---

title: "Getting the Hang of Enterprise Blockchain Indexing: Architectures, Tools, and Top Tips" slug: "enterprise-blockchain-indexing-explained-architectures-tools-and-best-practices" description: "## Summary" Enterprise blockchain indexing has evolved to include ephemeral L2 blob data, multi-chain rollups, and super fast sub-second streams. In this guide, we'll dive into the latest architectures, practical tool options, and tried-and-true practices to help you create reorg-safe, analytical solutions." category: "Blockchain Technology" authorName: "Jay" coverImage: "https://images.pexels.com/photos/7567298/pexels-photo-7567298.jpeg?auto=compress&cs=tinysrgb&fit=crop&h=627&w=1200" publishedAt: "2025-07-24T21:05:01.637Z" createdAt: "2025-06-06T13:04:01.413Z" updatedAt: "2026-01-21T08:57:37.519Z" readingTimeMinutes: 12

Summary: Enterprise blockchain indexing has come a long way! Now, it includes temporary L2 blob data, multi-chain rollups, and lightning-fast sub-second streams. In this guide, we're going to explore the most recent architectures, practical tool options, and reliable methods that will help you create reorg-safe, analytics-ready pipelines for 2025.

Enterprise Blockchain Indexing Explained: Architectures, Tools, and Best Practices

Decision-makers these days can’t just get by with a basic “indexer.” You really need a solid pipeline that can (a) keep up with rollups and proto-danksharded blobs, (b) handle reorgs and finality like a champ, (c) send data directly to the tools your teams are already using, and (d) scale smoothly without those surprise cloud bills sneaking up on you. Here’s the playbook we’ve put together at 7Block Labs for building and auditing top-notch indexing that works for both startups and larger enterprises.

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What changed in 2024-2025 and why your old indexer is brittle

• L2 blob data is basically temporary storage. Thanks to the Dencun upgrade (EIP‑4844) on Ethereum, these “blobs” chill out on the consensus layer for about 4,096 epochs, which translates to roughly 18 days, before they get pruned away. After that, the only thing left on L1 are the KZG commitments. This update is great for reducing costs for rollups, but if you want to keep any historical batch data, you’ll need to snag those blobs while they're still around. Each blob is about 128 KB, and a block can hold up to 6 blobs, plus blob fees come with their own market. (consensys.io)
• OP Stack rollups (like Optimism and Base) have officially started using blob sidecars thanks to the Ecotone update during the derivation process. Just a quick note: if there's a blob transaction happening, the blob calldata might get missed. So, if you're working on indexing OP Stack chains, ensure your pipeline is set up to handle blob retrieval sources--it's not just about parsing the basic calldata anymore. You can dive into the details here.
• The Graph is shifting its economic activities to Arbitrum to cut down on costs. If you’re working with subgraphs, brace yourself for lower fees and a few adjustments in how things run on L2, including changes in staking, rewards, and querying. Check out the details here: (coindesk.com)
• Managed, warehouse-native crypto datasets have really evolved. Google Cloud is now introducing BigQuery public datasets for major blockchain players, including a Google-managed Ethereum dataset that comes with curated event tables. This is perfect for enterprise analytics and finance teams wanting to explore the data in detail. (cloud.google.com)

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Indexing architectures that work in 2025

1) Protocol-native streaming + subgraph stack (Substreams/Firehose + The Graph)

When you're searching for that perfect mix of high-throughput, low-latency, and reorg-aware extraction on EVM (and a few non-EVM) networks, here are the tools you should definitely check out:

• Firehose is really cool--it’s a streaming-first, file-based ingestion layer tailor-made for blockchain data. With its high throughput and neat features for handling cursor-based reorganizations, it’s definitely worth checking out. Take a look here: (firehose.streamingfast.io).
• Substreams lets you whip up composable Rust modules for parallel indexing, which is pretty awesome! You can share those results via subgraphs or send them directly to sinks. Plus, it gets the benefits of Firehose’s reorg handling and slick performance. Explore it further here: (github.com).
• The move of The Graph Network to Arbitrum is a total game-changer. It cuts down on operational gas costs (like delegation, indexing rewards, and query fee settlements)--which is super crucial when you’re looking to scale. Get all the details here: (coindesk.com).

