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Begin by ensuring you have access to a Starburst Galaxy account and the necessary permissions to create catalogs and schemas. Log into your Starburst Galaxy web interface and prepare your environment for data ingestion.
Verify that your Parquet file is accessible from the machine or cloud environment where you plan to run your data transfer operations. Ensure that the file is correctly formatted and not corrupted.
Use Starburst Galaxy's interface to create an external table that maps to your Parquet file. This involves specifying the data structure of the Parquet file and creating a table definition that mirrors this structure in Starburst.
For example, use a SQL query similar to:
```sql
CREATE TABLE your_schema.your_external_table (
column1 data_type,
column2 data_type,
...
)
WITH (
format = 'PARQUET',
external_location = 's3://your-bucket/path/to/parquet/file/'
);
```
Once the external table is defined, load the data from the Parquet file into a temporary table in Starburst Galaxy. This step ensures that the data is in Starburst’s internal storage, optimizing subsequent operations.
Execute a SQL query to insert data:
```sql
CREATE TABLE your_schema.your_temp_table AS
SELECT * FROM your_schema.your_external_table;
```
Run queries to verify that the data in the temporary table matches the original Parquet file. Check for any discrepancies in row counts and data types.
Example query:
```sql
SELECT COUNT(*) FROM your_schema.your_temp_table;
```
If any data transformation or cleaning is needed, perform these operations on the temporary table. Use SQL queries to adjust data as required for your final use case.
Example transformation:
```sql
CREATE TABLE your_schema.your_final_table AS
SELECT column1, column2, ..., FUNCTION(column3) AS transformed_column
FROM your_schema.your_temp_table;
```
Once the data is verified and transformed, move it to the final table in your Starburst Galaxy schema. This step involves inserting the cleaned data into your production-ready table.
Finalize with:
```sql
INSERT INTO your_schema.your_final_table
SELECT * FROM your_schema.your_temp_table;
```
By following these steps, you’ll efficiently move data from a Parquet file into Starburst Galaxy using SQL commands and built-in functionalities, without the need for third-party connectors or integrations.
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.
Parquet File is a columnar storage file format that is designed to store and process large amounts of data efficiently. It is an open-source project that was developed by Cloudera and Twitter. Parquet File is optimized for use with Hadoop and other big data processing frameworks, and it is designed to work well with both structured and unstructured data. The format is highly compressed, which makes it ideal for storing and processing large datasets. Parquet File is also designed to be highly scalable, which means that it can be used to store and process data across multiple nodes in a distributed computing environment.
Parquet File's API gives access to various types of data, including:
• Structured data: Parquet files can store structured data in a columnar format, making it easy to query and analyze large datasets.
• Semi-structured data: Parquet files can also store semi-structured data, such as JSON or XML, allowing for more flexibility in data storage.
• Unstructured data: Parquet files can store unstructured data, such as text or binary data, making it possible to store a wide range of data types in a single file.
• Big data: Parquet files are designed for big data applications, allowing for efficient storage and processing of large datasets.
• Machine learning data: Parquet files are commonly used in machine learning applications, as they can store large amounts of data in a format that is optimized for processing by machine learning algorithms.
Overall, Parquet File's API provides access to a wide range of data types, making it a versatile tool for data storage and analysis in a variety of applications.
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: