Best data integration tool

Best HTTP to Databricks data integration tool

Need the best tool to move or migrate HTTP data into Databricks? Use ingestr for the open-source copy job, then add Bruin Cloud when the pipeline needs schedules, checks, lineage, alerts, and audit trails.

Open-source CLI Incremental loads Production ready

Short answer

Use ingestr when you need a direct, scriptable HTTP to Databricks move.

For a HTTP to Databricks migration, start with ingestr: an open-source CLI, a repo-friendly command, and incremental loading when the source supports it. Bruin Cloud is the upgrade when the same job needs scheduling, quality checks, lineage, alerts, and audit logs.

Start with a local CLI command and commit the workflow to your repo.

Use incremental or time-based loading when the source supports it.

Verify row counts and schema expectations before scheduling.

Add Bruin Cloud for orchestration, lineage, checks, alerts, and audit logs.

What you'll learn

How to install and set up ingestr in seconds
Connect to HTTP and Databricks with proper authentication
Copy entire tables or specific data with a single command
Set up incremental loading for continuous data synchronization

Prerequisites

  • Python 3.8 or higher installed
  • API endpoint URL
  • Authentication credentials (if required)
  • Databricks workspace with SQL endpoint
  • Personal access token generated
  • SQL endpoint running (not terminated)
  • Appropriate permissions on catalog/schema

Step 1: Install ingestr

Install ingestr in seconds using pip. Choose the method that works best for you:

Recommended: Using uv (fastest)

# Install uv first if you haven't already
pip install uv

# Run ingestr using uvx
uvx ingestr

Alternative: Global installation

# Install globally using uv
uv pip install --system ingestr

# Or using standard pip
pip install ingestr

Verify installation: Run ingestr --version to confirm it's installed correctly.

Step 2: Your First Migration

Let's copy a table from HTTP to Databricks. This example shows a complete, working command you can adapt to your needs.

Set up your connections

HTTP connection format:

http://?url=<api_url>&headers=<headers>&auth_type=<auth_type>

Parameters:

  • • url: The HTTP(S) endpoint URL
  • • headers: Custom headers as JSON (optional)
  • • auth_type: Authentication type (bearer, basic, etc.)

Databricks connection format:

databricks://token@host:port/http_path

Parameters:

  • • token: Personal access token (use as username)
  • • host: Workspace URL
  • • port: Port number (usually 443)
  • • http_path: SQL endpoint HTTP path

Run your first copy

Copy the entire users table from HTTP to Databricks:

ingestr ingest \
    --source-uri 'http://?url=https://api.example.com/data' \
    --source-table 'public.users' \
    --dest-uri 'databricks://[email protected]:443/sql/1.0/endpoints/abc123' \
    --dest-table 'raw.users'

What this does:

  • • Connects to your HTTP database
  • • Reads all data from the specified table
  • • Creates the table in Databricks if needed
  • • Copies all rows to the destination

Command breakdown:

  • --source-uri Your source database
  • --source-table Table to copy from
  • --dest-uri Your destination
  • --dest-table Where to write data

Step 3: Verify your data

After the migration completes, verify your data was copied correctly:

Check row count in Databricks:

-- Run this in Databricks
SELECT COUNT(*) as row_count 
FROM raw.users;

-- Check a sample of the data
SELECT * 
FROM raw.users 
LIMIT 10;

Advanced Patterns

Once you've mastered the basics, use these patterns for production workloads.

Only copy new or updated records since the last sync. Perfect for daily updates. 

ingestr ingest \
    --source-uri 'http://?url=https://api.example.com/data' \
    --source-table 'public.orders' \
    --dest-uri 'databricks://[email protected]:443/sql/1.0/endpoints/abc123' \
    --dest-table 'raw.orders' \
    --incremental-strategy merge \
    --incremental-key updated_at \
    --primary-key order_id

How it works: The merge strategy updates existing rows and inserts new ones based on the primary key. Only rows where updated_at has changed will be processed.

Common Use Cases

Ready-to-use commands for typical HTTP to Databricks scenarios.

Daily Customer Data Sync

Keep your analytics warehouse updated with the latest customer information every night. 

# Add this to your cron job or scheduler
ingestr ingest \
    --source-uri 'http://?url=https://api.example.com/data' \
    --source-table 'public.customers' \
    --dest-uri 'databricks://[email protected]:443/sql/1.0/endpoints/abc123' \
    --dest-table 'analytics.customers' \
    --incremental-strategy merge \
    --incremental-key updated_at \
    --primary-key customer_id

Historical Data Migration

One-time migration of all historical records to your data warehouse. 

# One-time full table copy
ingestr ingest \
    --source-uri 'http://?url=https://api.example.com/data' \
    --source-table 'public.transactions' \
    --dest-uri 'databricks://[email protected]:443/sql/1.0/endpoints/abc123' \
    --dest-table 'warehouse.transactions_historical'

Development Environment Sync

Copy production data to your development Databricks instance (with sensitive data excluded). 

# Copy sample data to development
ingestr ingest \
    --source-uri 'http://?url=https://api.example.com/data' \
    --source-table 'public.products' \
    --dest-uri 'databricks://[email protected]:443/sql/1.0/endpoints/abc123' \
    --dest-table 'dev.products' \
    --limit 1000  # Only copy 1000 rows for testing

Choosing a HTTP to Databricks data integration tool

If you're comparing the best data integration tools to move or migrate HTTP data into Databricks, ask a practical question: can the first copy run locally and stay reviewable when it becomes a production pipeline?

What is the best data integration tool to move data from HTTP to Databricks?

For most teams, ingestr is the best starting point for moving or migrating HTTP data into Databricks. It runs as an open-source CLI from your terminal, CI, or scheduler. Use Bruin Cloud when the same pipeline needs orchestration, lineage, monitoring, checks, and governance.

Can this run as an incremental pipeline?

Yes. Use snapshot-plus-incremental or time-based extraction when the source supports it. That keeps the first load simple while making later runs smaller and easier to monitor.

When should I use Bruin Cloud with ingestr?

Use Bruin Cloud when the HTTP to Databricks pipeline needs schedules, alerts, data quality checks, audit trails, or catalog and lineage visibility for the rest of the team.

Troubleshooting Guide

Solutions to common issues when migrating from HTTP to Databricks.

Connection refused or timeout errors

Check your connection details:

  • Ensure SQL endpoint is running
  • Verify personal access token is valid
  • Check workspace URL is correct
  • Confirm HTTP path matches your endpoint
Authentication failures

Common authentication issues:

  • Ensure SQL endpoint is running
  • Verify personal access token is valid
  • Check workspace URL is correct
  • Confirm HTTP path matches your endpoint
Schema or data type mismatches

Handling data type differences:

  • ingestr automatically handles most type conversions
  • Databricks: Delta tables support schema evolution
  • Databricks: Complex types (arrays, maps, structs) supported
  • Databricks: Photon acceleration for certain operations
  • Databricks: Partitioning affects query performance
Performance issues with large tables

Optimize large data transfers:

  • Use incremental loading to process data in chunks
  • Run migrations during off-peak hours
  • Split very large tables by date ranges using interval parameters

Ready to scale your data pipeline?

You've learned how to migrate data from HTTP to Databricks with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.

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