When to Pick This Option:

• If you're looking for real-time product features, not just the usual business intelligence stuff.
• When you expect traffic to vary and want dependable cursors and consistent rebuilds.
• If your team is all about using Rust for those reliable, deterministic transformations.

Design Tip:

• Think about using Substreams for your deterministic transformations. It’s a clever way to manage business joins a bit further down the line in your warehouse, allowing you to keep your compute tasks separate from your replay processes. Don’t forget to capture your state every N blocks and leverage cursors for precise reorganization rewinds. You can dive into it here: github.com

2) Warehouse‑first indexing (BigQuery public datasets + managed Ethereum)

For analytics teams that live and breathe SQL--and if you’re aiming to blend on-chain data with CRM and finance info:

• BigQuery's public datasets just got a major upgrade with new Layer 1s and Layer 2s like Arbitrum, Optimism, Polygon, and Tron joining the mix. Plus, there's now a Google-managed Ethereum dataset that includes curated event tables for ERC20, ERC721, and ERC1155 tokens. This really saves you from all that pesky DIY ETL work! (cloud.google.com)

When to Choose:

• Your primary users are analysts and data scientists, which is super important to keep in mind.
• You need to have governance, cost management, and some standard tools in the mix, like Looker and dbt.
• A little bit of latency doesn’t bother you--you're thinking in terms of seconds to minutes.

Design Tip

• Go ahead and partition by block_time/date and cluster by address/contract. It’s a great idea to whip up some materialized views for those frequently accessed queries, such as balances and token transfers. For your cold storage needs, think about using Iceberg or Parquet to help keep your expenses down.

3) Self‑hosted node + trace modules (Erigon/Geth/Nethermind) for deep EVM introspection

For situations where you’re looking to dig into internal call graphs, check out state diffs, or get a closer look at execution details:

• Erigon packs some awesome features with its flexible and filterable “parity-style” trace RPCs like trace_block, trace_filter, trace_replayTransaction, and stateDiff. These tools are great for diving deep into forensic indexing and tackling those tricky compliance rules. Take a look here.
• On the flip side, Geth comes with a variety of built-in tracers via debug_traceTransaction. These include a struct/opcode logger and JavaScript tracers. It's perfect for when you're after detailed instruction-level traces but aren't in the market for those parity-style batch filters. You can dive deeper into it here.

When to Choose:

• When you need to piece together internal calls or MEV paths, or if you're looking to double-check specific state changes instead of just skimming through logs.
• If you have the power to manage the infrastructure and can run archive nodes (or a similar setup with selective history).

Design Tip:

• If you're looking for those parity-style filters or need to do some deep historical scans, Erigon is the way to go. But for quick spot checks, you can’t beat Geth’s opcode tracer. Just remember to save your client version and adjust your prune policy so it aligns with your retention SLO. More info can be found here: (docs.erigon.tech)

4) High‑throughput non‑EVM streams (Solana Geyser)

For Solana DeFi/NFT Apps and Alerting/Bots:

If you're getting into Solana DeFi and NFTs, or if you want to set up some alerts and bots, here are some awesome resources and tools that could come in handy:

DeFi Apps on Solana

• Raydium: This is a super efficient AMM and liquidity provider. Take a look at it here.
• Serum: A decentralized exchange that's all about fast and affordable trading. Dive into the details here.
• Mango Markets: If you're into margin trading, this one's for you. Discover more here.

NFT Platforms on Solana

• Metaplex: This is your go-to framework for creating and launching NFTs. Dive in and get started here.
• Magic Eden: One of the top NFT marketplaces on Solana. Take a look and explore it here.
• Solanart: Another great NFT marketplace packed with tons of collections. Don't miss out, check it out here.

Alerting and Bot Tools

• Solana Beach: A fantastic tool for keeping an eye on transactions and wallets. Check it out here.
• Pyth Network: Perfect for real-time price feeds and market insights. Dive in here.
• The Graph: Great for creating subgraphs that make data access a breeze. Start exploring here.

Helpful Communities

• Solana Discord: Jump into the Solana community for some great discussions and support. You can check it out here.
• Twitter Spaces: Keep an eye on related projects and hop into conversations about the latest trends.

