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Begin by accessing your Apify account and navigating to the desired dataset. Use the Apify API to extract the data. You can do this by sending an HTTP GET request to the dataset endpoint, which will return the data in a JSON format. Save this data locally or in a specified storage location.
Once you have the JSON data, transform it into a CSV format. This can be accomplished using a scripting language like Python. Utilize libraries such as `pandas` to read the JSON data and convert it into a CSV file, which is a more universally acceptable format for data ingestion processes.
Log into your Databricks account and set up a new cluster if you haven't already. Ensure that the cluster is running so that it can process the data. Make note of the cluster details as you'll need them for uploading and accessing the data.
Use Databricks' built-in interface to upload your CSV file to the Databricks File System (DBFS). You can do this through the Databricks web UI by navigating to the 'Data' tab and selecting 'Add Data'. Then, upload the CSV file from your local system to DBFS.
With the CSV file uploaded, you need to create a table in Databricks Lakehouse. Use a Databricks notebook to execute Spark SQL commands. Start by using the `CREATE TABLE` command to define a new table schema that matches your CSV data structure.
Execute a `COPY INTO` command to load the data from the CSV file into the table you just created. This command will read the CSV file from DBFS and insert the data into the table, making it part of the Databricks Lakehouse.
After loading the data, run queries to verify that the data is correctly ingested and maintains its integrity. Check for any discrepancies or errors in the data. Use SQL commands to run counts, checksums, or sample queries to ensure that the dataset is complete and accurate.
By following these steps, you can effectively move data from Apify 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.
Apify is a web scraping and automation platform that can extract structured data from any website or automate any workflow on the web. For example, imagine you found a website selling shoes and want to get a spreadsheet with all the shoe sizes, colors, prices, etc., but the website doesn't make that information accessible in tabular form. Youcould certainly manually create such a spreadsheet using copy and paste, but that would take a lot of time and cause a lot of frustration. Or you can set up Apify to do this for you in a few seconds.
Apify's API provides access to a wide range of data types, including:
1. Web scraping data: Apify's web scraping tools allow users to extract data from websites and APIs, including HTML, JSON, XML, and CSV formats.
2. Social media data: Apify's API can be used to extract data from social media platforms such as Twitter, Facebook, and Instagram, including posts, comments, and user profiles.
3. E-commerce data: Apify's API can be used to extract data from e-commerce platforms such as Amazon, eBay, and Shopify, including product listings, prices, and reviews.
4. Search engine data: Apify's API can be used to extract data from search engines such as Google, Bing, and Yahoo, including search results, rankings, and keyword data.
5. Financial data: Apify's API can be used to extract financial data from sources such as stock exchanges, financial news websites, and investment platforms.
6. Weather data: Apify's API can be used to extract weather data from sources such as weather APIs and weather news websites.
7. Government data: Apify's API can be used to extract data from government websites and APIs, including census data, crime statistics, and public records.
Overall, Apify's API provides access to a wide range of data types, making it a powerful tool for data extraction and analysis.
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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