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One key feature of any powerful SQL platform is its ability to easily connect to external systems for data retrieval or logics execution. In this blog, we will dive into how Databricks SQL, a leading cloud-based SQL platform, supports integrations with external systems, illustrated with SQL code examples.
What is Databricks?
Databricks is a data analytics platform built around Apache Spark, an open-source, distributed computing system. Databricks is highly effective for big data processing, machine learning tasks, and also for running SQL queries on vast amounts of data.
Databricks SQL and External System Integration
Databricks SQL enables you to run interactive queries on your data stored in different systems, including AWS S3, Azure Blob Storage, or Google Cloud Storage. You can easily integrate these platforms with Databricks SQL to enable seamless data retrieval and analysis.
Connecting to AWS S3 with Databricks SQL
To connect Databricks SQL with AWS S3, you need to specify the S3 bucket and the access credentials (access key and secret access key).
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CREATE TABLE s3_table USING CSV OPTIONS ('header'='true', 'inferSchema'='true') LOCATION 's3a://bucket-name/data.csv'; |
Connecting to Azure Blob Storage with Databricks SQL
Connecting to Azure Blob Storage requires the storage account name and the storage account access key.
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CREATE TABLE azure_table USING CSV OPTIONS ('header'='true', 'inferSchema'='true') LOCATION 'wasbs:<a href="mailto://bucket-name@account-name.blob.core.windows.net/data.csv'" >//bucket-name@account-name.blob.core.windows.net/data.csv'</a>; |
Connecting to Google Cloud Storage with Databricks SQL
For Google Cloud Storage, you need to provide the bucket name. Set the necessary access rights on Google Cloud and Databricks will use the default service account to access the data.
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CREATE TABLE gcp_table USING CSV OPTIONS ('header'='true', 'inferSchema'='true') LOCATION 'gs://bucket-name/data.csv'; |
Conclusion
While these examples use storage services of different cloud providers, Databricks SQL enables connections to various databases and data warehouses, further enriching its capabilities. Leveraging these integrations can help data professionals to access, analyze, and draw insights from disparate data sources efficiently. Stay tuned for more in-depth exploration of using Databricks SQL for data integration tasks!