Introduction
Hi, I am Destiny Erhabor at Microsoft Learn Student Ambassador I build and write about software development, blockchain, DevOps/cloud computing, and tech communities.
Destiny Erhabor - freeCodeCamp.org
Caesarsage (Destiny Erhabor) (github.com)
In today's fiercely competitive business landscape, startups and entrepreneurs are constantly seeking innovative ways to gain a competitive edge, unlock hidden opportunities, and drive rapid growth. One such game-changing innovation is the fusion of Azure Databricks and Apache Spark, a dynamic duo that empowers startups to transform raw data into actionable insights.
Prerequisites
- Have an active azure account and subscription. All Student get Azure for FREE at Azure for Students – Free Account Credit | Microsoft Azure and you can apply to become at Microsoft for Startups | Microsoft
- Familiar with the azure portal you can learn all about Azure for FREE at Browse all learning paths and modules - Training | Microsoft Learn
Embracing Entrepreneurial Innovation
In the fast-paced world of startups and entrepreneurial ventures, the ability to innovate and make informed decisions is the key to success. Every decision can be the difference between gaining a competitive edge or falling behind. That's where the power of data-driven decision-making comes into play.
Data as the Entrepreneur's Fuel: Data is no longer just a buzzword; it's the lifeblood of modern businesses, especially startups. It provides valuable insights into customer behavior, market trends, operational efficiency, and countless other aspects of your business. With data, you have the potential to identify opportunities, mitigate risks, and steer your startup toward success.
The Entrepreneur's Challenge: Entrepreneurs often face a unique set of challenges, from resource constraints to fierce competition. This makes the ability to make informed, data-driven decisions even more critical. But the question is, how can startups harness the power of data effectively without drowning in complexity and cost.
Purpose and Scope of the Guide
The purpose of this guide is to equip entrepreneurs and startup founders with the foundational knowledge and tools they need to leverage data as a strategic asset. We will explore the capabilities of Azure Databricks and Apache Spark, two dynamic technologies that offer a path to data-driven entrepreneurial success.
Why Azure Databricks and Apache Spark?
Data is the new currency of the digital age, and startups that harness its potential hold the key to unprecedented success. Azure Databricks and Apache Spark provide the rocket fuel that can propel your entrepreneurial journey to new heights. These technologies have gained prominence for their ability to simplify data processing and analysis, making them accessible to startups and entrepreneurs.
By the end of this guide, you will understand why Azure Databricks is an ideal choice for cloud-based data processing and how Apache Spark can be your secret weapon for turning data into actionable insights.
Scope of the Journey
Our journey will take us from understanding the prerequisites for getting started to mastering the art of Spark. We'll explore the setup of Azure Databricks, the creation of data clusters, and the execution of Spark jobs.
Now, let's embark on this entrepreneurial data-driven journey together.
Understanding Azure Databricks and Apache Spark
Note: This section provides a high-level introduction to Azure Databricks outlining their significance and key features.
Azure Databricks is a cloud-based analytics platform that combines the power of Apache Spark with a unified workspace for data engineering, data science, and machine learning. It is designed to simplify big data and advanced analytics tasks, making it easier for organizations to derive insights from their data. The following are some essential details concerning Azure Databricks:
- Unified Analytics Platform: Azure Databricks provides a single, collaborative workspace where data engineers, data scientists, and business analysts can work together on data projects.
- Apache Spark Integration: At its core, Azure Databricks is tightly integrated with Apache Spark, an open-source, distributed data processing framework. Spark enables parallel processing of data across a cluster of computers, making it suitable for handling large-scale data analytics workloads.
- Scalability and Performance: Azure Databricks offers scalable and high-performance computing clusters, allowing users to process and analyze large volumes of data efficiently.
- Productivity and Collaboration: The platform promotes productivity and collaboration through features like Databricks Notebooks, which support multiple programming languages (Scala, Python, R), interactive data exploration, and visualization.
- Managed Service: Azure Databricks is a fully managed service, which means that users don't need to worry about infrastructure provisioning, cluster management, or software updates. Microsoft Azure takes care of the underlying infrastructure, allowing users to focus on their data and analytics tasks.
