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Begin by exporting the data from your IBM DB2 database. You can use the `EXPORT` command in DB2 to export data into a flat file format such as CSV. This can be done by executing a command like `EXPORT TO 'data.csv' OF DEL MODIFIED BY NOCHARDEL SELECT * FROM your_table` within the DB2 command line or a suitable script. Ensure the exported file is in a format that can be easily imported, such as CSV or TSV.
Once you have exported your data, transfer the file to a location accessible by Starburst Galaxy. This can be a local filesystem or a cloud storage service like Amazon S3, Google Cloud Storage, or Azure Blob Storage. Use secure transfer methods such as SCP or the relevant cloud service's CLI tools to ensure data integrity during transfer.
Set up your Starburst Galaxy environment to access the data files. This involves ensuring you have the necessary permissions and access configurations set up to read from the location where your data file is stored. For cloud storage, this may include setting up appropriate IAM roles or access keys.
Within Starburst Galaxy, define a catalog that points to the location of your data file. If using a cloud storage service, configure a catalog using the appropriate connector (e.g., S3, GCS, Azure) and specify the bucket or container where the file resides. This setup will allow Starburst Galaxy to access and query the data.
Use Starburst Galaxy�s SQL interface to create an external table that maps to your data file. Define the table structure to match the schema of the data you exported from DB2. For example, use a statement like `CREATE TABLE your_table_external (column1 TYPE, column2 TYPE, ...) WITH (external_location = 's3://your-bucket/data.csv', format = 'CSV')`.
Execute SQL queries against your newly created external table to validate the data transfer. This ensures that the data structure and content are correct. Run checks to compare row counts, data types, and sample data between the original DB2 dataset and the external table in Starburst Galaxy.
If needed, load the data into a permanent table within Starburst Galaxy for better performance or ease of access. This can be done by executing an `INSERT INTO` statement from the external table into a newly created or existing permanent table. This step is optional and depends on your data handling and performance requirements.
By following these steps, you can effectively move your data from IBM DB2 to Starburst Galaxy without the need for third-party connectors or integrations, while ensuring data integrity and accessibility.
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.
Specializing in the development and maintenance of Android, iOS, and Web applications, DB2’s AI technology offers fast insights, flexible data management, and secure data movement to businesses globally through its IBM Cloud Pak for Data platform. Companies rely on DB2’s AI-powered insights and secure platform and save money with its multimodal capability, which eliminates the need for unnecessary replication and migration of data. Additionally, DB2 is convenient and will run on any cloud vendor.
IBM Db2 provides access to a wide range of data types, including:
1. Relational data: This includes tables, views, and indexes that are organized in a relational database management system (RDBMS).
2. Non-relational data: This includes data that is not organized in a traditional RDBMS, such as NoSQL databases, JSON documents, and XML files.
3. Time-series data: This includes data that is collected over time and is typically used for analysis and forecasting, such as sensor data, financial data, and weather data.
4. Geospatial data: This includes data that is related to geographic locations, such as maps, satellite imagery, and GPS coordinates.
5. Graph data: This includes data that is organized in a graph structure, such as social networks, recommendation engines, and knowledge graphs.
6. Machine learning data: This includes data that is used to train machine learning models, such as labeled datasets, feature vectors, and model parameters.
Overall, IBM Db2's API provides access to a diverse range of data types, making it a powerful tool for data management and analysis.
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:
