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Promethium Data Connectors

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Connect, catalog, and query Cloud and on-premise data across Relational, NoSQL, Big Data, and SaaS data sources in minutes.

Data connection
made fast and easy

Data Lakes

Data Warehouses

Databases

SaaS Apps

Business Apps

Featured Data Connectors

Promethium's most popular data connectors with enhanced features, like parallelism, pushdown, table statistics, and authentication, drastically improve overall performance and security.

Promethium Data Connectors: IBM DB2, JDBC, Delta.io, Teradata, Snowflake, SAP HANA, Greenplum
Promethium Data Connectors: Apache Hive, Cloudera Database, MemSQL, SingleStore, MySQL, Oracle, MAPR, IBM Cloud, Microsoft SQL Server, PostgreSQL, Metastore
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Data Connectors

Database
Clipper

Clipper

dBase

dBase

Microsoft Visual FoxPro Logo

FoxPro

Greenplum Logo

Greenplum

IBM db2

IBM Db2

Ibm Informix

IBM Informix

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 SingleStore

MS SQL Server

Microsoft SQL Server

MySQL Enterprise

MySQL

Oracle Database

Oracle Database

Pervasive SQL

Pervasive SQL

PostgreSQL

PostgreSQL

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Progress OpenEdge

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SAP ASE

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SAP IQ

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SequeLink

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Teradata

Apache Accumulo

Apache Hudi

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Apache Ignite

Apache Kafka

Apache Kylin

MariaDB

Pinot​

Big Data
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Amazon EMR Hive

Apache Cassandra

Apache Hive

Apache Spark SQL

Cloudera CDH Hive

Cloudera Impala

DataStax

Delta Lake

Hortonworks Hive

IBM BigInsights

MapR Hive

MongoDB

Pivotal HD & HDB

Apache Impala

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Couchbase

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Druid​

Apache Iceberg

Apache Kudu

Apache Athena

Cockroach DB

Java

Prometheus

Python

Cloud Data Warehouse
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Amazon Redshift

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Azure Synapse

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Google BigQuery

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IBM Db2 Warehouse

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Oracle Autonomous DWH

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Snowflake

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ClickHouse

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AppMetrica

Vertica

Treasure Data

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Elastic Search

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Google Sheets

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Amazon Kenesis

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Google Analytics

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Scylla

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SaaS and Cloud
Promethium + AWS S3

AWS S3

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Azure SQL Database

Oracle Cloud Database

Oracle Database Cloud

Salesforce Connector

Salesforce

Rockset

Azure Data Lake Storage

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Didn't find the connector you were looking for?

There are just too many connectors to list!

Interested in reducing data consumption costs?
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