Python is the most powerful and simple programming language for performing several data-related tasks, including Data Cleaning, Data Processing, Data Analysis, Machine Learning, and Application Deployment. Databricks offers developers a choice of preferable programming languages such as Python, making the platform more user-friendly. By using Databricks Python, developers can effectively unify their entire Data Science workflows to build data-driven products or services.
In this article, you will learn how to execute Python queries in Databricks, followed by Data Preparation and Data Visualization techniques to help you analyze data in Databricks.
Table of Contents
- A basic understanding of the Python programming language.
- Working knowledge of Databricks.
What is Python?
Python is a high-level Object-oriented Programming Language that helps perform various tasks like Web development, Machine Learning, Artificial Intelligence, and more. It was created in the early 90s by Guido van Rossum, a Dutch computer programmer. Python has become a powerful and prominent computer language globally because of its versatility, reliability, ease of learning, and beginner friendliness.
It is an Open-source platform that supports modules, packages, and libraries that encourage code reuse and eliminate the need for writing code from scratch. The applications of Python can be found in all aspects of technologies like Developing Websites, Automating tasks, Data Analysis, Decision Making, Machine Learning, and much more. Today, Python is the most prevalent language in the Data Science domain for people of all ages.
What is Databricks?
Databricks is a centralized platform for processing Big Data workloads that helps in Data Engineering and Data Science applications. It allows a developer to code in multiple languages within a single workspace. Databricks is becoming popular in the Big Data world as it provides efficient integration support with third-party solutions like AWS, Azure, Tableau, Power BI, Snowflake, etc. It also serves as a collaborative platform for Data Professionals to share Workspaces, Notebooks, and Dashboards, promoting collaboration and boosting productivity.
What are the Key Features of Databricks?
Databricks have many features that differentiate them from other data service platforms. Some of the best features are:
1) End-to-End Machine Learning
At the initial stage of any data processing pipeline, professionals clean or pre-process a plethora of Unstructured Data to make it ready for the process of analytics and model development. Databricks help you in reading and collecting a colossal amount of unorganized data from multiple sources. Further, you can perform other ETL (Extract Transform and Load) tasks like transforming and storing to generate insights or perform Machine Learning techniques to make superior products and services.
Databricks integrates with various tools and IDEs to make the process of Data Pipelining more organized. It can integrate with data storage platforms like Azure Data Lake Storage, Google BigQuery Cloud Storage, Snowflake, etc., to fetch data in the form of CSV, XML, JSON format and load it into the Databricks workspace. In addition, it lets developers run notebooks in different programming languages by integrating Databricks with various IDEs like PyCharm, DataGrip, IntelliJ, Visual Studio Code, etc.
3) Data Lakehouse
Databricks offers a centralized data management repository that combines the features of the Data Lake and Data Warehouse. Merging them into a single system makes the data teams productive and efficient in performing data-related tasks as they can make use of quality data from a single source.
4) Delta Lake
Delta lake is an open format storage layer that runs on top of a data lake and is fully compatible with Apache Spark APIs. It ensures scalable metadata handling, efficient ACID transaction, and batch data processing. The ACID property of Delta Lake makes it most reliable since it guarantees data atomicity, data consistency, data isolation, and data durability.
Using PySpark for Databricks Python
Databricks is the platform built on top of Apache Spark, which is an Open-source Framework used for querying, analyzing, and fast processing big data. By amalgamating Databricks with Apache Spark, developers are offered a unified platform for integrating various data sources, shaping unstructured data into structured data, generating insights, and acquiring data-driven decisions.
To further allow data professionals to seamlessly execute Python code for these data operations at an unprecedented scale, Databricks supports PySpark, which is the Python API written to support Apache Spark. In other words, PySpark is a combination of Python and Apache Spark to perform Big Data computations.
There are no pre-requirements for installing any IDEs for code execution since Databricks Python workspace readily comes with clusters and notebooks to get started. Databricks community version allows users to freely use PySpark with Databricks Python which comes with 6GB cluster support. However, you need to upgrade to access the advanced features for the Cloud platforms like Azure, AWS, and GCP.
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Its completely automated Data Pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.
