fault tolerance, and operations.
The acceleration isn't just about initial development—it's about maintaining velocity over time. Traditional platforms suffer from velocity decay. Each new feature takes longer as technical debt accumulates, dependencies multiply, and complexity compounds. DataForge maintains constant velocity through architectural coherence. The tenth pipeline deploys as quickly as the first. The hundredth feature integrates as easily as the tenth.
This velocity extends to debugging and optimization. When a pipeline fails in traditional platforms, developers spend hours correlating logs across systems, reconstructing state, and identifying root causes. In DataForge, every execution maintains a complete trace. Developers can replay failures locally, step through transformations, and see exactly where things went wrong. What took hours now takes minutes.
Q: What if we need to extend DataForge beyond its built-in capabilities?
DataForge embraces extension through what we call the Open Core Architecture. While the platform provides comprehensive built-in capabilities, we recognize that every enterprise has unique requirements, legacy algorithms, and proprietary logic that need integration.
Extension points exist at every level of the stack, but they're not arbitrary hooks that break platform coherence. Each extension point is carefully designed to maintain system guarantees while enabling custom logic. When you add custom code, it doesn't run alongside DataForge—it becomes part of DataForge, inheriting all platform capabilities automatically.
For example, suppose your organization has developed a proprietary customer lifetime value algorithm refined over years. You don't want to reimplement it in DataForge's framework—you want to use your existing code. You register it as a Custom Operator:
@DataForge.custom_operator
def calculate_clv(customer_data):
# Your proprietary algorithm here
return clv_scoreThis simple decoration transforms your function into a first-class DataForge citizen. It automatically gains distributed execution across clusters, checkpoint/restart capabilities for fault tolerance, monitoring and alerting integration, and governance policy enforcement. Your proprietary logic runs unchanged, but now it operates at platform scale with enterprise reliability.
For deeper integration, DataForge provides the Extension SDK. This isn't just an API—it's a complete development framework that exposes DataForge's internal capabilities. You can build custom storage engines that integrate with the query optimizer. You can create specialized processing nodes that participate in the execution planner. You can even extend the governance system with organization-specific compliance rules.
A financial services client used this to integrate their proprietary risk models, developed over decades, directly into DataForge's execution engine. These models now run 100x faster thanks to DataForge's distributed processing, but the core algorithms remain unchanged, preserving years of refinement and regulatory approval.
Q: How does DataForge handle production incidents and debugging?
Production resilience in DataForge stems from what we call Observability-First Architecture. Every component, every operation, every data movement generates rich telemetry that feeds into our unified observability plane. But this isn't traditional monitoring that drowns operators in metrics—it's intelligent observability that understands context, detects anomalies, and guides resolution.
When an incident occurs, DataForge doesn't just alert you that something's wrong—it tells you what's wrong, why it's wrong, and how to fix it. The platform maintains a continuous model of normal behavior. When a pipeline that typically processes 10,000 records per second suddenly drops to 1,000, DataForge doesn't just flag the slowdown. It correlates this with other signals: a schema change upstream, increased data skew, or resource contention from another workload. The alert you receive includes this analysis, turning hours of investigation into minutes of resolution.
The Time Travel Debugger represents a revolution in production debugging. Every DataForge execution maintains a complete reproducible snapshot—not just of data, but of code versions, configuration states, and runtime conditions. When a pipeline fails at 3 AM, you don't try to reproduce the issue in development. You literally replay the exact production execution on your laptop, stepping through transformations, inspecting intermediate states, and identifying the precise failure point.
This extends to performance debugging. DataForge's Performance Profiler continuously analyzes execution patterns, identifying bottlenecks before they become problems. It might notice that a join operation's performance degrades when data skew exceeds 30%, or that a particular aggregation causes memory pressure on specific node types. These insights feed into the optimizer, automatically improving future executions. But they're also surfaced to developers with specific recommendations: "Partition on customer_region to improve join performance by 3x."
Q: What about disaster recovery and business continuity?
DataForge treats disaster recovery not as a separate system but as an inherent platform property through what we call Continuous Resilience. Every piece of data, every configuration, every pipeline definition exists in multiple places at all times. Failure isn't an edge case—it's an expected state the system continuously handles.
