Continuous Data Loading in Snowflake – Data Orchestration Techniques
By Sheila Simpson / May 8, 2022 / No Comments / Amazon AWS Exams, Azure and AWS, Azure Synapse and Its ETL Features, Capabilities Covered by Tools, Microsoft Exams
Continuous Data Loading in Snowflake
Continuous data loading in Snowflake involves ingesting and processing data in real-time or near real-time, ensuring that the target tables are continuously updated with the latest data. Snowflake offers several options to achieve continuous data loading, depending on the data source and the desired level of latency. Here are some approaches to consider:
• Streaming Data Ingestion: Snowflake supports streaming data ingestion through its integration with various messaging and streaming platforms, such as Apache Kafka, Amazon Kinesis, and Google Cloud Pub/Sub. By leveraging Snowflake’s Snowpipe service or using external tools like Apache NiFi, organizations can stream data directly into Snowflake tables in real-time. Snowpipe provides automated ingestion and processing of data as soon as it arrives, ensuring continuous loading of streaming data.
• Change Data Capture (CDC): CDC is a technique that captures and tracks changes made to the source data. Snowflake can integrate with CDC tools, such as Apache Kafka Connect, Debezium, or proprietary CDC solutions, to capture changes from transactional databases.
These tools capture inserts, updates, and deletes and deliver them to Snowflake, allowing for continuous loading of the changed data into target tables.
• Scheduled Batch Loads: If real-time data ingestion is not required, organizations can schedule batch loads at regular intervals using Snowflake’s data loading capabilities. Batch jobs can be triggered by a scheduler (e.g., Snowflake’s task scheduler or external schedulers like cron) to load data from various sources, such as files in cloud storage or databases, into Snowflake tables. By setting the desired scheduling frequency, organizations can achieve near-real-time or frequent data updates.
• External Data Sources: Snowflake allows direct querying of external data sources, including cloud storage platforms like Amazon S3, Azure Blob storage, or Google cloud storage. Organizations can set up continuous data loading by ensuring that the external data sources are regularly updated, and Snowflake queries are executed to access the latest data from these sources.
• Snowflake Data Sharing: Snowflake’s data sharing feature enables continuous data loading from external organizations or partners. With data sharing, organizations can securely share datasets, including real-time or near real-time data, between Snowflake accounts. This allows continuous updates of shared data as the source data changes, ensuring synchronized access to the most recent data.