Self-Hosted Data Processing Engines — Databend vs Apache DataFusion vs Apache Ballista

In the modern data stack, choosing the right data processing engine is critical for running analytics, ETL pipelines, and ad-hoc queries at scale. Three open-source projects have emerged as leading choices for self-hosted data processing: Databend, Apache DataFusion, and Apache Ballista. Each takes a fundamentally different approach — from a complete cloud data warehouse to an embeddable query engine to a distributed compute cluster.

This guide compares all three, examining their architecture, performance, deployment options, and ideal use cases so you can pick the right engine for your self-hosted data infrastructure.

Project Overview

Architecture Comparison

Databend — The Complete Data Warehouse

Databend is a full-featured, cloud-native data warehouse built from the ground up in Rust. It uses Apache Arrow for in-memory processing and Parquet for storage, with a decoupled compute-storage architecture. The system includes:

• Query layer: MySQL-compatible SQL interface with a PostgreSQL wire protocol option
• Storage layer: Object storage (S3, Azure Blob, GCS) with caching
• Compute layer: Elastic scaling with stateless query nodes
• Metadata service: Built-in metastore with ACID transactions

Databend’s architecture mirrors Snowflake’s decoupled model, making it the most “complete” solution — you get a warehouse, not just a query engine.

Apache DataFusion — The Embeddable Query Engine

Apache DataFusion is not a standalone server but a query engine library written in Rust. It provides:

• Logical planning: SQL parsing, semantic analysis, query optimization
• Physical planning: Cost-based optimization with extensible rule system
• Execution: Vectorized execution using Apache Arrow arrays
• Extensibility: Custom table providers, user-defined functions (UDFs), and optimizer rules

DataFusion is designed to be embedded into other applications. Projects like Databend, Ballista, and DataFusion Comet are all built on top of it. Think of it as the “PostgreSQL” of Rust data processing — a building block, not a finished product.

Apache Ballista — The Distributed Query Engine

Apache Ballista (formerly DataFusion Ballista) adds distributed execution to DataFusion. It uses:

• Scheduler: Coordinates query execution across worker nodes
• Executor: Stateless workers that process data partitions
• Shuffle: Built-in shuffle service for distributed joins and aggregations
• Kubernetes-native: Designed for deployment on K8s with horizontal scaling

Ballista is essentially “DataFusion but distributed” — it takes the same query engine and adds horizontal scaling across a cluster.

Deployment & Setup

Databend Docker Compose

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version: '3.8'
services:
  databend-query:
    image: datafuselabs/databend:latest
    ports:
      - "8000:8000"  # HTTP API
      - "3307:3307"  # MySQL protocol
    environment:
      - QUERY_DEFAULT_USER=default
      - QUERY_DEFAULT_PASSWORD=default
    volumes:
      - databend_data:/var/lib/databend
    deploy:
      resources:
        limits:
          memory: 4G
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/v1/health"]
      interval: 10s
      timeout: 5s
      retries: 3

  databend-meta:
    image: datafuselabs/databend:latest
    command: ["databend-meta"]
    ports:
      - "9191:9191"  # gRPC
      - "28002:28002"  # HTTP admin
    volumes:
      - databend_meta:/var/lib/databend-meta
    deploy:
      resources:
        limits:
          memory: 2G

volumes:
  databend_data:
  databend_meta:

Databend runs as a two-service setup: the meta store (for metadata) and the query engine. For production, you run multiple query nodes behind a load balancer.

Apache DataFusion — Embedded Usage

DataFusion is used as a Rust library, not deployed as a service:

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use datafusion::prelude::*;

#[tokio::main]
async fn main() -> datafusion::error::Result<()> {
    // Create a SessionContext
    let ctx = SessionContext::new();

    // Register a CSV file as a table
    ctx.register_csv("trips", "data/trips.csv", CsvReadOptions::new()).await?;

    // Run a SQL query
    let df = ctx.sql("SELECT passenger_count, COUNT(*), AVG(total_amount)                       FROM trips GROUP BY passenger_count").await?;

    // Collect and print results
    let results = df.collect().await?;
    datafusion::arrow::util::pretty::print_batches(&results)?;

    Ok(())
}

This is fundamentally different from Databend and Ballista — you embed DataFusion into your own application code.

Apache Ballista — Kubernetes Deployment

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apiVersion: apps/v1
kind: Deployment
metadata:
  name: ballista-scheduler
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ballista-scheduler
  template:
    metadata:
      labels:
        app: ballista-scheduler
    spec:
      containers:
        - name: scheduler
          image: apache/ballista-scheduler:latest
          ports:
            - containerPort: 50050  # gRPC
            - containerPort: 8080   # HTTP UI
          env:
            - name: BALLISTA_SCHEDULER_GRPC_PORT
              value: "50050"
          resources:
            requests:
              memory: "2Gi"
              cpu: "1"
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ballista-executor
spec:
  replicas: 3
  selector:
    matchLabels:
        app: ballista-executor
  template:
    metadata:
      labels:
        app: ballista-executor
    spec:
      containers:
        - name: executor
          image: apache/ballista-executor:latest
          env:
            - name: BALLISTA_EXECUTOR_GRPC_PORT
              value: "50051"
            - name: BALLISTA_EXECUTOR_SCHEDULER_HOST
              value: "ballista-scheduler"
            - name: BALLISTA_EXECUTOR_CONCURRENT_TASKS
              value: "4"
          resources:
            requests:
              memory: "4Gi"
              cpu: "2"

Ballista requires a scheduler and multiple executor nodes. The scheduler distributes query tasks across executors, which process data in parallel.

Performance Characteristics

When to Choose Each

Choose Databend when:

• You need a complete, production-ready data warehouse
• MySQL compatibility is required for existing BI tools
• You want elastic compute with decoupled storage
• Your team wants a Snowflake-like experience, self-hosted
• You need ACID transactions and time travel

Choose Apache DataFusion when:

• You are building a custom data application in Rust
• You need an embeddable SQL engine (not a standalone server)
• You want to customize the optimizer with domain-specific rules
• Your application already handles distributed execution
• You need low-latency queries within an existing service

Choose Apache Ballista when:

• You need distributed query execution across a cluster
• You want Kubernetes-native scaling for data processing
• Your workloads exceed single-node memory limits
• You need horizontal scaling for joins and aggregations
• You want Apache Foundation governance and community

Why Self-Host Your Data Processing Engine?

Running a data processing engine in-house gives you complete control over your analytics pipeline. Here is why organizations choose self-hosted solutions over managed cloud offerings:

Data Sovereignty and Compliance: Sensitive data never leaves your infrastructure. This is critical for healthcare (HIPAA), finance (PCI-DSS), and government workloads where data residency requirements prohibit cloud storage. With Databend, your data stays in your S3-compatible storage on-premises.

Cost Predictability: Cloud data warehouses charge per query, per terabyte scanned, and per compute-hour. At scale, these costs become unpredictable and often exceed self-hosted infrastructure costs by 3-5x. Running Databend on your own hardware eliminates per-query charges entirely.

Performance Tuning: Self-hosted engines allow deep optimization — from storage layout (Parquet sorting, Z-ordering) to query plan customization. DataFusion’s extensible optimizer lets you add domain-specific rules that cloud engines cannot support.

No Vendor Lock-In: All three projects are open-source (Apache 2.0). Your queries, schemas, and infrastructure are portable. You are not locked into a proprietary format or API.

Integration with Existing Stack: Self-hosted engines integrate directly with your existing data lake, object storage, and monitoring infrastructure. Databend speaks MySQL wire protocol, DataFusion embeds directly into Rust applications, and Ballista runs natively on Kubernetes.

For related reading, see our self-hosted OLAP database comparison and our streaming SQL engines guide for complementary data stack components.

FAQ

Is Databend production-ready for self-hosted deployments?

Yes. Databend is used in production by multiple organizations for analytics workloads. It supports high availability with multiple query nodes, persistent metadata storage, and integration with S3-compatible object storage. The project has been actively developed since 2021 and reached 9,000+ GitHub stars.

Can I use Apache DataFusion without writing Rust code?

DataFusion is primarily a Rust library, so you need to write Rust code to use it directly. However, it provides Python bindings (datafusion-python) and a CLI tool (datafusion-cli) that let you run SQL queries without writing application code. For production deployments requiring a SQL endpoint, consider Databend or Ballista instead.

How does Ballista compare to Apache Spark for distributed processing?

Ballista is designed specifically for SQL query execution, while Spark is a general-purpose data processing framework. Ballista typically uses less memory and starts faster than Spark for SQL workloads because it does not carry the overhead of Spark’s RDD and Catalyst abstractions. However, Spark has a much larger ecosystem and more mature tooling. Ballista is a good choice if you want a lightweight, Rust-native alternative to Spark SQL.

Does Databend support data ingestion from streaming sources?

Databend supports batch ingestion from files (Parquet, CSV, JSON, AVRO) and from databases via its COPY INTO command. It also supports streaming ingestion through its Kafka integration. For real-time streaming workloads, consider pairing Databend with a stream processor like RisingWave or Materialize.

What storage backends does DataFusion support?

DataFusion includes built-in table providers for Parquet, CSV, JSON, and AVRO files. It also supports external catalogs and can read from object storage (S3, GCS, Azure Blob) via the object_store crate. Custom table providers can be implemented to support any data source.

How many Ballista executor nodes do I need?

The number of executor nodes depends on your data size and query complexity. As a starting point: 3 executors for datasets up to 100 GB, 5-10 executors for 100 GB to 1 TB, and 10+ executors for larger datasets. Ballista scales horizontally, so you can add executors without downtime. Each executor should have at least 4 GB of memory and 2 CPU cores.