The architecture employs three levels of resilience. At the component level, every service runs in multiple instances across availability zones. If a compute node fails, workloads transparently migrate to healthy nodes. If a storage node fails, replicas immediately promote to primaries. This happens in milliseconds, typically faster than network timeout detection. Most "failures" are invisible to users.
At the regional level, DataForge maintains hot standby deployments in secondary regions. These aren't passive backups—they're active systems processing shadow workloads, staying warm and ready. Data replicates continuously using our Quantum Sync protocol, which guarantees consistency without sacrificing performance. In a regional failure, traffic redirects to the secondary region in under 60 seconds. Your pipelines continue running, your applications stay online, your business doesn't stop.
At the logical level, DataForge maintains complete system snapshots in immutable storage. These snapshots include not just data but entire platform states—every configuration, every pipeline definition, every security policy. You can restore your entire DataForge deployment to any point in time, useful not just for disaster recovery but for compliance, testing, and forensics.
The most powerful aspect is that disaster recovery requires zero additional configuration. You don't design DR plans, configure replication, or run disaster drills. DataForge handles everything automatically. Your only decision is RPO/RTO targets, which you express as simple business requirements: "Customer data must be recoverable within 5 minutes" or "Financial pipelines must maintain 99.99% availability." DataForge translates these requirements into technical implementations, adjusting replication frequencies, snapshot intervals, and standby resources automatically.
Q: How do we justify DataForge investment to our executive team?
The business case for DataForge writes itself when you understand the true cost of traditional data platform approaches. Enterprises typically spend $5-10 million on their first production data platform—not just in software licenses, but in consulting fees, implementation costs, and most painfully, opportunity costs from delayed time-to-market.
DataForge transforms this equation through three distinct value drivers. First, the velocity value: going from concept to production in 30 days instead of 18 months means your revenue-generating features launch five quarters earlier. For a typical enterprise launching data-driven products, this acceleration translates to $20-50 million in captured revenue that would otherwise go to faster-moving competitors.
Second, the efficiency value: DataForge eliminates entire categories of work. You don't need a six-person platform team spending a year on infrastructure. You don't need consultants designing reference architectures. You don't need specialized operators managing different components. A two-person team with DataForge accomplishes what traditionally requires 15-20 people. At typical enterprise salaries, that's $3-4 million in annual savings.
Third, the innovation value: when data platforms are hard to use, innovation dies. Product teams avoid data-driven features. Experiments take too long. Ideas wither in backlogs. DataForge makes data capabilities as accessible as calling an API. Teams that previously avoided data projects now build them enthusiastically. One client launched 47 new data-driven features in their first year with DataForge—more than they'd launched in the previous five years combined.
The ROI calculation is straightforward: DataForge pays for itself in the first quarter through efficiency gains alone. By year one, the velocity and innovation values deliver 10-20x returns. But the real value isn't in the spreadsheet—it's in becoming a truly data-driven organization while competitors struggle with infrastructure.
Q: What happens if DataForge doesn't work for us?
We stand behind DataForge with the industry's most aggressive success guarantee because we've engineered away traditional platform risks. If you don't have a production data platform running real workloads within 30 days, we refund your investment entirely. But let me explain why this guarantee isn't really a risk for either of us.
DataForge includes what we call Success Insurance—architectural patterns and operational practices that virtually guarantee successful deployment. The platform includes rollback capabilities at every level. Every change, every deployment, every configuration update can be instantly reversed. If a new pipeline causes problems, you roll back in seconds. If a platform upgrade introduces issues, you reverse it immediately. If a configuration change breaks integrations, you undo it instantly.
Beyond technical rollback, DataForge provides migration insurance. Your existing systems continue running unchanged while DataForge deploys alongside them. You're not replacing critical infrastructure on day one—you're adding new capabilities that gradually absorb existing workloads. If DataForge somehow doesn't meet your needs, your original systems remain intact and operational. You've risked nothing but the opportunity to transform your data capabilities.
The platform also includes extensive escape hatches. Every piece of data in DataForge can be exported in open formats. Every pipeline can be translated to standard Apache Beam or Spark code. Every model can be exported as standard ONNX or TensorFlow. You're never locked in—though after experiencing 10x developer productivity, we've never had a customer want to leave.
