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Before beginning the data migration, ensure that you have access to your ClickHouse database and the necessary permissions to export data. Use SQL queries to select the specific data you intend to export. Familiarize yourself with the ClickHouse `SELECT INTO OUTFILE` command to export data into a CSV or TSV file format, which can be easily processed for DynamoDB import.
Execute the appropriate SQL query to export your data. For example, use:
```sql
SELECT * FROM your_table INTO OUTFILE '/path/to/exported_data.csv' FORMAT CSV;
```
This command will export the data from `your_table` into a CSV file located at the specified path. Ensure the file is stored in a location accessible for further processing.
DynamoDB requires data in a JSON format for import. Write a script (using Python, Node.js, or another language of your choice) to convert your CSV data into JSON. Pay special attention to DynamoDB's data types and structure, ensuring your JSON file adheres to these requirements. For instance, convert CSV rows into JSON objects with key-value pairs corresponding to DynamoDB's attribute types (e.g., `S` for string, `N` for number).
Install and configure the AWS Command Line Interface (CLI) on your machine. Use the `aws configure` command to set up your credentials and default region. This will enable you to interact with AWS services, including DynamoDB, from your command line. Make sure you have the necessary permissions to create and manipulate DynamoDB tables.
Create the target DynamoDB table using either the AWS Management Console or AWS CLI. Define the primary key schema (partition key and optionally a sort key) based on the structure of your data. For example, using AWS CLI:
```bash
aws dynamodb create-table --table-name YourTableName --attribute-definitions AttributeName=YourPrimaryKey,AttributeType=S --key-schema AttributeName=YourPrimaryKey,KeyType=HASH --provisioned-throughput ReadCapacityUnits=5,WriteCapacityUnits=5
```
Adjust the attribute types and provisioned throughput as needed.
Use the AWS CLI `batch-write-item` command or a custom script to import the JSON data into your DynamoDB table. Due to DynamoDB's batch write limitations (a maximum of 25 items per request), consider implementing batching in your script to iterate through the JSON data and perform multiple `batch-write-item` requests. Ensure each batch does not exceed the maximum item count and size limits.
Once the data import is complete, verify the integrity of the data by running queries on your DynamoDB table to ensure all records have been imported correctly. Compare a sample of records from ClickHouse and DynamoDB to confirm accuracy. After verification, clean up any temporary files or scripts used during the migration process to maintain a tidy environment.
By following these steps, you can successfully move data from ClickHouse to DynamoDB without relying on 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.
An open-source database management system for online analytical processing (OLAP), ClickHouse takes the innovative approach of using a column-based database. It is easy to use right out of the box and is touted as being hardware efficient, extremely reliable, linearly scalable, and “blazing fast”—between 100-1,000x faster than traditional databases that write rows of data to the disk—allowing analytical data reports to be generated in real-time.
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: