Building an Open Source Data Stack: From Ingestion to Analytics
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Key Insights
- The Modern Open Source Data Stack The 2026 open source data stack is modular, composable, and cloud-agnostic.
- Teams assemble best-in-class tools for each layer rather than buying monolithi.
The Modern Open Source Data Stack
The 2026 open source data stack is modular, composable, and cloud-agnostic. Teams assemble best-in-class tools for each layer rather than buying monolithic platforms. This lesson walks through a complete production stack.
Layer 1: Ingestion
Airbyte (open source, MIT) provides 600+ connectors for APIs, databases, and file stores. It handles schema detection, incremental syncs, and normalization. Deploy via Docker Compose or Kubernetes.
Alternatives: Meltano (Singer taps), Apache NiFi (visual flow), Debezium (CDC from databases).
Layer 2: Storage
The lakehouse paradigm dominates: object storage (S3/MinIO) + table format (Apache Iceberg/Delta Lake) + query engine (Trino/Spark). Iceberg provides ACID transactions, time travel, and schema evolution on object storage.
Layer 3: Transformation
dbt is the standard for SQL transformations. Define models as SELECT statements; dbt handles dependency resolution, incremental materialization, testing, and documentation generation. Models are organized into layers (sources → staging → intermediate → marts).
Layer 4: Orchestration
Dagster has become the preferred orchestrator for data teams who value asset-centric design. Unlike Airflow's DAG-of-tasks approach, Dagster treats datasets as assets with explicit lineage. Prefect remains strong for teams wanting Python-native flow control.
Layer 5: Quality & Observability
Great Expectations + Soda for quality. OpenMetadata for catalog + lineage. Custom dashboards via Prometheus + Grafana for pipeline metrics. dbt-external-tables for freshness monitoring.
Layer 6: Analytics & Serving
Evidence (markdown-driven BI), Metabase (self-service), or Apache Superset (enterprise dashboards). For ML serving: MLflow for model registry, BentoML for serving, Feast for feature stores.
Putting It Together
This entire stack runs on a single `docker-compose.yml` for development and scales to production on Kubernetes. The total infrastructure cost for a mid-size data team: roughly $500-2000/month in cloud compute, zero licensing fees.
AcaciaFund itself follows this philosophy: Python-native, open source tools only, static output, zero vendor lock-in.
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Feynman Concept Cards
Master each building block: read the ELI5, explore the analogy, work the example, find your gaps, teach it back, build it.
Data Lake is a concept in foundations. In simple terms, Data Lake covers foundational knowledge in Data Engineering. This data engineering concept addresses key topics in the foundational knowledge in data engineering domain. Also known as: data lakehouse.
Analogy
Example
Find Gaps
Explain Data Lake as if teaching a colleague who is new to foundations. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Data Lake in a real-world foundations scenario. Walk through your design decisions.
Show solution
A diagram for Data Lake should include: 1. The core components of data lake 2. How they interact 3. Expected outcomes or outputs
Apache Iceberg is a concept in advanced techniques. In simple terms, Apache Iceberg covers advanced techniques in Data Engineering. This data engineering concept addresses key topics in the advanced techniques in data engineering domain. Also known as: Iceberg, table f
Analogy
Example
Find Gaps
Explain Apache Iceberg as if teaching a colleague who is new to advanced techniques. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Apache Iceberg in a real-world advanced techniques scenario. Walk through your design decisions.
Show solution
A diagram for Apache Iceberg should include: 1. The core components of apache iceberg 2. How they interact 3. Expected outcomes or outputs
Change Data Capture is a concept in advanced techniques. In simple terms, Change Data Capture covers advanced techniques in Data Engineering. This data engineering concept addresses key topics in the advanced techniques in data engineering domain. Also known as: CDC, change
Analogy
Example
Find Gaps
Explain Change Data Capture as if teaching a colleague who is new to advanced techniques. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Change Data Capture in a real-world advanced techniques scenario. Walk through your design decisions.
Show solution
A diagram for Change Data Capture should include: 1. The core components of cdc 2. How they interact 3. Expected outcomes or outputs
Change Data Capture Patterns is a concept in streaming. In simple terms, Change Data Capture Patterns covers streaming data architecture in Data Engineering. This data engineering concept addresses key topics in the streaming data architecture in data engineering domain. A
Analogy
Example
Find Gaps
Explain Change Data Capture Patterns as if teaching a colleague who is new to streaming. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Change Data Capture Patterns in a real-world streaming scenario. Walk through your design decisions.
Show solution
A diagram for Change Data Capture Patterns should include: 1. The core components of change data capture 2. How they interact 3. Expected outcomes or outputs
Infrastructure is a concept in specialized. In simple terms, A concept related to infrastructure
Analogy
Example
Find Gaps
Explain Infrastructure as if teaching a colleague who is new to specialized. Cover: what it is, how it works, and why it matters.
Create
Create a diagram that demonstrates Infrastructure in a real-world specialized scenario. Walk through your design decisions.
Show solution
A diagram for Infrastructure should include: 1. The core components of infrastructure 2. How they interact 3. Expected outcomes or outputs
Feynman Synthesis — Prove You Understand
1. The One-Pager
Explain this lesson's core idea to a smart 15-year-old. No jargon allowed.
2. The Gap Map
List 3 things you are still unsure about. Be specific.
Knowledge Check
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Flashcards
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