ETL CONSOLE

ETL & Data Integration Platform

Build, schedule, monitor, and operate database and file pipelines with controlled staging, SQL transformations, data quality checks, and private deployment options.

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Source

Staging

Transform

Target

Rows2.8M

Last run12 min

StatusSuccess

Source columnTarget columnMode

order_idorder_idKey

customer_refcustomer_codeMapped

updated_atsource_updated_atIncremental

amount_totalnet_amountValidated

Key ETL Console capabilities

Define controlled pipelines before data moves

Inspect source objects, map fields into relational targets or file exports, choose reload, full scan, snapshot, hybrid, or incremental strategies, and apply guardrails before scheduled jobs begin.

Reusable connections, mappings, staging settings, and SQL transformations
Safe reloads, rejected-row handling, and source-to-target consistency checks
Scheduling, dependency-aware runs, alerts, run logs, and status history
Data quality history, schema drift tracking, and operational monitoring

Who it is for

Built for teams operating real data pipelines

ETL Console is intended for teams that need repeatable database and file integration, controlled transformations, scheduling, monitoring, and operational visibility without maintaining every pipeline as custom code.

Data Engineers

Analytics Engineers

BI & Reporting Teams

Database Specialists

Data & BI Consultants

Connectors and sources

Move operational data from supported databases, files, storage, and APIs

ETL Console covers relational databases, MongoDB, uploadable files, S3 object sources and targets, and REST API resources without forcing every pipeline into custom code.

PostgreSQL

MySQL

MariaDB

SQL Server

Oracle

MongoDB

REST API

AWS S3

CSV

Excel

JSON

Parquet

More to come

Use cases

Operate recurring data integration work with visibility

Use ETL Console for practical integration jobs where teams need repeatable runs, source-to-target control, and operational history.

Example scenario

Multi-source operations datamart

Stage Oracle tables, a public CSV URL, and Microsoft SQL Server data into common PostgreSQL tables using incremental load mode, so scheduled runs add new records without reloading every source from scratch.

Apply business-logic SQL transformations across those staged sources, then publish the curated output to two targets: a PostgreSQL datamart for BI tools and full-copy snapshots in an AWS S3 bucket.

Sources

Oracle Public CSV URL MS SQL Server

Staging and logic

Staging to PostgreSQL SQL business transformations

Targets

PostgreSQL datamart AWS S3 bucket storage

Incremental staging for adding new records
Scheduled transformation jobs with two downstream targets
Error monitoring, run history, alerts, and data quality checks

Database replication to reporting

Move operational tables into reporting-ready relational targets with scheduled full, incremental, or hybrid staging.

PostgreSQL / MySQL / SQL Server / Oracle -> staging -> reporting table

File and S3 ingestion

Load CSV, Excel, JSON, JSON Lines, and Parquet data from uploads or S3 objects into controlled staging tables.

S3 or uploaded file -> staging -> validation and review

REST API extraction

Pull REST API resources into staging so external API data can be joined with database reporting workflows.

REST API resource -> staging -> downstream SQL

Operational data quality monitoring

Track recurring runs with consistency checks, rejected-row review, data quality history, and schema drift visibility.

Scheduled staging -> quality checks -> alerts and history

SQL transformation pipelines

Run SQL transformations after staging and monitor transformation runs separately from extraction runs.

Staged data -> SQL transformation -> target table

Private network data movement

Use private deployment patterns when databases, TLS, credentials, or network access cannot be exposed to public workers.

Private database -> customer-controlled deployment -> target

KPIs Strip

Stagings

Runs started: 34
Success rate: 94.1%
Failed: 2
Rows added: 284,920

Transformations

Runs started: 12
Success rate: 91.7%
Warnings: 1
Data Quality Issues: 3

Active Stagings / Transformations

Active stagings8

Inactive stagings2

Active transformations4

Inactive transformations1

Latest Fails

StatusTypeLabelTime

Failed Staging Oracle orders incremental 2026-08-15 08:10

Warning Transform Datamart customer revenue 2026-08-15 08:35

Next Scheduled Runs

CSV FX rates09:15

SQL Server invoices09:30

S3 datamart snapshot10:00

Freshness and Rejected Rows

Oracle orders6m old

CSV FX ratesSLA 15m

Rejected rows17

Staging Run Stats

Mon

Tue

Wed

Thu

Performance Stats

Rows today128,740

Duration42.6 min

Throughput50.4 rows/sec

Private network deploymentReady

Custom TLS and credentialsEnabled

Data quality checksPassing

Freshness SLAReview

Restricted Beta

Request access for a real ETL workload

Restricted deployments are invite-based. Share the systems you need to connect, the data movement you plan to operate, and any private network or deployment requirements.

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