Creating an Automated Data Pipeline with Databricks

In this article, we'll explore how to use Databricks to build a complete data pipeline. We'll cover everything from gathering and cleaning raw data to analyzing the processed data. By the end of this Article, you can build an end-to-end data pipeline that automates data processing and analysis, freeing up time and resources for other critical tasks. Let's dive in!

What is a data pipeline?

A data pipeline is a process that takes data from different sources and transforms it into a format that is easier to use. The process usually involves extracting the data, cleaning it up, and storing it in a different location. The cleaned data can then be used for analysis or other purposes.

One example of a data pipeline is the ETL process. This process involves extracting the data from different sources, transforming it into a usable format, and then loading it into a database or data warehouse. By doing this, the data becomes organized and easy to use for data analysts and scientists.

Steps to create a Data Pipeline using Databricks:

In this article, we'll guide you through creating a data pipeline on Databricks. Here are the steps we'll cover:

1. Use Databricks tools to examine a raw dataset.
2. Create a Databricks notebook to collect the raw data and save it to a target table.
3. Create a Databricks notebook to modify the raw data and save the changes to a target table.
4. Create a Databricks notebook to analyze the modified data.
5. Schedule the data pipeline to run automatically using a Databricks job.

Following these steps will help you create an end-to-end data pipeline on Databricks that can handle a variety of data processing tasks.

Step 1: Set up a Cluster

To carry out the data processing and analysis in this article, you'll need a cluster to provide the computing resources. Here's how you can set it up:

1. Click on the "Compute" icon in the sidebar.
2. On the Compute page, select "Create Cluster" and the "New Cluster" page will appear.
3. Provide a unique name for the cluster and leave the other values in their default state.
4. Click on "Create Cluster".

By following these steps, you'll have a cluster set up and ready to run commands for your data pipeline. If you want to learn more about Databricks clusters, you can check out the "Clusters" section.

Step 2: Check out the Source Data

To get started with processing data, you'll need to check out the raw data. Here's how you can do it:

1. Click on the "New" icon in the sidebar and select "Notebook" from the menu. The "Create Notebook" dialog will pop up.
2. Enter a name for the notebook, such as "Explore Data". Choose "Python" as the default language and select the cluster you created or an existing one.
3. Click on "Create".

To view the contents of the directory that contains the dataset, write the following code into the first cell of the notebook. Then, click on the "Run Menu" button and select "Run Cell":

The README file contains information about the dataset, including the data schema. The schema information is important for the next step of ingesting the data.

To view the contents of the README file, follow these steps:

1. Click on the downward arrow in the cell actions menu and select "Add Cell Below".
2. Write the following code into the new cell.
3. Click on the "Run Menu" button and select "Run Cell".

The records used in this example are stored in the "/databricks-datasets/songs/data-001/" directory. To see what's in this directory, follow these steps:

1. Click on the downward arrow in the cell actions menu and select "Add Cell Below".
2. Write the following code into the new cell.
3. Click on the "Run Menu" button and select "Run Cell".

To view a sample of the records, follow these steps:

1. Click on the downward arrow in the cell actions menu and select "Add Cell Below".
2. Write the following code into the new cell.
3. Click on the "Run Menu" button and select "Run Cell".

By viewing a sample of the records, you can make some observations about the data. These observations will come in handy later when you process the data:

• The records don't have a header, but there's a separate file in the same directory that contains the header.
• The files are in tab-separated value (TSV) format.
• Some fields may be missing or invalid.

Step 3: Moving data to Delta Lake

Databricks suggests saving data using Delta Lake, which is an open-source storage layer that provides ACID transactions and supports the data lakehouse architecture. In Databricks, Delta Lake is the default format for creating tables.

To start ingesting the raw data into Delta Lake, follow these steps:

1. Click the New icon in the sidebar and select Notebook from the drop-down menu.
2. Name the notebook "Ingest data," choose Python as the Default Language, and select the cluster you created or an existing one.
3. Type the following code in the first cell of the notebook, replacing <table-name> with the desired Delta table name, and <checkpoint-path> with a path to a directory in DBFS to store checkpoint files.
4. Click Run Menu and select Run Cell. This code will define the data schema using information from the README file, ingest the songs data from all the files in file_path, and save the data to the Delta table specified by table_name.

Step 4: Transform and write data to Delta Lake

To refine the song's data, you can filter out unwanted columns and add a timestamp for the creation of each new record. Here's how to do it:

1. Click the "New" icon in the sidebar and select "Notebook" from the menu.
2. Give your notebook a name, such as "Prepare data." Choose "SQL" as the default language and select the cluster you created or an existing one.
3. Click "Create."
4. In the first cell of the notebook, enter the SQL code to filter out the unwanted columns and add a new field containing the timestamp.
5. Click "Run" in the menu and select "Run Cell" to execute the code.

Step 5: Analyze the transformed data

To analyze the song data that was prepared in the previous step, you can add queries to the processing pipeline.

To do this, click on the New icon in the sidebar and select Notebook. In the Create Notebook dialog, enter a name for the notebook, for example, Analyze songs data. Select SQL as the default language and choose the cluster you created or an existing cluster. Then click Create.

In the first cell of the notebook, enter the following code.

Next, add a new cell by clicking on the Down caret in the cell actions menu and selecting Add Cell Below. Then, enter the following code in the new cell.

Step 6: Create a Databricks job to run the pipeline

To automate the process of running the data pipeline, you can create a workflow using a Databricks job. Here are the steps to do that:

1. In your Data Science & Engineering workspace, click Jobs in the sidebar, and then click Create Job.
2. Add a name for your job, for example, "Songs workflow".
3. Create the first task named "Ingest_data" by selecting Notebook as the task type and choosing the data ingestion notebook from your workspace.
4. Select a cluster to run the task on.
5. Save the task and add two more tasks named "Prepare_data" and "Analyze_data" by following the same process.
6. Once all three tasks are added, click the Run Now button to execute the workflow.
7. To view details of the task runs, click on the task in the job runs view.

Step 7: Schedule the data pipeline job

To schedule the job to run on a regular basis, follow these steps:

1. Click Jobs in the sidebar.
2. Click on the name of the job you just created to open the Job details panel.
3. Click Edit schedule.
4. Select Scheduled as the Schedule Type.
5. Specify the frequency, start time, and time zone for the job to run.
6. Optionally, use the Show Cron Syntax checkbox to display and edit the schedule in Quartz Cron Syntax.
7. Click Save to set the schedule for the job.

Conclusion

In conclusion, we have discussed the process of building a data pipeline on Databricks. By following the steps outlined in this article, you can easily ingest, prepare, and analyze data using Databricks. With its powerful processing capabilities, Delta Lake storage layer, and SQL and Python notebooks, Databricks provides a robust platform for building data pipelines.

However, building a data pipeline is only the first step in leveraging data for meaningful insights. To fully realize the value of data, it is important to continuously monitor and refine the pipeline to ensure that it is delivering accurate and relevant data. With the Databricks platform, you can easily monitor and optimize your data pipeline to ensure that it is meeting your business needs.

By leveraging the power of Databricks, you can build a scalable and efficient data pipeline that can drive insights and innovation for your organization. Whether you are a data analyst, data scientist, or business leader, Databricks provides the tools you need to succeed in a data-driven world. So start building your data pipeline today and unlock the power of your data!