Q: What's the actual process for getting DataForge into our organization?
Starting with DataForge follows a proven three-phase journey that we've refined across hundreds of enterprise deployments. Each phase has clear objectives, measurable outcomes, and natural progression points.
Phase One is Discovery and Validation, typically taking one week. We begin with a half-day workshop where your platform and product teams describe their current challenges and future aspirations. Our Solution Architects then perform an automated assessment of your environment, discovering data sources, analyzing workloads, and identifying integration points. By week's end, you receive a detailed Implementation Blueprint showing exactly how DataForge will deploy in your environment, which use cases will launch first, and what value you'll capture when.
Phase Two is Pilot Deployment, lasting two to three weeks. We deploy DataForge in your development environment and work with your team to implement your first production use case. This isn't a toy proof-of-concept—it's a real solution to a real problem. Your team learns the platform while building something valuable. By the end of this phase, you have a functioning DataForge platform processing actual data and delivering measurable business value. More importantly, your team has hands-on experience and confidence in the platform.
Phase Three is Production Rollout, taking another week. We promote your pilot to production, onboard additional teams, and establish operational procedures. Our Customer Success team provides intensive support during this phase, ensuring smooth operations and rapid issue resolution. By day 30, you have multiple teams building on DataForge, production workloads running reliably, and a clear roadmap for platform expansion.
The entire process requires minimal time from your team—roughly 20 hours spread across the month. We handle the heavy lifting through automation and expertise. Your team focuses on defining requirements and learning the platform, not wrestling with infrastructure.
Q: What skills does our team need to successfully adopt DataForge?
DataForge democratizes data platform capabilities, making them accessible to teams without specialized expertise. If your developers can write Python or SQL, they can build on DataForge. If your operators can manage cloud resources, they can operate DataForge. The platform abstracts away complexity without hiding capability.
That said, successful DataForge adoption benefits from three roles, though they don't need to be separate people. First, a Platform Owner who understands your organization's data strategy and can make decisions about policies, priorities, and governance. This person doesn't need deep technical skills—they need business context and decision authority.
Second, a Technical Lead who can guide implementation and establish best practices. This person should understand software development, data processing concepts, and basic distributed systems principles. They don't need to be a distributed systems expert—DataForge handles that complexity. They need to understand what good looks like and help teams achieve it.
Third, Developer Champions who build the first applications and inspire others. These are your curious, motivated developers who enjoy exploring new technologies and sharing knowledge. They become DataForge experts not through formal training but through building real solutions and teaching others.
We provide comprehensive enablement for all three roles. Platform Owners receive strategic guidance on data platform evolution. Technical Leads get architectural deep-dives and best practices workshops. Developer Champions get hands-on training and direct access to our engineering team. Within two weeks, your team possesses all the knowledge needed for successful platform adoption.
DataForge represents more than a technology platform—it embodies a fundamental shift in how enterprises approach data infrastructure. The traditional months-long, consultant-heavy, integration-nightmare approach to platform building isn't just slow—it's obsolete.
In a world where competitive advantage comes from how quickly you can turn data into decisions, spending 18 months building infrastructure is corporate suicide. Your competitors are launching ML-driven features while you're still evaluating vendors. They're personalizing customer experiences while you're designing data models. They're capturing market share while you're integrating systems.
DataForge compresses 18 months into 30 days not through shortcuts or compromises, but through architectural innovation and operational excellence. Every enterprise data platform eventually needs the same capabilities: streaming and batch processing, storage and compute, governance and security, monitoring and debugging. DataForge provides all of these, pre-integrated, pre-optimized, and production-proven.
Your developers start building immediately. Your platform team manages one system instead of twenty. Your product teams launch features in days instead of quarters. Your executives see ROI in months instead of years. This isn't aspiration—it's the documented experience of every DataForge customer.
The question isn't whether you need a modern data platform—you do. The question is whether you'll spend the next 18 months building one or the next 30 days deploying one. With DataForge, that choice is finally yours to make.
Start your 30-day transformation today. Because in the time it takes your competitors to finish their vendor evaluations, you'll be in production, delivering value, and pulling ahead.