> ## Documentation Index
> Fetch the complete documentation index at: https://docs.moralis.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Warehouse Access with Apache Iceberg

> Query Data Feeds from your warehouse or notebooks through Apache Iceberg-compatible workflows, on platforms like Snowflake, BigQuery, Databricks, Spark, Trino, DuckDB, Pandas, and dbt.

export const RequestAccessButton = ({href = "https://admin.moralis.com/data-feeds", title = "Request an early-access account"}) => {
  return <a href={href} className="request-access-button" style={{
    display: "inline-flex",
    alignItems: "center",
    gap: "8px",
    padding: "12px 24px",
    borderRadius: "8px",
    background: "linear-gradient(135deg, #0f7fff 0%, #0a5fd9 100%)",
    color: "#fff",
    fontSize: "15px",
    fontWeight: 600,
    textDecoration: "none",
    border: "none",
    boxShadow: "0 2px 8px rgba(15, 127, 255, 0.35)",
    transition: "box-shadow 0.15s ease, transform 0.15s ease",
    width: "fit-content"
  }}>
      <style dangerouslySetInnerHTML={{
    __html: `
            .request-access-button,
            .request-access-button:hover {
              color: #fff !important;
              text-decoration: none !important;
            }
            .request-access-button:hover {
              box-shadow: 0 4px 14px rgba(15, 127, 255, 0.5);
              transform: translateY(-1px);
            }
          `
  }} />
      <span>{title}</span>
      <span aria-hidden="true">{"→"}</span>
    </a>;
};

Data Feeds stores decoded onchain data in a data lake designed for **Apache Iceberg**-compatible access.
That means your warehouse, query engines, and notebooks can read feed data using the tooling your data team
already works with, without you building and operating an ETL pipeline to get it there.

## Why Iceberg

[Apache Iceberg](https://iceberg.apache.org/) is an open table format for large analytical datasets. Instead
of a proprietary export, feed data is organized as tables that Iceberg-aware engines can read **in place**:

* **No ETL to maintain.** You query the data where it lands rather than copying it into your warehouse first.
* **Engine choice.** Iceberg is supported across the modern data stack, so the same tables serve SQL
  warehouses, Spark jobs, and local notebooks.
* **Fresh data.** Data is committed as it lands, so analytical reads see recent blocks without waiting on a
  batch export.

## Compatible platforms

Iceberg-compatible workflows cover platforms and tools including:

| Layer                     | Tools                                       |
| ------------------------- | ------------------------------------------- |
| Warehouses                | **Snowflake**, **BigQuery**, **Databricks** |
| Query engines             | **Spark**, **Trino**                        |
| Local / notebook analysis | **DuckDB**, **Pandas**                      |
| Transformation            | **dbt**                                     |

<Note>
  **Early access.** Integration availability for specific platforms can vary, confirm the platforms and
  workflows you plan to use when you onboard. See [Early access](../early-access).
</Note>

## Typical workflows

* **Warehouse SQL**: point Snowflake, BigQuery, or Databricks at feed tables and join onchain activity
  against your own data.
* **Notebook analysis**: pull feed data into DuckDB or Pandas for exploration and backtesting.
* **Batch pipelines**: run Spark or Trino jobs over full history for aggregation and feature engineering.
* **Modeled marts**: build dbt models on top of feed tables and ship curated datasets to your team.

## Warehouse access vs. the sink

The [database sinks](../concepts/destinations) land ready-to-query tables in a database you operate, which
suits **serving** workloads (an app querying balances, for example). Warehouse access suits **analytics**
workloads, where your data team queries feeds alongside the rest of the business's data. Both read the same
data lake, see the [access methods overview](./overview).

## Get connected

Catalog and connection details for warehouse access are provided during early-access onboarding.

<RequestAccessButton />