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- Connectors: Hevo supports 100+ Integrations to SaaS platforms, Files, Databases, BI tools, and Native REST API & Webhooks Connectors. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; Databricks; MySQL, SQL Server, TokuDB, DynamoDB, PostgreSQL Databases to name a few.
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Executing Python with Databricks
To get started with Databricks Python, here’s the guide that you can follow:
1) Databricks Python: Creating a Cluster
Clusters should be created for executing any tasks related to Data Analytics and Machine Learning. With Databricks, Cluster creation is straightforward and can be done within the workspace itself:
- Click the New Cluster option on the home page or click on the Create (plus symbol) in the sidebar.
- Now, select the Cluster option from the displayed menu. The Cluster Creation page appears.
- Name the Cluster and click on Create Cluster.
- Another way to create a Cluster is by using the Cluster UI button on the sidebar of the workspace.
- Now, the Cluster is ready for further execution.
2) Databricks Python: Creating a Notebook
- Once the Cluster is created, users can create a new Notebook where the code is executed.
- For creating a Notebook, click on the Create (plus symbol) in the sidebar, and from the displayed menu, select the New Notebook option.
- Name the Notebook and choose the language of preference like Python, SQL, R, etc. In this case, you can select Python.
- Finally, choose the Clusters where the created Notebook is to be attached.
3) Databricks Python: Data Collection
Data collection is the process of uploading or making the dataset ready for further executions. Users can upload the readily available dataset from their file explorer to the Databricks workspace. For uploading Databricks to the DBFS database file system:
- Click on the Data UI button in the sidebar.
- A popup tab will be displayed.
- Click on the Upload button in the top bar.
- In the Upload Data to the DBFS dialogue box, select a target directory where the dataset is to be stored.
- Then in the file section, drag and drop the local file or use the Browse option to locate files from your file Explorer.
After uploading the dataset, click on Create table with UI option to view the Dataset in the form of tables with their respective data types. You can make changes to the Dataset from here as well.
4) Databricks Python: Accessing the Data
To perform further Data Analysis, here you will use the Iris Dataset, which is in table format. For performing data operations using Python, the data should be in Dataframe format. For converting the Dataset from the tabular format into Dataframe format, we use SQL query to read the data and assign it to the Dataframe variable.
For further code executions,
- Import the necessary libraries in the Notebook:
from pyspark.sql import SQLContext
- To read and assign Iris data to the Dataframe,
df = sqlContext.sql(“SELECT * FROM iris_data”)
- df is the name assigned to the Dataframe.
- sqlContext.sql is the method/function.
- Select, from are the SQL operations.
- Iris_data is the uploaded dataset.
To run this code, the shortcuts are Shift + Enter (or) Ctrl + Enter.
Now the tabular data is converted into the Dataframe form.
- For viewing all the columns of the Dataframe, enter the command df.columns:
After executing the above command, all the columns present in the Dataset are displayed.
- To display the total number of rows in the data frame, enter the command df.count():
The above command shows there are 150 rows in the Iris Dataset.
- For viewing the first 5 rows of a dataframe, execute display(df.limit(5)):
Similarly display(df.limit(10)) displays the first 10 rows of a dataframe.
5) Databricks Python: Data Visualization
Databricks Notebooks allow developers to visualize data in different charts like pie charts, bar charts, scatter plots, etc.
- For visualizing the entire Dataframe, execute display(df):
In the above output, there is a dropdown button at the bottom, which has different kinds of data representation plots and methods.
- From these given plots, users can select any kind of chart to make visualizations look better and rich. To customize the Charts according to the user’s needs, click on the Plot options button, which gives various options to configure the charts.
Using the PySpark library for executing Databricks Python commands makes the implementation simpler and straightforward for users because of the fully hosted development environment.
In this article, you have learned the basic implementation of codes using Python. Databricks serves as the best hosting and development platform for executing intensive tasks like Machine Learning, Deep Learning, and Application Deployment.
You would require to devote a section of your Engineering Bandwidth to Integrate, Clean, Transform and Load your data into your Data lake like Databricks, Data Warehouse, or a destination of your choice for further Business analysis. This can be effortlessly automated by a Cloud-Based ETL Tool like Hevo Data.
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