DÆTA provides integration capabilities with various third-party services to enhance functionality and user experience.
Content Delivery Network (CDNs)
Integrate DÆTA with popular CDNs to optimize content delivery:
from daeta_sdk import DaetaClient
from cloudflare import CloudflareClient
daeta_client = DaetaClient('YOUR_DAETA_API_KEY')
cf_client = CloudflareClient('YOUR_CLOUDFLARE_API_KEY')
def serve_file_via_cdn(file_id):
file_url = daeta_client.get_file_url(file_id)
cdn_url = cf_client.create_cdn_resource(file_url)
return cdn_url
# Usage
cdn_url = serve_file_via_cdn('1234abcd')
print(f"File accessible via CDN: {cdn_url}")
Backup and Disaster Recovery
Implement automated backup solutions using DÆTA:
import schedule
from daeta_sdk import DaetaClient
client = DaetaClient('YOUR_API_KEY')
def backup_database():
dump_file = '/tmp/db_dump.sql'
os.system(f'pg_dump my_database > {dump_file}')
client.upload_file(dump_file, 'database-backups')
schedule.every().day.at("02:00").do(backup_database)
while True:
schedule.run_pending()
time.sleep(1)
AI and Machine Learning Platforms
Integrate DÆTA as a data source for AI/ML models:
from daeta_sdk import DaetaClient
import tensorflow as tf
daeta_client = DaetaClient('YOUR_API_KEY')
def load_training_data():
file_ids = daeta_client.list_files('training-data')
datasets = []
for file_id in file_ids:
file_content = daeta_client.download_file(file_id)
dataset = tf.data.Dataset.from_tensor_slices(file_content)
datasets.append(dataset)
return tf.data.Dataset.concatenate(datasets)
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(load_training_data(), epochs=10)
