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You can build a Reddit analysis pipeline by using Reddit-approved access for collection, Airflow to schedule repeatable steps, DuckDB to transform and query the data, and Ollama for optional local-model analysis. Treat the design below as a starting architecture, not a tested deployment: your Reddit access terms, data volume, Airflow version, storage, and available hardware determine the final setup.
What each component does
| Component | Role in the pipeline | Key boundary |
|---|---|---|
| Reddit Data API or another approved access route | Collects only the communities, fields, and time range your use case needs. | Public visibility does not by itself grant API access or commercial-use rights. |
| Apache Airflow | Schedules and monitors collection, transformation, and analysis tasks. | For Airflow 3.0 and later, author DAGs through the airflow.sdk public interface; task code should not query Airflow’s metadata database directly. |
| DuckDB | Runs SQL transformations and analysis inside an Airflow task process. | A separate DuckDB cluster is not inherently required for this pattern. Remote storage and other backends still need their own credentials and configuration. |
| Ollama | Optionally applies local-model analysis, such as classification or summarization, to selected text. | The Ollama server and chosen model must be available where the request runs. Local inference is not permission to use Reddit content for model training. |
Check Reddit access and permitted use first
Reddit’s Developer Platform and Accessing Reddit Data help page, updated February 14, 2025, says commercial use of Reddit developer tools and services requires Reddit’s permission and a contract. Its examples include monetized services, subscriptions, advertising, paid data access, and publishing Reddit content on monetized websites or apps. Check the current terms and obtain any required approval before building a commercial service; the answer may depend on your use case and granted access.
The same guidance says Reddit content may not be used as input for model training without Reddit’s explicit consent. Sending content to a model for inference, such as a one-time classification or summary, is not the same activity as training a model. That distinction does not establish that a particular inference workflow is permitted, so verify your intended use under Reddit’s current terms.
Reddit says its APIs are rate-limited, but its general help guidance does not establish one universal quota. Follow the current documentation for the specific service and the terms attached to your approved access rather than hard-coding an assumed request limit.
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Plan the pipeline around small, explicit stages
- Collect: Use an access method and scope appropriate to your approved use. Keep the collector separate from analysis code so access changes do not require rewriting the DuckDB or Ollama stages.
- Stage: Save the permitted fields in a documented format, such as JSON Lines, and record when and how each batch was collected. Define a retention and deletion policy before accumulating data.
- Transform: Have an Airflow task load staged records into DuckDB and produce a stable, queryable representation. Make retries safe by identifying batches or records so a rerun does not silently duplicate data.
- Analyze: Use SQL for counts, grouping, filtering, and other structured questions. Send only the text needed for a language-model task to Ollama, and keep that step optional when a deterministic query is sufficient.
- Review and retain: Monitor failures, API throttling, storage growth, and model errors. Keep raw and derived data only as long as your permitted use and operational needs justify.
This separation makes it possible to change one stage without treating the whole system as a single opaque job. It also gives you a clear place to enforce collection scope, retention, and review before content reaches a model.
Use Airflow 3’s public DAG interface
Airflow’s documentation identifies airflow.sdk as the official public interface for DAG authors as of Airflow 3.0. The example below is deliberately limited to transforming an already-approved, locally staged JSON Lines file. It assumes each illustrative record has a post_id, subreddit, created_utc, and text field; adapt the schema to the fields you are permitted to collect. It does not implement Reddit authentication or collection.
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from pathlib import Path
import duckdb
from airflow.sdk import dag, task
INPUT_FILE = "/data/reddit/approved_batch.jsonl"
DUCKDB_FILE = "/data/warehouse/reddit.duckdb"
@dag(schedule="@daily", catchup=False)
def reddit_intelligence():
@task
def transform_staged_batch():
source = Path(INPUT_FILE)
if not source.exists():
raise FileNotFoundError(f"Expected staged input: {source}")
con = duckdb.connect(DUCKDB_FILE)
try:
con.execute("""
CREATE TABLE IF NOT EXISTS posts (
post_id VARCHAR PRIMARY KEY,
subreddit VARCHAR,
created_utc TIMESTAMP,
text VARCHAR
)
""")
# Illustrative schema and load pattern; validate and deduplicate
# records according to your actual source and batch identity.
con.execute("""
INSERT INTO posts
SELECT post_id, subreddit,
CAST(created_utc AS TIMESTAMP), text
FROM read_json_auto(?)
ON CONFLICT (post_id) DO NOTHING
""", [str(source)])
finally:
con.close()
@task
def summarize_by_community():
con = duckdb.connect(DUCKDB_FILE, read_only=True)
try:
return con.execute("""
SELECT subreddit, COUNT(*) AS post_count
FROM posts
GROUP BY subreddit
ORDER BY post_count DESC
""").fetchall()
finally:
con.close()
summarize_by_community() & transform_staged_batch()
reddit_intelligence()The final dependency expression in a real DAG should express the intended order: transformation must complete before its summary task reads the table. For example, assign the transform task to a variable and pass its result or dependency to the summary task using Airflow’s supported task-flow pattern. The snippet’s SQL and field names are illustrative, not a prescribed Reddit schema.
Install DuckDB and any Airflow provider you choose in the task execution environment, with versions compatible with your Airflow deployment. The documented DuckDB provider executes queries in the Airflow task process; it does not make remote-storage credentials appear automatically. If multiple workers need the database file, choose storage and task placement deliberately so a task can read the same durable data after retries or worker changes. Do not solve orchestration problems by reading or writing Airflow’s metadata database from a task; use supported interfaces such as the Stable REST API, Python client, or task context methods when Airflow metadata access is needed.
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Connect Ollama only where the task runs
Ollama documents a local API base at http://localhost:11434/api and an OpenAI-compatible base at http://localhost:11434/v1. A local request to a model downloaded to that Ollama server can omit authorization. The model must actually be available to that server, and the API route and request format should match the installed Ollama release or the provider version you use.
In a containerized Airflow deployment, localhost refers to the Airflow task container itself, not automatically to a separate Ollama container or host. Configure a reachable Ollama host for the environment running the task, and avoid exposing an unauthenticated local service beyond the intended network boundary. Airflow’s provider documentation describes a self-hosted integration pattern with a model identifier such as ollama:<model>; confirm the supported connection settings for your installed provider version.
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Keep model work narrow. For example, a task might classify a permitted subset of post text into a small set of labels, then write the output to a separate derived table with the model identifier and processing date. Do not send every field just because it is available, and do not treat generated classifications or summaries as verified facts about authors or communities.
Design for throttling, retries, and recovery
- Bound collection: Poll only the approved scope and avoid unnecessary repeated requests. Add backoff for transient failures and respect the limits that apply to your access.
- Make retries safe: Track a batch identifier or stable record key, and design writes so retrying a task does not create unintended duplicates.
- Separate raw and derived data: Keep a clear boundary between collected records, transformed tables, and model-produced outputs so you can trace or remove derived results when needed.
- Monitor each stage: Record task outcomes, input batch dates, record counts, API errors or throttling, DuckDB failures, and model request failures without logging secrets or unnecessary post content.
- Plan storage deliberately: A database file on ephemeral worker storage may not be available to a later task or after a worker replacement. Use durable storage or deliberate task placement, and configure backend credentials separately where applicable.
Choose deployment based on the workload, not the title
The title alone does not establish a suitable machine, model, or deployment topology. Decide based on the amount of data, number of concurrent tasks, acceptable model latency, context needs, privacy boundary, and hardware already available. A local model can keep inference within the environment hosting Ollama, but that fact alone does not establish how Reddit content may be collected, retained, or used.
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