Decentralized applications (DApps) are often slower than their Web2 counterparts because they must organize blockchain data drawn from multiple sources. Industry voices converge on the view that data indexing is the critical infrastructure layer needed to unlock real-time performance for Web3 apps. This expansion in data flows—ranging from RPC endpoints to smart contracts and other blockchain components—creates a demand for a unified, scalable indexing approach. Leaders in the space argue that unless developers can rely on a robust indexing backbone, performance gains will remain out of reach for most DApps, forcing teams to build bespoke solutions that are costly, complex, and time-consuming.
This article examines the performance bottlenecks facing DApps, the role of data indexing in solving them, and the ambitious throughput goals being pursued by Ethereum’s ecosystem and Layer-2 networks. It also explores the implications for developers, the growing ecosystem of indexing providers, and the long-term roadmap shaping decentralized data infrastructure. By unpacking these interconnected threads, we aim to provide a holistic view of how indexing could become the next major catalyst for Web3 performance and scalability.
The Web3 performance bottleneck: why DApps lag behind traditional platforms
The tempo of decentralized applications hinges on the speed at which they can access and retrieve data stored across a distributed ledger. Unlike centralized Web2 systems that can optimize data access through singular storage layers, Web3 composites rely on a constellation of data sources. At the core are RPC nodes that serve as gateways to the blockchain, smart contracts that define programmable logic and state, and additional infrastructure that records, validates, and serves data. The volume of data on high-throughput chains can run into hundreds of terabytes when you factor in raw blockchain logs, historical states, event streams, and auxiliary metadata. The sheer scale creates a fundamental challenge: how to index and query this data efficiently so DApps can deliver responsive user experiences.
The indexing process is a way to transform raw blockchain data into an organized, queryable dataset. It involves extracting relevant information from diverse sources, organizing it into a coherent schema, and making it retrievable with low latency. The problem is not only about storing data but about making it accessible in a way that supports fast, reliable, and scalable reads for front-end applications, wallets, explorers, and other services that rely on up-to-date state or historical context. In this context, data indexing is not a peripheral infrastructure concern; it is a core capability that directly affects user-perceived performance. If indexing is slow or inconsistent, even well-designed applications can feel sluggish, fail to respond to user actions promptly, or exhibit stale data issues that undermine trust.
From the perspective of developers, the practical reality is that indexing is a specialized discipline that few teams are equipped to handle on a day-to-day basis. It requires expertise in data engineering, distributed systems, and database optimization, all within the constraints of a rapidly evolving blockchain environment. The result is a friction point: developers spend valuable time building and maintaining bespoke indexing pipelines, rather than focusing on app logic, user experience, and business features. This fragmentation not only slows time-to-market but also increases the risk of data inconsistencies, latency spikes, and operational debt. The governance and reliability of indexing systems become critical concerns as a project scales and interacts with multiple chains, layers, and cross-chain data streams.
Several factors contribute to the latency and inefficiency of Web3 data access today. First, the decentralized nature of data means that retrieval often involves stitching together information from multiple sources, sometimes across different nodes or networks. This multi-source aggregation adds overhead and variability in response times. Second, the heterogeneity of blockchains and their evolving data models creates complexity in maintaining up-to-date indexes. When new data formats emerge or protocol upgrades occur, indexing pipelines must adapt, which can temporarily degrade performance if not managed carefully. Third, data duplication and replication strategies, while essential for reliability, can add to storage and compute costs, complicating the design of fast, scalable query layers. Fourth, the lack of a universal standard for indexing and query interfaces across networks means developers must implement adapter code and custom integrations for each chain, further slowing progress and increasing maintenance burdens.
The overarching takeaway is clear: as throughput grows and blockchains generate more data, the need for a robust, centralized, or semi-centralized indexing layer becomes more acute. Without this layer, the performance gains promised by base-layer improvements and Layer-2 innovations risk being undermined by the overhead of data management. The data indexing challenge is thus not merely a technical detail but a strategic factor that will shape the adoption pace and success of Web3 applications in the coming years. In short, throughput is a double-edged sword: higher transactions per second demand more powerful and scalable data indexing to keep applications responsive and reliable.