Sample Code for Alerts

Here’s a quick guide on how to set up alerts using JavaScript:

const alertThreshold = 100; // Set your price alert threshold

function checkPrice(price) {
    if (price > alertThreshold) {
        console.log(`Alert! Price has exceeded ${alertThreshold}: Current Price is ${price}`);
    }
}

// Example usage
checkPrice(120);

Just adjust that code to fit your requirements, and you'll be all set to create some awesome alerts!

Final Thoughts

The Solana ecosystem is really hopping right now, packed with all sorts of activities and chances to dive into. Whether you're into trading DeFi tokens or minting some cool NFTs, there's tons for you to check out and enjoy! Have fun exploring!

• Geyser plugins are a game-changer for streaming validator events--think accounts, transactions, and slots--directly to platforms like Kafka, RabbitMQ, gRPC, and Postgres. If you're eager to get started, there are some great, production-ready Kafka plugins you can set up right away. Take a look here: (github.com).
• Ops tip: If you're jumping into Solana streaming on a larger scale, make sure to go for high-core, high-RAM hosts and properly adjusted gRPC servers. The community has compiled some really useful reference specs and tuned defaults to guide you along the way. Check out all the details here: (solana-dapp.slv.dev).

When to Choose:

• You need account or transaction updates in less than a second.
• You're developing real-time monitoring tools, liquidation bots, or on-chain UIs that operate under tight Service Level Agreements (SLAs).

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Finality, reorgs, and correctness: how to get this right

• Finality on Ethereum typically takes around two epochs, which is roughly 12.8 minutes. When you're diving into your ETL process, it’s wise to separate the “head” (which updates fast, but is subject to change) from the “finalized” view (which is a bit slower but more reliable). Be sure to label this clearly for anyone down the line who's working with the data. (inevitableeth.com)
• If you want to stay updated on finality, give the Beacon API a try. You can either subscribe to events like finalized_checkpoint and chain_reorg, or just check in regularly for finalized checkpoints. This approach will help you keep your materialized tables in sync. (ankr.com)
• Keep in mind that finality delays can pop up from time to time--just think back to May 2023! We might see something similar again. It's a good idea to put some backpressure in place. If finality gets stuck, take it easy on merges and wait a bit before adding any new heads until the finalized checkpoint starts moving along. (blockworks.co)
• When it comes to OP Stack chains, remember that withdrawals have a challenge window of about 7 days. So, when you're looking at your "settled" metrics, factor this in. On the bright side, after Bedrock, deposits will be confirmed much faster. It’s also a good idea to index both L2 outputs and L1 events to stay on top of the bridge state. (cipheredge.org)
• When you're indexing OP chains that make use of blob transactions (like Ecotone), keep in mind that you’ll need to pull blobs from beacon nodes or external blob stores. Simply depending on calldata might not be enough. (specs.optimism.io)

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Practical, real-world patterns (with implementation notes)

A) L2 rollup pipeline (OP Stack: Base/Optimism) with blob capture

Goal: Quick UX Metrics with Accurate Settlement and Future-Proof Blob Replay

We’re looking to gather UX metrics in under a minute. This means we need to nail down the settlement process and make sure our blob replay system is built to stand the test of time.

• Sources:
  • We're connecting to the L2 execution node to pull in live blocks and logs (that's our speedy route).
  • For OptimismPortal deposits and output roots, we're relying on an L1 Ethereum node (that's our settlement route).
  • Plus, we’ll be adding a Beacon node (after Ecotone) or a blob retrieval service to fetch blobs tied to Batch Submitter Type‑3 transactions. Check it out here: (specs.optimism.io)
• Flow:
  1. First things first, we’ll gather the L2 blocks and whip up a “head” topic that includes the block number and l2_timestamp.
  2. Next, we’ll keep our eyes peeled on L1 for any output root proposals and their statuses. This will help us create a “settlement” topic, which will be keyed by the L2 block range.
  3. For those batches that come with blobs, we’ll need to snag blob sidecars within 18 days and stash them away in S3/GS, all organized by slot and epoch. (consensys.io)
  4. Finally, we’re going to set up two tables: l2_events_head (which should be pretty quick) and l2_events_finalized (this one will join with output roots and get updated every hour).
• SLOs:
  • UX dashboards will grab data straight from the head, whereas finance and risk will depend on the finalized data.
  • If we hit an L1 reorg that affects epoch N, our go-to policy is to invalidate any impacted L2 epochs and replay from the last invariant checkpoint.