- Integration with Azure Services: Azure Databricks seamlessly integrates with various Azure services, such as Azure Data Lake Storage, Azure SQL Data Warehouse, and Azure Machine Learning, making it part of a comprehensive Azure data ecosystem. For instance, you can train a machine learning model on a Databricks cluster and then deploy it using Azure Machine Learning Services.
Use cases and Industrial Applications
Azure Databricks is used across various industries and for a wide range of use cases. Some common applications include:
1.Data Analytics and Business Intelligence
- Use Case: Organizations can use Azure Databricks and Spark for data analytics and business intelligence, enabling them to process large datasets, run SQL queries, and create interactive dashboards.
- Industry Applications: This use case is valuable across industries for gaining insights into customer behavior, market trends, and operational performance.
2. Machine Learning and AI
- Use Case: Data scientists and machine learning engineers can leverage Spark MLlib to build and train machine learning models for tasks like classification, regression, recommendation systems, and natural language processing.
- Industry Applications: Industries such as e-commerce, finance, healthcare, and autonomous vehicles use Azure Databricks and Spark for predictive modeling, fraud detection, and personalized recommendations.
3. Real-time Stream Processing
- Use Case: Azure Databricks Streaming allows organizations to process real-time data streams, enabling applications like fraud detection, monitoring, and IoT data processing.
- Industry Applications: Financial institutions use stream processing for real-time trading analytics, while IoT companies monitor devices in real-time for predictive maintenance.
4. Data Warehousing and Data Lake
- Use Case: Azure Databricks can store and query large datasets efficiently using Delta Lake, providing reliability and ACID transactions.
- Industry Applications: Data warehousing and lake storage are critical for industries dealing with large volumes of structured and unstructured data, such as retail, healthcare, and energy.
5. Recommendation Systems
- Use Case: Organizations can build recommendation engines using collaborative filtering and machine learning techniques, providing personalized content and product recommendations.
- Industry Applications: E-commerce, media, and streaming platforms heavily rely on recommendation systems to improve user engagement and revenue.
6. Log and Event Analysis:
- Use Case: Analyzing logs and events helps organizations detect anomalies, troubleshoot issues, and optimize system performance.
- Industry Applications: IT operations, cybersecurity, and online services industries utilize log and event analysis for security monitoring and performance optimization.
7. Genomic Data Analysis
- Use Case: Genomic research benefits from Spark's distributed computing capabilities to analyze large genomic datasets, identify genetic variations, and accelerate drug discovery.
- Industry Applications: Healthcare and pharmaceutical companies use genomics analysis to advance personalized medicine and drug development.
8. IoT Data Processing
- Use Case: Azure Databricks processes and analyzes data from IoT devices, allowing organizations to gain insights from sensor data, improve product quality, and make real-time decisions.
Getting Started with Azure Databricks
Before you can run Spark, you need to spin up a compute cluster, and before you can spin up a compute cluster, you need to create a Databricks workspace. Now let's get started:
How to Create an Azure Databricks Workspace
Step by step guide to creating your azure data bricks workspace.
- Login to your Azure Portal
- Search for data bricks and select. This will take you to the azure data bricks resource, next click the create button at the top corner to create a new resource:
- After clicking the Create button, Complete the require field from the form displayed by selection your
- Subscription: Your azure subscription.
- Resource group: Existing or Create a new one.
- Workspace name (Here you can enter any name, it is not globally unique unlike other azure resources)
- Region: Select a region closer to you.
- Pricing Tier: The pricing tier is of three form Standard, Premium and Trial. Choose the one based on your requirement. In this article, you will be using the Trial which gives you premium access for 14 days.
- Leave other field as default and click Review + create.
How to access your Azure Databricks Workspace
After creating your workspace, you can access it from the Azure portal by clicking on the Go to resource button. This will take you to the Azure Databricks resource.
Next, click the Launch Workspace from the overview page to open the Azure Databricks workspace (This will open a new portal) where you can create clusters and notebooks.
What is a Databricks Cluster
A cluster is a set of computation resources and configurations on which you run data engineering, data science, and data analytics workloads. You can create one or more clusters in your workspace depending on your workload requirements.
How to Create a Cluster
To create a cluster, click on the New button on the left-hand side of the workspace and then click on the Cluster button.
This will open the Compute to Create Cluster dialog box, where you can specify the cluster details.
Cluster Settings
The following are some of the essential cluster settings:
- Cluster Name: The name of the cluster, which must be unique within the workspace. For instance, you can name the cluster "My Cluster" or naming that suits your business.
