Browser-based pipeline development
Develop modular pipeline blocks in a visual, notebook-style workspace while keeping transformation logic visible as code.
Managed Mage OSS hosting
Build and schedule data pipelines in a browser
Run Mage OSS on Seemi Cloud for Python, SQL, and notebook-style data workflows. Build transformations, schedule recurring jobs, inspect results, and connect pipelines to your databases and object storage from a developer-friendly web workspace.
SEEMI CLOUD
Reliable infrastructure with operational safeguards built in
Secure by design Backup ready Regional choice API and agent ready
Python + SQLCode-first pipeline development
Browser IDENotebook-style development workspace
ScheduledRecurring and manually triggered jobs
Managed HostingMage OSS under your Seemi deployment
Capabilities
Designed for practical cloud operations
Scheduled Python and SQL workloads
Run recurring extraction, transformation, loading, synchronization, reporting, and general Python jobs on defined schedules.
Step-by-step execution and debugging
Run individual blocks, inspect logs and outputs, and iterate on pipeline logic without treating every change as a black-box deployment.
Protected with Seemi Forward Auth
Place the Mage web workspace behind Seemi Cloud Forward Auth so browser access is protected before requests reach the application.
Persistent project workspace
Keep pipeline code and eligible application state on persistent storage, with backup capacity configurable for the deployment.
Human engineering support
Engage Seemi engineers for sizing, source connectivity, migration planning, pipeline design, and integration troubleshooting.
Use cases
A focused alternative for code-first data workloads
Choose a supported service from the catalog or bring a workload that fits your architecture.
Scheduled ETL and ELT pipelines
Recurring Python data-processing jobs
Database-to-database transformations
API, file, and object-storage ingestion
SQL transformations and dbt-oriented workflows
Teams seeking a simpler, light weight alternative to Databricks workloads
FROM SOURCE TO SERVING LAYER
Build workflows around the data services you already use
Mage can coordinate extraction, transformation, and loading while Seemi Cloud supplies compatible data and storage services around the pipeline workspace.
Connect
Read from APIs, files, PostgreSQL, MySQL, object storage, or other supported sources.
Transform
Use modular Python or SQL blocks to clean, enrich, validate, and reshape data.
Schedule
Run pipelines manually or on recurring schedules with visible execution history and logs.
Deliver
Load curated data into PostgreSQL, MySQL, StarRocks, or S3-compatible storage.
SEEMI DATA PLATFORM FIT
A strong companion for Seemi database and analytics SKUs
PostgreSQL
Operational data, pipeline metadata, staging tables, and transformed application datasets.
Explore PostgreSQLMySQLExtract from or load into transactional MySQL workloads using controlled pipeline jobs.
Explore MySQLStarRocksPrepare and load analytical datasets for fast SQL reporting and high-concurrency analytics.
Explore StarRocksS3-compatible storageUse object storage for files, extracts, data-lake inputs, pipeline outputs, and backup workflows.
Explore object storagePOSITIONING
A lighter, highly economical alternative for Databricks
Mage can replace heavier platforms for many code-first ETL, ELT, scheduling, transformation, and recurring Python workloads. Mage allows you to run automated data-engineering and transformation without relying on expensive tools like Databricks billed on hourly basis.
Seemi Cloud Engineering can help enterprises design future fit data architecture, select SKUs, size the deployment and support with custom data engineering solutions. Seemi's data SKUs(Mage, MySQL, Postgres, Starrocks) have helped enterprises build their digital transformation program on robust data foundation.
Ready to move forward?