Throughput’s direct impact on data management
The higher the throughput of a blockchain network—the metric commonly expressed as transactions per second (TPS)—the more data is generated. Every transaction, event, and state transition potentially adds to the dataset that must be indexed and served to applications. In October, notable figures in the ecosystem outlined ambitious goals to scale the Ethereum base layer and its suite of Layer-2 solutions to collectively exceed 100,000 TPS. This target is not only about raw transactional capacity; it implies a new regime of data throughput that must be matched by indexing systems to prevent bottlenecks at the data access layer.
Achieving such a throughput target would amplify the volume of data that needs to be ingested, labeled, and made queryable in near real-time. As throughput scales, indexing architectures must handle higher velocity streams, larger data footprints, and more complex query patterns. This requires innovations in data ingestion pipelines, event streaming, incremental indexing, and efficient storage structures. It also underscores the need for cross-chain interoperability within a broader ecosystem where the data fabric must cohesively present a unified view of user accounts, contract states, events, and liquidity flows across networks and sidechains.
The relationship between throughput and indexing quality is symbiotic. As networks push for higher TPS, the performance expectations for data retrieval rise in tandem. A robust indexing layer can unlock the benefits of high-throughput networks by delivering fast, consistent access to historical and real-time data. Conversely, if indexing cannot keep pace, even the fastest blockchains will feel slow to end users, dampening the perceived value of Layer-2 solutions and undermining confidence in decentralized apps. The industry recognizes this dynamic and is increasingly positioning data indexing as a foundational service—akin to a network infrastructure layer—that must be stable, scalable, and operationally robust to realize the full potential of scalable blockchains.
Data indexing as the missing infrastructure layer
Given the throughput-driven data deluge, a growing consensus centers on the importance of a dedicated data indexing layer. This layer acts as an intermediary that collects raw blockchain data from various sources, processes and normalizes it, and stores it in a way that supports fast querying. The goal is to transform disparate data streams into a coherent, reusable, and queryable dataset that DApps can leverage without reinventing the wheel each time.
What a robust indexing layer delivers
A mature indexing solution provides several critical capabilities:
- Unified data access: It harmonizes data from RPC endpoints, smart contracts, and other blockchain infrastructure into a single, coherent view. This reduces the need for bespoke integrations and simplifies the developer experience.
- Real-time and historical capabilities: It should support both current state queries and historical retrospectives, enabling features like time-travel debugging, auditing, and analytics.
- Efficient data organization: By employing optimized schemas, indexing strategies, and caching, it minimizes latency and ensures consistent performance under load.
- Fault tolerance and reliability: A resilient indexing service can tolerate node outages, network partitions, and other disruptions without compromising data integrity.
- Scalability and elasticity: As networks increase throughput or add new chains, the indexing layer should scale horizontally to accommodate growth without degraded performance.
- Operational simplicity: A managed infrastructure approach can offload maintenance, upgrades, and schema evolution from developers, enabling faster time-to-value.
The current state and the path forward
Today, many Web3 projects rely on in-house indexing pipelines, which are often bespoke, complex to maintain, and slow to adapt to rapid protocol changes. This reality leaves developers wrestling with data engineering challenges that pull resources away from core product development. In-house solutions are frequently built to address a specific dataset or chain, then fray under cross-chain data needs or evolving user requirements. The absence of a common standard for indexing and querying across networks further compounds these issues, creating a fragmentation that hinders interoperability and increases the operational burden for projects seeking to scale.
The industry response is moving toward standardized, provider-agnostic indexing frameworks that can ingest data from multiple chains, normalize it, and expose a consistent API for applications. In this vision, a data indexing layer would act as a shared utility—much like a database or a cloud service in the Web2 world—that developers can rely on to extract precise, timely insights from the blockchain. By decoupling data organization from application logic, this approach aims to accelerate development cycles, reduce duplication of effort, and enable more reliable performance as networks scale.
The role of indexing providers and how they fit into the ecosystem
Indexing providers, such as Pangea, are positioned to become the keystones of this emerging data infrastructure. Their value proposition centers on delivering a turnkey solution that abstracts away the complexity of data ingestion, normalization, and optimized query acceleration. By offering unified access to blockchain data across multiple sources, these providers reduce the need for each DApp to build its own custom indexing layer. They can also introduce performance guarantees, standardized schemas, and robust operational practices that help developers focus on product features rather than data plumbing.
