What You Will Need (Prerequisites)

This section gives a brief overview of what a new user to Dataworkz will need to bring to create Data Applications.

To get started with Dataworkz, you need a few essential components and configurations. The sections below describe each requirement.

1. Storage Setup

Dataworkz provides flexible options for storing and managing data. Whether you are working with documents for RAG, structured enterprise databases, or graph data, the platform includes built-in integrations for each use case.

Default Storage

When you first sign up, Dataworkz provides a default workspace with two built-in storage options:

• S3-compatible object storage: Upload files such as PDFs, Word documents, or PowerPoint decks. This storage is commonly used for RAG ingestion pipelines. Direct uploads from your local machine are limited to 1 MB per file. For larger files, connect your own external storage.

• MongoDB (Vector storage): Embeddings, text chunks, and metadata are stored in MongoDB by default. This serves as your vector database and enables semantic search across ingested datasets.

These defaults support quick experimentation. Most production projects connect external, enterprise-grade storage.

Supported Storage Options

From the Databases panel in Dataworkz, you can configure the following storage systems:

• Vector Databases: Pinecone, OpenSearch, Weaviate (coming soon)

• NoSQL Databases: MongoDB, Pg-vector, Couchbase, Aerospike, Datastax

• Relational Databases: Oracle, Microsoft SQL Server, MySQL, MariaDB, DB2, Postgres, Cockroach

• Graph Database: Neo4J

Data Privacy and Best Practices

The default workspace is convenient for getting started. Before uploading sensitive or proprietary data, set up your own dedicated vector and object storage (such as S3, GCS, or your enterprise database of choice).

Once external storage is configured, you can:

• Migrate existing datasets out of the default workspace.

• Adjust workspace and collection settings for stricter access control.

• Ensure compliance with internal data governance policies.