Data engineering, automated

    Your Team's New
    Data

    Nile manages storage, compute, lineage, and versioning as one system,
    so AI agents can safely run data workflows end to end.

    NileNile Studio
    Ask Nile anything...
    WHAT IS NILE?

    One platform for your entire data stack

    Ask questions about your data, manage your pipelines, and visualize insights in one platform.

    Nile takes care of all the infrastructure: compute, storage, lineage, versioning, and rollbacks.

    Why teams adopt Nile

    Autonomous data workflows need a
    safe place to operate

    AI can now write queries, build pipelines, and modify schemas. But those changes still run directly on production, with no way to recover when something breaks.

    Nile gives data workflows the same model developers have with Git. Every change runs on an isolated branch before touching production. AI proposes. You review. You merge — or you don't.

    Propose change

    (AI)

    Run on versioned branch

    (isolated data)

    Review results

    (human)

    Merge or discard

    (safe prod)

    Every workflow runs in isolation before touching production data.

    The Solution

    A unified platform purpose-built for autonomous data workflows

    Nile gives AI the context, control, and safety to manage data workflows end-to-end. Unlike fragmented point tools, everything works together from the ground up.

    Run every workflow change on a versioned copy of your data
    See full lineage and downstream impact before it hits production
    Roll back instantly to any known-good version
    AI proposes and executes changes, teams review and approve
    nile-console
    $nile rollback sales --to version-122

    → Analyzing dependency graph...

    → Found 12 downstream tables

    → Cascading rollback in progress...

    ✓ Rollback complete in 1m 23s

    12

    Tables Updated

    1m 23s

    Recovery Time

    100%

    Data Integrity

    HOW IT WORKS

    Stop managing infrastructure. Start shipping.

    Get started in minutes. Scale to petabytes. Let AI handle the complexity.

    STEP 01

    Connect Your Data

    Point to any S3 location—we auto-detect schema and create queryable tables instantly. Import wizard handles partitions, data types, everything.

    IMPORT FROM 's3://your-bucket/data/'
    STEP 02

    Build Pipelines Naturally

    Write SQL or Python. Click 'Save as Table' and your query becomes a scheduled, versioned, dependency-tracked pipeline. No YAML files.

    SAVE AS TABLE sales_summary
    STEP 03

    Let AI Operate

    Ask questions in natural language. AI understands your schemas, lineage, and can execute queries, generate reports, and manage workflows.

    Ask: 'Are all of tables loaded with the latest data?'
    STEP 04

    Recover Instantly

    Something went wrong? Click rollback and the entire dependency tree cascades automatically. Minutes, not weeks.

    nile rollback sales --to version-122
    What you get with Nile

    Data Lineage

    Visualize how your tables connect. Trace data flow across your entire pipeline. See dependencies, detect issues, and maintain data quality with automatic lineage tracking.

    NileNile Studio
    ?
    Inventory Analysis×
    Product Performance×
    Store Trends×
    Customer LTV×
    Revenue Report×
    sales×

    finance.sales

    managed
    v1.main @ v1.0.14(current)
    OverviewSchemaLineageJobsVersions & BranchesDefinition
    Direction:
    Layout:
    finance / customersColumns (4)customer_idstringcustomer_namestringemailstringphonestringJobs (1)customer-generator2026-01-192026-01-182026-01-17finance / productsColumns (4)product_idstringproduct_namestringcategorystringsubcategorystringJobs (1)product-generator2026-01-192026-01-182026-01-17finance / storesColumns (4)store_idstringstore_namestringstore_typestringregionstringJobs (1)store-generator2026-01-192026-01-182026-01-17finance / salesColumns (4)transaction_idstringsale_datedatetransaction_timetimestampcustomer_idstringJobs (1)sales-generator2026-01-192026-01-182026-01-17finance / product_performanceColumns (4)report_datedateproduct_idstringproduct_namestringbrandstringJobs (1)product-aggregator2026-01-192026-01-182026-01-17finance / executive_dashboardColumns (4)report_datedateregionstringtotal_revenuedoubletotal_costdoubleJobs (1)executive-report2026-01-192026-01-182026-01-17finance / sales_by_storeColumns (4)report_datedatestore_idstringstore_namestringstore_typestringJobs (1)store-aggregator2026-01-192026-01-182026-01-17finance / sales_trendsColumns (4)report_datedateperiod_typestringtotal_salesdoubleavg_daily_salesdoubleJobs (1)sales-trends-calculator2026-01-192026-01-182026-01-17
    8 tables7 edges
    Declared (7)
    Detected (0)
    External
    ETL Codefinance.sales
    1"""
    2Self-generating sales fact table.
    3Generates 250-1000 sales transactions per job run by sampli
    4Each record is traceable via job_run_ts timestamp.
    5
    6Includes cost/margin columns needed by downstream aggregati
    7"""
    8
    9def transform_data(spark):
    10 from datetime import datetime
    11 from pyspark.sql.functions import (
    12 rand, col, expr, lit, current_timestamp, current_da
    13as floor, when, round spark_round
    14 )
    15
    16 import random
    17
    18 job_run_ts = datetime.now().isoformat()
    19
    20 # Read from upstream dimensions
    21 products = spark.table('finance.products').filter(col(
    22 customers = spark.table('finance.customers')
    23 stores = spark.table('finance.stores')
    24
    25 num_transactions = random.randint(, )2501000
    26
    27 # Sample from dimensions
    28 sampled_products = products.orderBy(rand()).limit(min(
    29 sampled_customers = customers.orderBy(rand()).limit(min
    30 sampled_stores = stores.orderBy(rand()).limit(min(, s20
    31
    32 # Cross join and sample to create transactions
    ...110 lines
    Core Primitives

    Four primitives that make AI reliable

    Built from the ground up—not bolted on. These foundations enable AI to safely operate on your data.

    Real Data Versions

    Data, schema, and transformations versioned together with Git-like branching.

    Zero-ETL Pipelines

    Write a query, save as table—instant production pipeline with scheduling.

    Built-in Lineage

    Automatic dependency tracking at partition level. AI explains impact before acting.

    Safe Recovery

    Point-in-time rollbacks cascade across tables and teams automatically.

    enables

    Agentic Control Layer

    A context-aware, reliable interface for AI to plan, execute, and validate workflows. Powered by real versions, lineage, and recovery—AI can operate safely while humans supervise.

    AI-Native Platform
    Real Data VersionsZero-ETL PipelinesBuilt-in LineageSafe Recovery
    Integrations

    Works seamlessly with your existing stack

    Powerful integrations, zero friction

    dbt
    dbt
    Apache Airflow
    Apache Airflow
    Snowflake
    Snowflake
    Databricks
    Databricks
    AWS
    AWS
    Downloads

    Get Started with Nile

    Download the desktop application for your platform and start managing your data with version control.

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    Version Latest

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    Version Latest

    Linux

    Version Latest

    View system requirements and more

    Ready to Dive In?

    Join the data teams who are shipping faster, recovering instantly, and trusting AI with their workflows.

    Or try the live demo
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