B) “Save the blobs” data retention

If you’re looking to roll up historical analytics or audits that stretch past 18 days, you’ll want to actively save those blobs. The Graph ecosystem has a great way to capture, store, and keep blob data searchable for the long term. If you're not already on that stack, you might want to consider setting up a similar sidecar service. For more info, check it out here.

Implementation Sketch

• First things first, make sure you subscribe to the beacon nodes for the blob sidecars that correspond to the batcher transaction hashes.
• After that, hang onto the raw blob payloads, and don’t forget to keep a decoded index. This index should include info like chain_id, batch_tx_hash, blob_index, l1_block, and epoch.
• Finally, set up a resolver that connects KZG commitments to your stored payloads. This will come in handy for any historical replays or audits you might need to run later on.

C) Solana NFT/DeFi indexer with Geyser -> Kafka -> ClickHouse

• If you’re diving into account updates and transactions, definitely give the Geyser Kafka plugin a shot! Just remember to have an allowlist set up for programs like Metaplex and the Token Program. You can grab it here on GitHub.
• Make sure you get everything into ClickHouse and set up row TTLs for your hot tables. Then, for your cold storage needs, backfill to S3 Parquet.
• For keeping things idempotent, key your Kafka messages with (slot, tx_signature, index). Also, don’t forget to use a merge tree with a version column (slot) so you can sort out updates consistently.
• When it comes to capacity planning, definitely stick to the community-shared best practices for managing those high-load gRPC/Kafka setups. It might be smart to provision some bigger memory boxes for your validators and streamers. You can read more about it here.

D) Deep EVM introspection with Erigon/Geth

• If you’re diving into on-chain compliance or tackling MEV/regulatory analyses, you can snag parity-style traces using Erigon’s trace_filter or trace_block methods for specific addresses. And if you hit any tricky opcode-level scenarios, just switch over to Geth’s debug tracers. Check it out here: (docs.erigon.tech).
• Don’t forget to store those normalized trace actions (like call, create, selfdestruct) along with stateDiffs in some columnar storage. This makes it super easy to join them on the fly with logs and receipts whenever you need to dig into the details!

E) BI‑first: BigQuery Ethereum and multi-chain datasets

• If your organization is jumping on the BigQuery bandwagon, don’t forget to check out those public datasets for networks like Optimism, Arbitrum, Polygon, Tron, and Google-managed Ethereum. They include some handy curated event tables that can seriously reduce your ETL time and give analysts consistent schemas to work with. You can dive deeper into this here.

Example Query Idea (ERC20 Transfers by Day, Curated Tables)

Have a look at the managed Ethereum dataset’s event tables--it'll save you from the headache of manually decoding topics in SQL. With this, you can effortlessly whip up 7-day rolling summaries for your dashboards. Check it out here: cloud.google.com

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Emerging tools that reduce time-to-value

• Substreams + Firehose: This combo gives you top-notch, speedy, and reorg-aware indexing, all made possible by Rust modules and streaming sinks. It's just what you need to whip up real-time features that are good to go for product use. Take a look on GitHub.
• The Graph on Arbitrum: If you want to save some cash on fees for indexers, delegators, and subgraph consumers, this is a total game-changer. It’s super budget-friendly, especially when you're trying to scale up. Check out the full story on CoinDesk.
• Goldsky Mirror: This nifty tool lets you stream raw blocks, logs, and traces (even your subgraph) directly into Postgres, ClickHouse, S3, or Kafka, all with some really fast sub-second latency. It’s compatible with over 100 chains and private subgraph endpoints, making it super convenient for fetching data right inside your VPC. Check it out on Goldsky.
• Satsuma Data Warehouse Sync: This handy feature lets you take snapshots of subgraph entities and sink them into BigQuery or Snowflake on a regular schedule. It’s super helpful for ensuring your analytics stay in sync with your subgraph schema. For more details, check out the Satsuma docs.
• Aptos Indexer SDK: This cool SDK is crafted in Rust and uses a step-function processor pattern for Move events and writesets. Plus, it’s got templates and processor status tracking built right in. If you're diving into the world of Aptos, this is definitely worth a look! You can find all the details on Aptos.