- Cluster Mode: The cluster mode determines how the cluster resources are used. The available options are Single and Multiple Node. The single mode is suitable for single-user workloads, while the High Concurrency mode is designed for multi-user workloads.
- Cluster Node Types: The cluster node types determine the hardware configuration for the driver and worker nodes. You can select the node types based on your workload requirements. For instance, if you want to run a memory-intensive workload, you can select a node type with more memory.
- Databricks Runtime Version: The Databricks Runtime is the runtime environment for the cluster, which includes Apache Spark and various other components. You can select the Databricks Runtime version based on your requirements. For instance, if you want to use Spark 3.0 with Scala 2.12, you can select the Databricks Runtime 7.3 (Scala 2.12, Spark 3.0.1) option.
- Terminate after: The terminate after option allows you to automatically terminate the cluster after a specified period of inactivity. For instance, you can set the terminate after option to 30 minutes to terminate the cluster after 30 minutes of inactivity.
- Cluster Tags: You can add tags to your cluster to organize and manage your resources. For instance, you can add a tag named "Environment" with a value of "Production" to indicate that the cluster is used for production workloads. This helps in managing your workflows.
- Click the Create Compute when done, you can also see the Summary at the right side of the screen.
- Once the cluster is created, you would see the different tabs available and on the notebook tab, you will see (0) attached notebooks. This is because you have not created any notebook yet.
- You will create one in the coming sections, let's talk about Apache Spark and what it brings to the table briefly.
Understanding Apache Spark
Apache Spark is an open-source distributed computing framework that provides a unified analytics engine for large-scale data processing. It is designed to be fast, easy to use, and general-purpose, making it suitable for a wide range of use cases.
Key Features
The following are some of the key features of Apache Spark:
- In-Memory Processing: Spark uses in-memory processing to speed up data processing, allowing it to process large datasets faster than traditional disk-based systems.
- Fault Tolerance: Spark provides fault tolerance by tracking the lineage of each RDD (Resilient Distributed Dataset), allowing it to recover from failures.
- Data Parallelism: Spark uses data parallelism to process data in parallel across a cluster of computers, making it suitable for handling large-scale data analytics workloads.
- Unified Analytics Engine: Spark provides a unified analytics engine for data engineering, data science, and machine learning, allowing users to perform various data tasks using a single framework.
Spark Core Components
Spark's core components include the following:
- Resilient Distributed Datasets (RDDs): RDDs are Spark's primary data abstraction, which represent a collection of elements partitioned across a cluster of computers. RDDs are immutable and fault-tolerant, allowing them to be recomputed in case of failures.
- DataFrames: DataFrames are Spark's primary data abstraction for structured data, which represent a distributed collection of rows with named columns. DataFrames are similar to tables in a relational database, making them suitable for data analysis and data processing tasks.
- Datasets: Datasets are Spark's primary data abstraction for structured data, which represent a distributed collection of typed objects. Datasets are similar to DataFrames, but they provide compile-time type safety, making them suitable for data science and machine learning tasks.
Working with Azure Databricks Notebooks
A notebook is a web-based interface that allows you to create and run code in a browser. You can use notebooks to write code in languages like Scala, Python, and R, and then execute the code interactively. Notebooks are a powerful tool for data exploration, visualization, and collaboration.
How to Create a Notebook
To create a Notebook, click on the New button on the left-hand side of the workspace and then click on the Notebook button. This will open the Create Notebook dialog box, where you can specify the notebook details.
Alternatively, you can, go through your Workspace by clicking the Workspace on the left pane, Select the user inside the User folder and Click Add to select Notebook.
Notebook Settings
The following are some of the essential notebook settings:
- Notebook Name: The name of the notebook, which must be unique within the workspace. For instance, you can name the notebook "My Notebook".
- Language: The language to use for the notebook. The available options are Scala, Python, SQL and R.
- Cluster: The cluster to use for the notebook. You can select an existing cluster or create a new cluster.
How to run a Notebook
To run a notebook, click on the Run button or if you are a window’s user like me you can also press the shift+Enter keys. This will run the notebook and display the results in the notebook.
- To check for the file system, you use %fs ls. The % sign followed by the fs is a magic command that allows us to interact with the file system since our default shell is SQL, similarly, to interact with python you will use %python <python-command>. The ls command is used to list the files in the file system. The output of the command is shown below:
- All the files in the file system are stored in the dbfs:/FileStore/tables directory. The dbfs stands for Databricks File System.
Analyzing Data: Azure Airline Datasets
Now, you could import your business data from external source for analysis here or even from azure resources such as azure storage, azure datalake, etc. But for this guide, you will be using some default datasets.
- Let's look at the data in the airline's dataset. To do this, you will use the head command. The head command is used to display the first few lines of a file. The output of the command is shown below:
%fs head /databricks-datasets/airlines/part-00000
- This dataset contains information about flights in the US from 1987 to 2008.
How to Load CSV into a Table
A Databricks table is just a Spark DataFrame if you’re familiar with Spark. You can also think of it as being like a table in a relational database. To load the csv file, run these commands:
- This returns ok when done. The DROP TABLE is to avoid duplication.
How Query the Data
Now that you have the data in a table, you can query it using SQL. To do this, run
SELECT * FROM airlines
How to Visualize the data.
- On the right side of the results screen, click the + button and select visualization to create a visualization of the data.
- This takes you to the virtualization page and you can select different virtualization the conditions that meet your need, here you used DaysOfWeek to FlightNum on a Line chart:
- Click save and you can see it in your notebook. You can add more Virtualizations.
Running Spark Jobs
A job is a unit of work that is submitted to a Spark cluster for execution. A job can be a single task or a series of tasks. For instance, a job can be a single SQL query or a series of SQL queries. It is one way of running an entire notebook. You can also run a notebook cell by cell. This is useful when you want to run a single cell or a series of cells.
How to run a Job from Notebook
- To run a job from your current notebook, click on the notebook name in the Workspace tab and then click on the Run All button at the Top. This will run the notebook and display the results in the notebook.
- Also, you can schedule a job to run at a specific time. To do this, click on the notebook name in the Workspace tab and then click on the Schedule button. This will open the Schedule Job dialog box, where you can specify the job details.
How Run a Job from the pane
When you are not on the notebook you want to run the job against, you can create a new Job by clicking the Job Runs under Data Engineering from the left side of the workspace.
- This view stores all your Jobs and can also monitor them here.
- At the top right corner, Click the Create jobs button to create the job.
Job Settings
The following are the required job settings:
- Task Name: The name of the job, which must be unique within the workspace. For instance, you can name the job "My Job".
- Source: The Task sources. The default is Notebook.
- Path: Select the Notebook path from your user workspace.
- Cluster: Here you can either create a new cluster for the job to run or use your existing one.
- When completed, Click on Create button.
- Here, you can either Run now or Schedule.
Executing Spark jobs by running the entire notebooks.
- Click the Run now.
Viewing results.
Click on the job run and it will display your result. As you can see below this is out notebook SQL script executed for use:
Congratulations!!!
In the world of startups and entrepreneurship, data-driven decisions are the catalyst for success. With this guide, you've taken the first step towards harnessing the potential of Databricks—a journey that empowers you to navigate your entrepreneurial path with precision and achieve data-driven excellence. Your success story awaits; let Databricks be your guiding light.
Check the further resources section for more tutorial on Azure Databricks.
Clean up
Delete the cluster.
To delete the cluster, click on the cluster name in the Clusters tab and then click on the Delete button. This will delete the cluster and remove it from the Clusters tab.
Delete the notebook.
To delete the notebook, click on the notebook name in the Workspace tab and then click on the Delete button. This will delete the notebook and remove it from the Workspace tab.
Delete the workspace.
To delete the workspace, click on the workspace name in the Azure portal and then click on the Delete button. This will delete the workspace and remove it from the Azure portal.
Delete azure resource groups.
To delete the resource group, click on the resource group name in the Azure portal and then click on the Delete button. This will delete the resource group and remove it from the Azure portal.
Conclusion
In this article, you have learned how to create a Databricks workspace, create a cluster, create a notebook, and run Spark jobs. You can explore more with this basic knowledge.
Further Resources
Explore Azure Databricks Module
Use Apache Spark On Azure Databricks
Azure Databricks Notebooks & Azure-Data-Factory Module
Data Engineer-Azure Databricks
Azure Databricks documentation | Microsoft Learn
Build and Operate-Machine Learning Solutions With Azure Databricks