From a broader perspective, the normalization of indexing services could enable a more modular and scalable Web3 ecosystem. Developers could mix and match data services, leverage consistent data models across chains, and deploy sophisticated analytics and user experiences without the overhead of maintaining bespoke data pipelines. This shift holds the potential to unlock new classes of applications that depend on rapid access to multi-source blockchain data, such as high-fidelity dashboards, real-time arbitrage and liquidity analytics, cross-chain analytics platforms, and user-centric experiences that require near-instant state updates.
Technical considerations for a scalable indexing layer
Designing a scalable indexing layer requires careful attention to several technical considerations. First, data ingestion must support high-throughput streams without becoming a bottleneck. Techniques such as streaming ingestion, log-based pipelines, and change data capture can be employed to capture event data promptly. Second, a consistent and extensible data model is essential to accommodate the diverse data types found on different chains, including transactions, events, state diffs, and contract interactions. Third, indexing latency must be minimized through efficient query planning, caching strategies, and optimized storage layouts. Fourth, data freshness and eventual consistency concerns must be addressed, especially for applications that require up-to-date information with tight latency constraints. Fifth, security and data integrity controls—such as cryptographic validation, tamper resistance, and robust access controls—are crucial to maintain trust in the indexed data. Finally, operators must consider cost efficiency, as data storage and compute costs can scale with the breadth of data indexed and the frequency of updates.
In this framing, the indexing layer is not a replacement for blockchain networks but a complementary, essential service that enables practical, scalable use of decentralized data. It sits at the intersection of data engineering, distributed systems, and blockchain protocol design, providing a pragmatic path toward the performance and interoperability goals that the ecosystem has set out.
Throughput and the roadmap: ambitious goals across Ethereum and Layer-2 networks
A central driver of the indexing conversation is the plan to handle much higher data throughput while preserving security and decentralization. The Ethereum ecosystem has outlined ambitious goals to scale both the base layer and the surrounding Layer-2 networks so that transactions per second, and the associated data flows, can be processed at scale. This section explores the core milestones, the technology stacks involved, and the intercoupled nature of throughput, interoperability, and data indexing.
Ethereum base layer and Layer-2 interoperability
Ethereum’s roadmap has long emphasized momentum on both the base layer and Layer-2 scaling solutions. The intention is not only to push the base layer toward higher throughput but also to enable more seamless interoperability between Ethereum and its growing family of Layer-2 networks. Layer-2 solutions, including optimistic and zero-knowledge (ZK) rollups, aim to process the bulk of transactions off-chain while preserving the security guarantees of the main chain. The resulting data streams—proofs, state roots, transaction bundles, and cross-layer messages—must be efficiently ingested, indexed, and made queryable for downstream applications. The synergy between base-layer throughput improvements and Layer-2 aggregation is what makes comprehensive data indexing even more critical; as more data flows through the ecosystem, the indexing layer must grow in capability and resilience to keep the experience fast and reliable.
StarkNet and the pursuit of higher TPS
Within the Layer-2 landscape, StarkWare’s StarkNet has emerged as a prominent solver for throughput challenges. At industry events, StarkNet’s leadership has underscored aggressive performance targets, including a plan to quadruple its transactions per second within a short horizon. A fourfold increase in TPS would position StarkNet as a competitive alternative to high-throughput networks, while also raising expectations for the corresponding data-access layer. Achieving such gains would demand not only improvements in the computation and proof systems that underpin ZK rollups but also enhancements in how data is sequenced, published, and indexed for application use. The implications for developers are substantial: higher TPS on StarkNet must be matched with indexing solutions capable of ingesting and serving data at a commensurate pace, preserving low latency and consistent reliability across a growing set of applications.
ZK-Rollups and the pursuit of cost-efficient throughput
ZK-Rollups, exemplified by projects like ZKSync, are pursuing aggressive throughput goals as part of their broader strategy to deliver scalable, affordable transactions. The project roadmap indicates plans to boost throughput to around 10,000 TPS by 2025, while simultaneously reducing transaction fees to incredibly low levels, potentially as low as one ten-thousandth of a dollar per transaction. These ambitions reflect a broader industry trend toward making decentralized use cases feasible at scale, including microtransactions and data-heavy interactions. The corresponding indexing challenges are non-trivial: increased throughput means more data to index and serve, while lower fees imply tighter margins for data services. The indexing layer must balance speed, cost, and reliability, ensuring that fast throughput does not outpace the ability to provide timely and accurate data to applications and users.
