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- Connect to your Oracle database using SQL*Plus or any other Oracle database client.
- Determine the data you want to export. You may want to export entire tables or just a subset of data, depending on your requirements.
- Export the data to a CSV file or another suitable format using Oracle’s export utilities like expdp or sqlplus. For example, you can use the following command in SQL*Plus to export a table to a CSV file:
SPOOL /path/to/your/outputfile.csv
SELECT /*csv*/ * FROM your_table;
SPOOL OFF
- Compress the file to reduce the size and transfer time, using a tool like gzip.
- Choose a cloud storage service compatible with Databricks, such as AWS S3, Azure Blob Storage, or Google Cloud Storage.
- Upload the exported file(s) to the chosen cloud storage. You can use the cloud provider’s web interface, CLI, or SDKs to upload the files.
- Log in to your Databricks workspace.
- Create a new cluster or use an existing cluster that meets your workload requirements.
- Install any necessary libraries on the cluster that may be required for reading from your cloud storage or processing the data.
- Mount the cloud storage to DBFS (Databricks File System) using Databricks’ built-in utilities. This will allow you to access the data as if it were a local file system. For example, to mount an S3 bucket, you can use the following command:
dbutils.fs.mount("s3a://your-bucket-name", "/mnt/your-mount-name")
- Read the data into a Spark DataFrame using the appropriate Spark APIs. For example, to read a CSV file:
df = spark.read.csv("/mnt/your-mount-name/path/to/your/outputfile.csv")
- Perform any necessary data transformations using Spark DataFrame transformations.
- Cleanse and prepare the data for storage in Databricks Lakehouse.
- Define the target location within Databricks Lakehouse where you want to store the data.
- Write the data from the Spark DataFrame to Databricks Lakehouse using DataFrame writer API. For example, to write data to Delta Lake format:
df.write.format("delta").save("/mnt/your-mount-name/delta/your-table")
- Verify the data has been transferred correctly by reading a sample of the data from Databricks Lakehouse and comparing it against the original data from Oracle.
- Perform any additional validation checks as necessary, such as row counts, data types, and integrity constraints.
- Un-mount the cloud storage if it is no longer needed.
- Delete any temporary files that were created during the process.
- Create a Databricks job to automate the data transfer process.
- Schedule the job to run at your desired frequency.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Oracle DB is a fully scalable integrated cloud application and platform service; it is also referred to as a relational database architecture. It provides management and processing of data for both local and wide and networks. Offering software-as-a-service (SaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS), it sells a large variety of enterprise IT solutions that help companies streamline the business process, lower costs, and increase productivity.
Oracle DB provides access to a wide range of data types, including:
• Relational data: This includes tables, views, and indexes that are used to store and organize data in a structured manner.
• Spatial data: This includes data that is related to geographic locations, such as maps, satellite imagery, and GPS coordinates.
• Time-series data: This includes data that is related to time, such as stock prices, weather data, and sensor readings.
• Multimedia data: This includes data that is related to images, videos, and audio files.
• XML data: This includes data that is stored in XML format, such as web pages, documents, and other structured data.
• JSON data: This includes data that is stored in JSON format, such as web APIs, mobile apps, and other data sources.
• Graph data: This includes data that is related to relationships between entities, such as social networks, supply chains, and other complex systems.
Overall, Oracle DB's API provides access to a wide range of data types that can be used for a variety of applications, from business intelligence and analytics to machine learning and artificial intelligence.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Oracle DB is a fully scalable integrated cloud application and platform service; it is also referred to as a relational database architecture. It provides management and processing of data for both local and wide and networks. Offering software-as-a-service (SaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS), it sells a large variety of enterprise IT solutions that help companies streamline the business process, lower costs, and increase productivity.
Databricks is an American enterprise software company founded by the creators of Apache Spark. Databricks combines data warehouses and data lakes into a lakehouse architecture.
1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
2. Click on the "Oracle DB" source connector and select "Create new connection".
3. Enter a name for your connection and click "Next".
4. In the "Connection Configuration" section, enter the following information:
- Host: the hostname or IP address of your Oracle DB server
- Port: the port number used to connect to your Oracle DB server
- Database: the name of the database you want to connect to
- Username: your Oracle DB username
- Password: your Oracle DB password
5. Click "Test connection" to ensure that the connection is successful.
6. If the connection is successful, click "Next" to proceed to the "Schema Selection" section.
7. In the "Schema Selection" section, select the schema(s) you want to replicate data from.
8. Click "Create connection" to save your connection settings.
9. You can now create a new Oracle DB source in Airbyte and start replicating data from your Oracle DB database.
1. First, navigate to the Airbyte website and log in to your account.
2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.
3. Scroll down until you find the "Databricks Lakehouse" connector and click on it.
4. You will be prompted to enter your Databricks Lakehouse credentials, including your account name, personal access token, and workspace ID.
5. Once you have entered your credentials, click on the "Test" button to ensure that the connection is successful.
6. If the test is successful, click on the "Save" button to save your Databricks Lakehouse destination connector settings.
7. You can now use the Databricks Lakehouse connector to transfer data from your source connectors to your Databricks Lakehouse destination.
8. To set up a data transfer, navigate to the "Sources" tab and select the source connector that you want to use.
9. Follow the prompts to enter your source connector credentials and configure your data transfer settings.
10. Once you have configured your source connector, select the Databricks Lakehouse connector as your destination and follow the prompts to configure your data transfer settings.
11. Click on the "Run" button to initiate the data transfer.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
Oracle DB provides access to a wide range of data types, including:
• Relational data: This includes tables, views, and indexes that are used to store and organize data in a structured manner.
• Spatial data: This includes data that is related to geographic locations, such as maps, satellite imagery, and GPS coordinates.
• Time-series data: This includes data that is related to time, such as stock prices, weather data, and sensor readings.
• Multimedia data: This includes data that is related to images, videos, and audio files.
• XML data: This includes data that is stored in XML format, such as web pages, documents, and other structured data.
• JSON data: This includes data that is stored in JSON format, such as web APIs, mobile apps, and other data sources.
• Graph data: This includes data that is related to relationships between entities, such as social networks, supply chains, and other complex systems.
Overall, Oracle DB's API provides access to a wide range of data types that can be used for a variety of applications, from business intelligence and analytics to machine learning and artificial intelligence.