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Best practices we recommend (and implement)

1. Make finality a top priority

• Always remember to balance the quick thinking of the “head” with the absolute certainty of the “finalized.” Use beacon finalized checkpoints as your guiding light through SSE or debug endpoints. And hey, if you notice finality lagging, make sure to backfill it. (ankr.com)

1. Keep replays budget-friendly

• Store the raw block/log payloads (or outputs from the Substreams module) in Parquet format on object storage. With deterministic and idempotent transforms in play, you'll be able to rewind to any cursor and reconstruct everything without a hitch. Take a look on GitHub!

3) Capture Blobs Proactively

• If you're getting into indexing OP-style rollups, don't forget to either run a blob capture sidecar or partner with a provider that can "save the blobs." Otherwise, you might lose your historical batch data after just 18 days. (consensys.io)

1. Choose Erigon for address-filtered historical traces

• When you're tackling big scans over long periods, Erigon’s trace_filter in archive mode really beats the usual per-transaction traces. And if you need to dive into a specific transaction, you can always rely on Geth’s opcode tracers. Take a look here: (docs.erigon.tech)

1. Don’t get too bogged down with EVM logs

• For OP Stack, remember to index both L2 blocks and L1 bridge/output contracts. If you’re diving into Aptos, you'll need to keep an eye on Move events and writesets. And for those working with Solana, don't forget to stream account updates and transactions using Geyser. It’s all about making your model fit the native data of the chain. (specs.optimism.io)

1. Keep warehouse costs in check from the start

• Split tables by date and group them by addresses or contracts. Pre-aggregate those hot metrics and make sure to double-check the numeric precision (don’t forget about those token decimals!). And whenever possible, go for those curated managed datasets. For more info, take a look at this post on (cloud.google.com).

7) Bake in Observability

• Keep an eye on the chain_reorg and finalized_checkpoint events happening on your beacon nodes. It's a good idea to emit your own watermarks and lag metrics for every topic or table you’re tracking. And hey, don’t overlook the importance of setting up alerts for any stalls compared to the head. Check out more details at (ankr.com).

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A brief decision framework (use this in your RFPs)

• Looking for low latency?
  • If you’ve got a UI or bots that need responses in under a second, you might want to check out Substreams/Firehose, Goldsky Mirror, or Solana Geyser. You can find more info here: (firehose.streamingfast.io).
  • On the other hand, if you’re okay with waiting a few minutes for some analytics, BigQuery’s managed and public datasets are a solid choice. Check it out here: (cloud.google.com).
• Need to dig into internal traces or state diffs?
  • Totally! Just launch Erigon using the trace_* commands, and make sure to explore the Geth debug tracers while you're at it. You can grab more details here.
• Are you indexing OP Stack chains using blobs?
  • Double-check that you're all set up for blob retrieval and archival since it's essential for Ecotone derivation. You can find the details here.
• Want to leverage subgraphs while keeping your data snug in your VPC?
  • Take a look at Goldsky Mirror replication or Satsuma Warehouse Sync. (goldsky.com)

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Implementation checklist (copy/paste for your program plan)

• Governance
  • Let’s clear up the difference between “finalized” and “head” consumers, as well as the SLAs.
  • We should finalize the blob retention policy, aiming for a minimum of 18 days, plus a bit of extra safety margin. (consensys.io)
• Sources
  • For EVM: we'll fire up an archive or near-archive node, then snag the Beacon API to keep tabs on finality.
  • For OP Stack: we’ll need an L2 node and the L1 portal/output contracts to fetch those blobs. Check it out here.
  • For Solana: we’re going to utilize a validator along with the Geyser Kafka/gRPC plugin. You can find more on this GitHub page.
• Pipeline
  • When it comes to real-time data, we can check out Firehose/Substreams or Geyser->Kafka; we’ll be setting up schema-versioned sinks for that.
  • For batch processing, let’s take advantage of BigQuery’s public and managed datasets, along with dbt models and materialized views. You can find more details here: (firehose.streamingfast.io)
• Storage
  • When it comes to hot storage, you might want to think about using Postgres or ClickHouse. They both work really well when you set up partitioning by time and entity.
  • For cold storage, I recommend going with Parquet together with Iceberg or Hive metastore. This combo will help you maintain a rewindable history.
• Observability
  • We'll get the Beacon SSE subscriptions rolling and monitor the lag metrics for each sink. And hey, make sure to set up data contracts for every entity! (ankr.com)
• Validation
  • Let’s take a moment to cross-check the results from our subgraphs with the raw node logs and the warehouse aggregates. It’d be great to run some diffs on the snapshots to spot any discrepancies.
• Cost controls
  • Let's get our retention policy in line, compress those Parquet files, and pre-aggregate the hot metrics. Plus, whenever we can, we should definitely tap into those curated Ethereum event tables. They’re a game changer! (cloud.google.com)

