Third-party Integrations

Integrates with third-party services for enhanced functionality.

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)

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Last updated 7 months ago

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}")

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)

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)