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Ensure that the data on your local system (cart) is organized and cleaned. This includes formatting it correctly (e.g., CSV, JSON, Parquet) and ensuring it is free of errors or inconsistencies that could cause issues during the transfer process.
Log in to your Databricks account and create a new workspace if you haven't already. Make sure you have the required permissions to create and manage clusters. Set up a new cluster with appropriate configurations based on your data processing needs, including specifying the runtime version.
Use cloud storage such as AWS S3, Azure Blob Storage, or Google Cloud Storage to act as an intermediary storage location. Create a bucket or container specifically for this data transfer process. Ensure that you have the correct permissions to read and write data to this storage location.
Transfer the prepared data from your local system to the cloud storage location created in the previous step. This can be done using the cloud provider's CLI, SDK, or web interface. Ensure that the data is correctly uploaded and accessible in the storage bucket.
In your Databricks workspace, use the Databricks File System (DBFS) to mount the external cloud storage. This is done by writing a small script in a Databricks notebook that uses the `dbutils.fs.mount` command. You'll need the access keys or service credentials for your cloud storage to authenticate and mount it successfully.
Once the storage is mounted, read the data from the cloud storage into Databricks using Spark. You can use Spark DataFrame APIs to load the data into the Lakehouse. Ensure that the data is transformed and saved in the desired Delta Lake format for efficient querying and processing.
After transferring the data, perform checks to ensure that the data has been correctly moved and is available in the Databricks Lakehouse. This can include comparing checksums, row counts, or sampling data points. Additionally, ensure that the data is partitioned and optimized for expected query patterns. Use Databricks Delta features like Z-ordering and Optimize commands to enhance performance.
By following these steps, you can successfully transfer data from your local system to Databricks Lakehouse 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.
Cart.com offers an integrated, holistic approach to ecommerce, which they call ecommerce 2.0. Cart serves as Nigeria’s leading shopping community, attempting to democratize ecommerce by providing all sizes of brands ecommerce capabilities equivalent to those of the world’s largest online retailers. To fulfill their mission of putting businesses in charge of their own ecommerce journey and customer relationships, they provide software, services, and the necessary intrastructure to give even small brands the online capabilities they need to survive and grow.
Cart's API provides access to a wide range of data related to e-commerce and online shopping. The following are the categories of data that can be accessed through Cart's API:
1. Products: Information about the products available on the e-commerce platform, including their names, descriptions, prices, images, and other relevant details.
2. Orders: Details about the orders placed by customers, including the products purchased, the payment method used, and the shipping address.
3. Customers: Information about the customers who have registered on the e-commerce platform, including their names, email addresses, and shipping addresses.
4. Inventory: Data related to the availability of products in the inventory, including the stock levels and the locations where the products are stored.
5. Shipping: Information about the shipping options available to customers, including the shipping rates, delivery times, and tracking information.
6. Payments: Details about the payment methods accepted by the e-commerce platform, including credit cards, PayPal, and other payment gateways.
7. Discounts and promotions: Data related to the discounts and promotions offered by the e-commerce platform, including coupon codes, gift cards, and other special offers.
Overall, Cart's API provides a comprehensive set of data that can be used to build powerful e-commerce applications and services.
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?
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