Solana and the benchmark of high throughput
Solana has consistently highlighted its high-throughput architecture as a key differentiator. Current public-facing figures place non-voting throughput in the range of roughly 800 to 1,050 TPS. The Solana ecosystem has cultivated considerable developer interest by prioritizing a monolithic design that emphasizes speed and predictability. As with other chains, the aim is to provide a performant data environment that supports complex, data-intensive applications. For DApps built on Solana, this throughput is a critical enabler for real-time gaming, high-frequency trading interfaces, and other demanding use cases. However, the ecosystem’s growth also intensifies the need for robust indexing to ensure that data from Solana, Solana-based Layer-2s, and cross-chain interactions can be consumed quickly and accurately by front-end apps and analytics platforms.
The broader market implications
The collective push toward higher TPS across base layers and Layer-2 networks signals a dramatic shift in the expectations placed on data infrastructure. As throughput expands, the demand for scalable, reliable indexing grows in lockstep. The presence of centralized or semi-centralized indexing services can help standardize data models, reduce redundancy, and improve cross-chain analytics. Yet these same services must uphold rigorous security standards and maintain trust in their data to avoid creating single points of failure in a highly distributed ecosystem. The balancing act between decentralization, reliability, cost, and performance will shape the adoption of indexing solutions and, by extension, influence how quickly Web3 applications can realize their envisioned user experiences.
The developer’s perspective: from in-house indexing to managed data services
The shift toward a standardized, scalable indexing layer has profound implications for developers. The current landscape often requires teams to invest significant time and resources into building, maintaining, and evolving their own indexing solutions. This in-house approach carries multiple downsides: it diverts engineers from core product development, introduces a bespoke stack that may be brittle in the face of protocol upgrades, and heightens the risk of data inconsistencies across apps and dashboards. In-house indexing can become an engineering drag race—every new chain or data source introduces a fresh integration, forcing teams to repeatedly reinvent the wheel.
A managed data indexing service promises to alleviate these pain points. By providing a consistent data model across chains, real-time and historical query capabilities, and robust operational excellence, such services can dramatically accelerate development cycles. They can also reduce the time-to-market for new features, improve data quality, and offer predictable performance that teams can rely on when designing user interfaces and analytics. For developers, the benefits include faster iteration, more reliable data delivery, and the ability to deploy more ambitious features without being gridlocked by data plumbing.
Yet, adopting a centralized indexing solution is not without considerations. Security and data sovereignty remain paramount; developers must evaluate how indexing providers handle data privacy, access controls, and governance. There is also the question of vendor-lock-in: relying on a single provider can raise concerns about platform dependence, pricing shifts, or policy changes. To address these concerns, the ecosystem may favor modular architectures that allow for interoperable data services, backward-compatible API layers, and clear migration paths. The outcome would be a more resilient, scalable, and flexible environment in which DApps can thrive without being mired in the complexities of data engineering.
Practical implications for product teams
Product teams can expect several tangible benefits from a mature indexing layer:
- Reduced time-to-market for new features that rely on complex data queries, historical insights, or cross-chain analytics.
- Improved user experiences thanks to lower latency for data-heavy interactions, such as live dashboards, on-chain analytics, and real-time transaction monitoring.
- Enhanced reliability and data integrity through centralized validation, deduplication, and consistency checks that help prevent stale or conflicting information.
- Greater ability to experiment with new business models that depend on rapid access to blockchain data, including dynamic pricing, real-time governance decisions, and data-driven collaboration features.
- Ability to scale with confidence as networks increase throughput, because the indexing layer is designed to absorb higher data volumes without sacrificing performance.
Implementation considerations and best practices
For teams evaluating indexing providers or considering an internal migration toward a dedicated indexing layer, several best practices can help maximize outcomes:
- Define clear data schemas and query requirements up front, including which data domains matter most (accounts, contracts, events, states, cross-chain data, etc.).
- Establish performance benchmarks and service-level expectations, including latency targets for common queries and worst-case failure scenarios.
- Plan for a staged rollout, starting with a subset of chains or datasets to validate reliability and performance before scaling.
- Ensure robust security controls, including access management, encryption, and tamper-evident data validation.
- Build a governance framework that accommodates protocol upgrades, schema evolution, and cross-team collaboration to maintain alignment as the data landscape evolves.
By aligning on these practices, developers can leverage indexing services to unlock the performance potential of Web3 platforms while maintaining control over data quality and security.
