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vbnet
SET HEADING ON
SET MARKUP CSV ON
SPOOL data_export.csv
SELECT * FROM your_table;
SPOOL OFF
duckdb
package).- Examine your Oracle schema and decide how each data type will map into DuckDB.
- For example, use
INTEGER
orBIGINT
for integer columns,DECIMAL(p, s)
for numeric columns with precision,TIMESTAMP
for date-time columns, andVARCHAR
for text. - If Oracle’s DATE columns include time data, define them as TIMESTAMP in DuckDB to avoid losing the time portion.
- Create the tables. For instance:
CREATE TABLE new_table (
column1 INTEGER,
column2 VARCHAR,
column3 TIMESTAMP
);
- Run the DuckDB CLI or use a script to load each CSV into its corresponding table.
- A common syntax is:
COPY new_table
FROM 'path/to/data_export.csv'
(AUTO_DETECT TRUE);
- Consider these points:
- If auto-detection fails, specify options like
DELIMITER ','
,HEADER TRUE
, orDATEFORMAT '%Y-%m-%d'
. - Ensure text and numeric columns match the table’s data types to avoid errors.
- If some rows contain invalid data for the target column type, fix those rows in the CSV or adjust the schema as needed.
- If auto-detection fails, specify options like
- Compare record counts with the original Oracle table using
SELECT COUNT(*) FROM table_name;
. - Inspect a few records:
SELECT * FROM table_name LIMIT 5;
.
- If needed, convert columns that imported as text into the right data type. For example:
UPDATE new_table
SET date_column = STRPTIME(date_column, '%d-%b-%y');
- Check any columns that in Oracle might have been empty strings but now appear as actual empty strings instead of NULL. Update them if your application needs them to be NULL:
UPDATE new_table
SET text_column = NULL
WHERE text_column = '';
- Align numeric columns if Oracle used a precision larger than what you defined in DuckDB.
Add Indexes and Constraints
- Align your new DuckDB tables with the constraints in Oracle. For instance:
ALTER TABLE new_table
ADD PRIMARY KEY (id_column);
- If you need unique constraints or foreign keys, add them similarly:
ALTER TABLE child_table
ADD FOREIGN KEY (fk_column) REFERENCES parent_table(pk_column);
- DuckDB often runs analytical queries quickly without many indexes, but if you have frequent point lookups, consider adding them using:
pgsql
CREATE INDEX idx_name ON new_table(column1);
COALESCE
in place of NVL
).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: