Row64 Integrations
Row64 is designed to easily connect to many Database, Data Lake and Data Application platforms.
This project contains a folder per integration. The primary strategy used is to pull from the data source and copy it into RamDb. A general overview of this connection strategy is here:
https://pypi.org/project/row64tools/
The integrations are examples that are intended to be modified to fit your specific needs. Linux distributions, Python, Pandas, connectors and security requirments are in a time period of rapid change. So, if you find any problems, please log them under Issues and contact our support team if you are under a commercial license.
For high speed data streaming (where the update rate is in seconds or milliseconds), please check out Row64 streaming. There are a different set of connectors and a different connection strategy that pushes updates directly through the server to connected clients. More details here:
Row64 Stream Overview V3.5
These examples are mostly in Python, but we intend to grow them in multiple languages. We also intend to add some C++ low-level examples for higher speed at larger scales (10M-1B records). For a dataset under 10 million records that is not frequently updating, Python should be sufficent.
Pages in this section
- Amazon Athena
- Amazon DynamoDB
- Amazon Redshift
- Apache Cassandra
- Apache Drill
- Apache Druid
- Apache Hive
- Apache Impala
- Apache Kylin
- Apache Pinot
- Apache Solr
- Ascend.io
- Azure MSSQL
- BigQuery
- ClickHouse
- CockroachDB
- Databricks
- Dremio
- DuckDB
- Elasticsearch
- Exasol
- Firebolt
- Google Sheets
- Hologres
- IBM DB 2
- IBM Netezza
- MariaDB
- MongoDB
- MySQL
- Oracle
- PostgreSQL
- Presto
- SAP S/4HANA
- Snowflake
- SQLite
- SQL Server
- Teradata
- Trino
- Vertica
- YugabyteDB