Market dynamics and roadmap: what lies ahead for decentralized data
The convergence of higher throughput aspirations and a renewed focus on data infrastructure signals a pivotal inflection point for the Web3 ecosystem. The market is increasingly recognizing data indexing as a strategic service rather than a back-end convenience. This realization is likely to drive a mix of investment, product development, and standards efforts aimed at delivering robust, interoperable indexing capabilities that can scale with the ecosystem’s ambitions.
Adoption drivers
Key factors driving adoption of indexing services include:
- Demand for real-time and historical data access across multiple chains, layers, and ecosystems.
- The need to reduce development time and complexity associated with building and maintaining in-house indexing pipelines.
- The expectation of consistent data quality and reliability across diverse data sources.
- The desire to unlock new revenue streams through analytics, dashboards, and data-driven products that rely on scalable data access.
Risks and considerations
As with any transformational shift, there are risks to be managed:
- Security and data integrity: Ensuring indexing services do not become attack surfaces or data leakage points remains essential.
- Governance and standards: Without widely accepted standards for data models and APIs, interoperability may lag, slowing broad adoption.
- Vendor risk: Relying on third-party indexing providers introduces dependency and potential pricing volatility.
- Latency variability: Even with indexing, extreme network conditions or cross-chain complexities can introduce latency that needs continuous optimization.
The long-term vision
In the long run, the industry envisions a robust, interoperable data fabric for Web3 that provides:
- A consistent, high-performance data plane spanning multiple blockchains and Layer-2 networks.
- A suite of standardized data schemas, query interfaces, and tooling that maximize developer productivity.
- An ecosystem of indexing providers that compete on performance, reliability, and cost, while adhering to common interoperability principles.
- A governance model that ensures security, privacy, and data integrity across the decentralized data ecosystem.
If achieved, this vision would enable a new class of applications and services—ranging from advanced analytics platforms to real-time governance dashboards—that were previously impractical due to data bottlenecks. It would also reduce the friction barrier for developers, accelerating innovation and user adoption across the decentralized landscape.
The road to a scalable, developer-friendly data layer: challenges and opportunities
While the promise of a scalable indexing layer is compelling, realizing it will require addressing several challenges head-on. The path forward involves technical innovation, collaborative standardization, and thoughtful governance to balance decentralization with operational practicality.
Key challenges to navigate
- Cross-chain complexity: The more data sources involved, the more difficult it becomes to maintain consistent data models, versioning, and query capabilities across chains.
- Real-time guarantees: Providing tight latency bounds in a decentralized context is non-trivial, given network variability and node distribution.
- Data privacy and access control: Ensuring that sensitive or private data is protected while remaining accessible to authorized applications is critical.
- Reliability under load: The indexing layer must scale predictably under peak demand, avoiding cascading failures or bottlenecks.
- Cost management: Balancing performance with operating costs is essential to keep data access affordable for developers and users.
Opportunities for innovation
- Modular architectures: Building indexing as a modular service with interchangeable components could reduce vendor lock-in and enable flexible scaling.
- Standardized data schemas: Developing community-driven data models and API contracts would facilitate interoperability and faster integration.
- Edge and caching strategies: Advanced caching and edge computing approaches could dramatically reduce latency for global users.
- Proven security frameworks: Implementing rigorous cryptographic validation and auditability would strengthen trust in the data layer.
The role of collaboration
Achieving these outcomes requires continued collaboration among blockchain protocols, Layer-2 teams, indexing providers, developer communities, and infrastructure operators. Shared standards, open specifications, and interoperable tooling will be essential to unlock the full potential of scalable, developer-friendly data access across the Web3 universe.
Conclusion
The relentless growth in blockchain throughput is reshaping the data landscape for decentralized applications. As networks push toward higher transactions per second, the demand for a robust, scalable, and developer-friendly data indexing layer becomes increasingly urgent. Leading voices in the space contend that solving the data organization challenge once—and providing a unified, efficient way to index cross-chain data—will unlock a new generation of responsive and capable DApps. By decoupling indexing from application logic and embracing standardized, high-performance data services, the Web3 ecosystem can reduce development overhead, accelerate innovation, and deliver more compelling user experiences at scale. The journey toward a scalable indexing backbone is ongoing, but it represents a practical and strategic pathway to realize the full promise of decentralized applications in a data-rich, high-throughput future.