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Frequently asked technical questions (with crisp answers)

• “How long do we have to capture blob data?”
  You’ve got around 4,096 epochs to work with, which is about 18 days. After that, the nodes will start pruning the data. So, be sure to map out your data capture plan well within that window. (consensys.io)
• “Can we rely on calldata forever for OP Stack batches?”
  Not really! Once the batches are submitted as blob transactions, we switch to pulling up blobs after Ecotone derivation. So, don’t forget to capture and save those blobs. (specs.optimism.io)
• “What’s a comfy Ethereum reorg buffer?”
  To get some solid immutability, you can count on those beacon finalized checkpoints, which take about 12.8 minutes. When it comes to what’s considered a “safe-enough” user experience, plenty of teams are cool with N blocks. Just keep in mind that only finalized checkpoints give you those strong guarantees. (inevitableeth.com)
• “We need some traces for compliance--which client should we use?”
  For those larger historical scans, I’d recommend using Erigon with trace_filter. If you're looking to get into the nitty-gritty opcode details of specific transactions, then Geth's debug tracers are definitely your best bet. You can find all the info you need here.
• “We’re on the hunt for subgraphs and warehouse joins too!”
  You can easily deploy subgraphs and sync them to your warehouse with Goldsky Mirror, or give Satsuma’s warehouse sync a shot to pull in subgraph entities into BigQuery or Snowflake. Take a look here: (goldsky.com)

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Where 7Block Labs fits

We take care of everything involved in designing, implementing, and running these pipelines from beginning to end:

• Substreams/Firehose subgraphs equipped with reorg-safe sinks
• Blob capture services built on the OP Stack, along with historical blob archives
• Solana Geyser Kafka clusters that integrate with ClickHouse/S3 tiers
• BigQuery-centric analytics configured with cost guardrails and dbt
• Comprehensive EVM trace infrastructures (like Erigon/Geth) for compliance and forensic analysis

If you're on the fence about building or buying for 2025, don’t worry--we’ve got your back in figuring out what you really need based on the architecture we talked about earlier. We can have a pilot up and running in just 2 to 4 weeks. After that, we’ll hook you up with the runbooks, Infrastructure as Code (IaC), and Service Level Objectives (SLOs).

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References and further reading

• EIP‑4844 blobs: There's some cool stuff happening with retention, size, and the fee market. For all the juicy details, check out Consensys or swing by Etherscan.
• The Graph’s move to Arbitrum: This shift is set to shake things up big time regarding fees and operations. Get all the info over at CoinDesk or The Block.
• Firehose documentation: If you're digging into the Substreams repo, don't miss out on the awesome capabilities detailed in the Firehose docs.
• BigQuery updates: Google is leveling up with some managed/public blockchain datasets and some pretty neat Ethereum curated tables. Get the full scoop at Google Cloud.
• OP Stack derivation: Curious about Ecotone blob retrieval and all that jazz with deposits and epochs? Check out the specs over at Optimism.
• Erigon trace module: Want to dive deeper into Geth tracers like debug_traceTransaction? You can find all the details in the Erigon docs.
• Solana Geyser Kafka plugins: There’s some handy community catalog stuff and notes on performance operations waiting for you. Look it up on GitHub.
• “Save the blobs”: Discover The Graph’s plan for keeping blobs available in the long haul by checking out their latest post at The Graph.

Ready to Map Out Your Indexing Roadmap?

At 7Block Labs, we’re all about helping you figure out the best options for your needs, whether it's latency, accuracy, or keeping within your budget. Plus, we'll set you up with a slick production-ready pipeline that your teams can handle with ease.