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Begin by logging into your Mailjet account and navigate to the SMS section. Select the data you wish to export, such as SMS logs or delivery reports. Export the data in a CSV format, which is typically supported by Mailjet. Save the exported CSV file to your local system.
Ensure that your local environment has the necessary tools for data processing. Install Python and libraries such as Pandas for data manipulation, and configure any additional tools needed to handle CSV files. Verify that your environment is set up to interact with Databricks.
Load the exported CSV file into a Pandas DataFrame for cleaning and transformation. Perform data cleansing tasks such as removing duplicates, handling missing values, and standardizing data formats. Ensure that the data structure matches the schema expected by your Databricks Lakehouse.
Access your Databricks workspace and create a new cluster if one is not already available. Configure the cluster to meet your processing requirements, ensuring it has the necessary resources and libraries to handle the imported data.
Use the Databricks CLI or web interface to upload the cleaned CSV file to the Databricks File System (DBFS). This step involves copying the file from your local system to a directory within DBFS, making it accessible to your Databricks notebooks and jobs.
In a new Databricks notebook, use PySpark or Spark SQL to read the CSV file from DBFS into a Spark DataFrame. Define the schema explicitly if needed, and then write the DataFrame into a Delta table within your Databricks Lakehouse. This table will serve as your structured data repository.
Perform data validation by querying the Delta table to ensure that the data has been ingested correctly and matches the expected structure. Apply any necessary optimizations, such as partitioning or indexing, to enhance query performance and storage efficiency within the Lakehouse.
By following these steps, you can effectively move data from Mailjet SMS to Databricks Lakehouse without relying on any 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.
Mailjet is one of the affordable software for email marketing campaigns SMS campaigns, newsletter creation, email template building etc. Mailjet permits you to send transactional SMS messages using our Send SMS API. The Mailjet Transactional SMS API offers a straight-forward way to add SMS functionalities to third-party applications. Mailjet's SMS API allows you to send text messages to users around the globe through a simple RESTful API.
Mailjet SMS's API provides access to various types of data related to SMS messaging. The categories of data that can be accessed through the API are as follows:
1. Account data: This includes information about the user's Mailjet SMS account, such as account ID, API key, and account balance.
2. Message data: This includes details about the SMS messages sent and received through the Mailjet SMS platform, such as message ID, sender ID, recipient number, message content, and delivery status.
3. Contact data: This includes information about the contacts or recipients of SMS messages, such as contact ID, phone number, and contact attributes.
4. Campaign data: This includes data related to SMS campaigns, such as campaign ID, campaign name, and campaign statistics.
5. Analytics data: This includes data related to SMS message performance, such as delivery rates, open rates, click-through rates, and conversion rates.
6. Integration data: This includes data related to the integration of Mailjet SMS with other platforms or applications, such as integration ID, integration type, and integration status.
Overall, Mailjet SMS's API provides comprehensive access to data related to SMS messaging, enabling users to track and optimize their SMS campaigns for maximum effectiveness.
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:
