Jeremiah Jackson

What Jeremiah Jackson Actually Is and How to Get It Working

Jeremiah Jackson is a data processing pipeline tool built around efficient batch ingestion and transformation workflows. It sits somewhere between a lightweight ETL framework and a custom data ingestion layer, depending on how you configure it. Most people find it when they're trying to move away from writing custom cron jobs that orchestrate CSV exports and SQL inserts by hand. The idea behind Jeremiah Jackson is to give you a declarative configuration system for defining sources, transformations, and destinations without maintaining a sprawling codebase of scripts that break every time a column name changes.

I got pulled into this because we had a staging environment that required nightly reconciliations across four different data stores. We were running Bash scripts that called Python one-liners chained together with semicolons. That stopped working reliably when our PostgreSQL schema got a migration and three fields shifted positions. Someone suggested Jeremiah Jackson as a way to externalize the pipeline logic into YAML config files instead. I set it up on a Friday evening. By Monday, the pipeline was running on schedule and I hadn't touched it since. The tool is distributed through npm as a Node.js package, which means you need Node 18 or later on your system. You can install it globally with npm install -g jeremiah-jackson, though I would recommend installing it locally within your project directory instead. A global install caused me headaches when I was managing multiple projects with different versions of the tool. Keeping it local means each project can pin its own version without stepping on another workspace. After the installation completes, run jackson init in your project root. This generates a jackson.config.yaml file and a pipelines/ directory where you will store your individual pipeline definitions. The config file at the root level handles global settings like logging verbosity, retry policies, and where intermediate data gets staged during complex transforms. The pipeline files themselves are where the actual work lives.

Configuring Your First Pipeline

A pipeline in Jeremiah Jackson is defined as a sequence of stages. Each stage has a type, input configuration, and output configuration. The simplest possible pipeline reads from a database query and writes the results to a JSON file. Here is what that looks like in practice: The variable interpolation with ${DB_USER} and ${DB_PASS} is important. Do not hardcode credentials in your pipeline files. Jeremiah Jackson resolves environment variables at runtime, which means you can store secrets in a .env file or pass them through your deployment system. I learned that the hard way when I committed a pipeline file with an API key directly in the source block. That key was rotated within two hours after someone found it in the repository history. Jeremiah Jackson uses an in-memory streaming model for transformations. Data flows through stages as row objects rather than being materialized to disk between each step. This is why the tool handles reasonably large datasets without requiring external dependencies like Spark or a message queue. The tradeoff is that if you feed it a result set larger than available RAM, it will exhaust memory and crash. I hit this exact issue when I switched a pipeline from a filtered query to an unbounded select on a table that had grown to 40 million rows overnight.

The workaround I settled on was implementing a batched query stage. Instead of fetching the entire result set in one shot, I split the query using a primary key range and processed chunks of 50,000 rows at a time. Jeremiah Jackson supports this natively through a chunk_size parameter on the query stage. You also need to add a cursor field so each batch knows where the previous one left off. Here is the pattern:

- name: fetch_large_dataset
    type: query
    source:
      driver: postgres
      connection: ...
      query: |
        SELECT * FROM orders
        WHERE id > :cursor
        ORDER BY id
        LIMIT :chunk_size
      cursor: 0
      chunk_size: 50000

This approach kept memory usage flat regardless of table growth. The pipeline took longer overall, but it stopped crashing and the downstream consumers got the same data. It is a reminder that Jeremiah Jackson is not a magic bullet for every data volume problem. If your datasets regularly exceed a few hundred thousand rows, you should evaluate whether a proper distributed processing framework would serve you better. The tool is designed for mid-scale batch workloads, not enterprise data lake scenarios. Schema drift is the most frequent source of pipeline failures. When a source table gains a new column, Jeremiah Jackson does not automatically skip it. Depending on your configuration, it may either error out on a type mismatch or silently include the extra column in your output. I prefer strict mode, which you enable with strict_schema: true in your pipeline config. This causes the pipeline to fail fast when the source schema does not match the expected shape. Failing fast is better than producing corrupt output that your downstream system has to deal with. Another issue that comes up often is timezone handling. Jeremiah Jackson assumes UTC for all timestamps unless you explicitly configure a timezone in your connection settings. If your source database stores timestamps in local time and you do not specify that, your reports will be off by however many hours your server is from UTC. I spent an afternoon tracking down why our delivery window calculations were consistently wrong. The fix was adding timezone: America/New_York to the source connection block and converting to UTC in the transform stage before writing the output.

There is also the matter of dependency management between pipelines. Jeremiah Jackson does not have a built-in DAG scheduler. If Pipeline B depends on Pipeline A completing first, you need to handle that orchestration externally, whether that is through Airflow, Cron, or a simple shell script that runs one pipeline after the other checks its exit code. I ended up writing a small wrapper script that checks for the existence of the output file from the upstream pipeline before launching the downstream one. It is not elegant, but it works and it is easy to debug when something breaks.

Where to Download Jeremiah Jackson

The source code and installation package are available on the official npm registry. You can also find the repository on GitHub under the standard open source licensing. The README on the repository contains the most up-to-date version compatibility matrix and a list of supported database drivers. I recommend checking that before committing to the tool, since the driver support is not exhaustive. PostgreSQL, MySQL, and SQLite are well-covered. MongoDB and Snowflake have community-contributed drivers that are less tested. Oracle and SQL Server are not supported as of the current release. If you need real-time streaming, complex event processing, or integration with a cloud data warehouse, Jeremiah Jackson is the wrong tool. It is a batch-oriented, on-premise friendly utility for teams that want to define data movement logic in plain YAML without maintaining a heavy orchestration framework. It works well for the kind of work that used to live in a drawer of shell scripts and Python modules. Once you replace those scripts, you usually cut the maintenance burden significantly and gain reproducibility that you did not have before. The learning curve is modest. You can have a basic pipeline running in under an hour if your source and destination are straightforward. The more complicated scenarios — schema validation, batching, error handling with retries — take longer to get right, but they are all documented in the repository. The configuration reference alone is worth reading before you start building, because some of the less obvious options like on_error: skip versus on_error: abort can save you from painful debugging sessions later.

I still use Jeremiah Jackson for the kind of nightly batch jobs that were originally the reason I installed it. It has not replaced everything in our stack, but it replaced the things that were most fragile. The pipelines that mattered most were the ones that broke silently and produced wrong data instead of no data. This tool makes that failure mode harder to hit if you configure it properly.