Ecosystem Tool Connections

Singdata Lakehouse supports multiple integration methods including JDBC drivers, Python/Java SDKs, and Spark/Flink Connectors. All major SQL clients, BI tools, and ETL platforms are supported. Choose the approach that fits your use case.

I want to connect with a SQL client

Recommended: DBeaver or DataGrip — connect via the JDBC driver with support for SQL editing, schema browsing, and data export.

Tool	Description	Reference
DBeaver	Free and open-source; the community edition is sufficient for everyday queries and data exploration	DBeaver Connection Guide
DataGrip	From JetBrains; strong code completion and SQL analysis	DataGrip Connection Guide
SQL Workbench/J	Lightweight; suitable when you only need basic SQL execution	SQL Workbench/J Connection Guide

All of the above connect via the JDBC driver. Connection string format:

jdbc:clickzetta://<instance_name>.<region_id>.api.singdata.com/<workspace_name>?username=<user>&password=<pwd>&virtualCluster=default

See JDBC Driver for details.

I want to use a BI tool for data visualization

Tool	Description	Reference
FineBI	A leading domestic BI platform; connects via JDBC and is well-suited for internal enterprise reporting	FineBI Connection Guide
Tableau	Connects via JDBC; ideal for complex visualizations and exploratory analysis	Tableau Connection Guide
Metabase	Open-source and easy to deploy; suitable for self-service analytics in small to mid-sized teams	Metabase Connection Guide
Apache Superset	Open-source; supports SQLAlchemy connections; suitable for teams with operational capacity	Superset Connection Guide
Rath	Open-source intelligent analytics tool with automatic insight generation	Rath Connection Guide
Streamlit	Python data application framework; lets data science teams build apps quickly	Streamlit Connection Guide
Zeppelin	Notebook-style interface; suitable for data exploration and reporting	Zeppelin Connection Guide

Most BI tools connect via the JDBC driver. If a tool supports SQLAlchemy (e.g., Superset), you can also use the SQLAlchemy interface provided by the Python SDK.

I want to use an ETL tool for data integration

Tool	Description	Reference
DataX	Open-sourced by Alibaba; suitable for offline batch data synchronization with simple configuration	DataX Integration Guide
dbt	Data transformation tool; ideal for SQL modeling and data transformation inside Singdata Lakehouse	dbt Integration Guide
Airbyte	Open-source ELT platform with a rich connector library; suitable for aggregating data from multiple sources	Airbyte Integration Guide

Choosing the right tool:

Syncing data from a single source → DataX
Data modeling and transformation inside Singdata Lakehouse → dbt
Connecting to multiple SaaS data sources (Salesforce, HubSpot, etc.) → Airbyte

I want to connect programmatically

Method	Language	Description	Reference
JDBC Driver	Java / any JVM language	Standard JDBC interface; supports SQL queries and DML	JDBC Driver
Python SDK	Python	PEP 249-compliant; supports SQL queries, bulk writes (bulkload), and real-time writes	Python SDK
Java SDK	Java	Supports bulk writes (BulkLoad) and real-time streaming writes (RealtimeStream)	Java SDK Bulk Upload · Java SDK Real-time Upload

Choosing a write mode:

Offline bulk import (GB-scale or larger) → BulkLoad (Java SDK or Python SDK bulkload)
Real-time row-by-row writes (millisecond latency) → RealtimeStream (Java SDK) or Python SDK real-time upload
Standard SQL INSERT → JDBC

I want to process data with a compute engine

Engine	Description	Reference
Apache Spark	Read and write Singdata Lakehouse tables via the Spark Connector; supports the DataFrame API and spark-sql	Spark Connector
Apache Flink	Write to Singdata Lakehouse via the Flink Connector; supports CDC scenarios and append-only mode; sink tables only (write)	Flink Connector

Two Flink Connector modes:

igs-dynamic-table: supports CDC (insert / update / delete); the target table must have a primary key
igs-dynamic-table-append-only: append only, no updates or deletes; the target table is a regular table

The Flink Connector currently supports write (sink table) only — it cannot be used as a source table or dimension table.

Other

Tool	Description	Reference
MindsDB	Machine learning platform; run predictions directly on Singdata Lakehouse data	MindsDB Integration Guide

For tools not listed here, you can create a custom connection using the JDBC driver or SQLAlchemy, depending on what connection methods the tool supports.

Not sure which approach to use?

What is your use case? ├── Interactive SQL queries / data exploration │ ├── GUI client → DBeaver or DataGrip │ └── Command line → cz-cli ├── Data visualization / reporting │ ├── Internal enterprise reporting → FineBI │ ├── Exploratory analysis → Tableau / Metabase │ └── Custom applications → Streamlit / Superset ├── Data integration / ETL │ ├── Offline batch sync → DataX │ ├── SQL modeling and transformation → dbt │ └── Multiple SaaS data sources → Airbyte ├── Programmatic access │ ├── Java applications → JDBC Driver or Java SDK │ └── Python applications → Python SDK └── Compute engine ├── Batch processing / ML → Spark Connector └── Stream processing / CDC → Flink Connector