ClickHouse is a high-performance columnar database management system designed for real-time analytics. It supports a wide range of data sources for ingestion and integration, including:
Local Files: ClickHouse can directly read data from local files in formats like CSV, JSON, Parquet, ORC, and Avro. For example, you can use the INSERT INTO statement to load data from a CSV file.
Kafka: ClickHouse integrates with Apache Kafka for real-time data streaming. You can use the Kafka engine table to consume messages from Kafka topics and process them in real time.
MySQL/MariaDB: ClickHouse supports federated queries to MySQL or MariaDB databases using the MySQL engine table, allowing you to query external MySQL data directly.
PostgreSQL: Similar to MySQL, ClickHouse can connect to PostgreSQL using the PostgreSQL engine table for federated queries.
HDFS (Hadoop Distributed File System): ClickHouse can read data stored in HDFS using the HDFS engine table, enabling big data analytics.
S3-Compatible Storage: ClickHouse supports reading data from S3-compatible object storage (e.g., Tencent Cloud COS) using the S3 engine table, allowing efficient querying of large datasets stored in the cloud.
Other Databases: ClickHouse can connect to other databases like SQLite, ODBC, and JDBC sources via external integrations.
For example, if you're using Tencent Cloud, you can store data in Tencent Cloud COS (Cloud Object Storage) and query it directly in ClickHouse using the S3 engine table, enabling seamless cloud-based analytics.