How to load data from Nasa to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Nasa data into Databricks Lakehouse within minutes.
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How to Sync to Manually
Step 1: Identify NASA Data Source
Identify the NASA datasets you wish to move. NASA provides various data portals, such as Earthdata, where you can access datasets. Ensure you have the necessary credentials or API keys to access these datasets if required.
Step 2: Download NASA Data Locally
Use tools like `wget` or `curl` to download the datasets directly to a local machine. For example, you can use:
```bash
wget -O local_file_name
```
Ensure that you have adequate local storage to hold the data before downloading.
Step 3: Prepare Data for Upload
Verify the integrity of the downloaded data and convert it into a format that's compatible with Databricks, such as CSV, JSON, or Parquet. Use data transformation tools like Python with pandas if any conversion is needed:
```python
import pandas as pd
df = pd.read_csv('local_file_name.csv')
df.to_parquet('local_file_name.parquet')
```
Step 4: Set Up Databricks Environment
Log into your Databricks account and create a new cluster if you don't already have one. Ensure that your cluster is running and configured with the necessary permissions to access the workspace's FileStore.
Step 5: Upload Data to Databricks FileStore
Use Databricks' web interface to upload files directly to the FileStore. Navigate to the "Data" section, click on "Add Data," and follow the instructions to upload the prepared files. This will place the data in `/FileStore/tables/`.
Step 6: Load Data into Databricks Lakehouse
Use a Databricks notebook to load the data from the FileStore into a Delta Lake table. For example:
```python
df = spark.read.format("parquet").load("/FileStore/tables/local_file_name.parquet")
df.write.format("delta").saveAsTable("nasa_data_table")
```
This step converts the data into a Delta Lake format, allowing for efficient querying and management.
Step 7: Verify Data Integrity and Accessibility
Execute a few test queries to ensure the data has been correctly loaded into the Lakehouse:
```python
spark.sql("SELECT FROM nasa_data_table LIMIT 10").show()
```
Verify that the data structure matches your expectations and that all records have been successfully imported.
By following these steps, you can efficiently transfer and store NASA data within the Databricks Lakehouse environment without relying on third-party connectors.