This repository contains the source code for databricks, for loading multiple databases, currently compatible with SQL Server and MongoDB.
The main goal of these scripts is to facilitate the efficient and flexible loading of data from different database schemas, specifically designed to integrate with Databricks for streamlined data handling.
- Generic Configuration: Allows configuration of different databases by simply passing connection parameters such as host, user, password, and database name.
- Incremental Loading: Supports incremental loading, enabling the specification of the table and date column to load only the most recent data.
- Compatibility: Currently supports MSSQL Server and MongoDB, with the possibility of expanding to other databases easily with providers
- Ease of Use: Scripts are designed to be easily configurable and executable, minimizing the need for manual intervention.
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Configuration:
- Create a configuration file or pass the parameters directly, including details like database type, host, port, user, password, and database name.
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Full Loading:
- To perform a full data load, simply configure the connection parameters and run the corresponding script.
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Incremental Loading:
- For incremental loading, specify the table and date column. The script will fetch records added or updated since the last execution.
Using Databricks for data extraction and loading offers several advantages over traditional ETL tools like Azure Data Factory (ADF):
- Databricks provides a unified platform for data engineering, machine learning, and analytics, which allows for a seamless transition between data loading and processing tasks.
- Unlike ADF, which focuses primarily on orchestration and pipeline management, Databricks integrates data processing capabilities directly into the loading process, reducing the need for multiple tools.
- Databricks is built on Apache Spark, known for its ability to process large volumes of data efficiently. This makes it ideal for handling big data workloads that might be cumbersome in ADF.
- Parallel processing and in-memory computation in Databricks can significantly reduce data load times compared to ADF's batch-oriented approach.
- Databricks allows for greater flexibility in data transformations and custom scripting, which can be written in languages like Python, Scala, and SQL.
- ADF has predefined activities and limited flexibility for custom transformations, whereas Databricks allows for complex, tailored data processing pipelines.
- Databricks can often be more cost-effective for extensive data processing tasks as it eliminates the need for separate data transformation stages that ADF might require.
- It reduces the complexity of managing multiple services, as data extraction, transformation, and analysis can all occur within Databricks.
- With Databricks, it is easier to implement real-time streaming and process data as it arrives, a feature that ADF does not natively support.
- This real-time capability is crucial for applications that need immediate insights from continuously flowing data.
- Databricks seamlessly integrates with machine learning and advanced analytics workflows, allowing for immediate utilization of loaded data in predictive modeling and other analytic processes.
- ADF would typically require integration with other services to achieve similar functionality, adding complexity and potential points of failure.
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