Azure databricks

Azure Databricks is an easy, fast, and collaborative Apache spark-based data analytics platform for the Microsoft Azure cloud services platform. It accelerates innovation by bringing data science data engineering and business together. Making the process of data analytics more productive more secure more scalable and optimized for Azure.

This blog post covers Microsoft Azure Databricks, Apache spark, the Azure Databricks Architecture, technology & new capabilities available for data engineers using the power of Databricks on Azure, and Create a Databricks Instance and Cluster.

What Is Azure Databricks?
Databricks + Apache Spark + enterprise cloud = Azure Databricks
It is a fully-managed version of the open-source Apache Spark data analytics and it features optimized connectors to storage platforms for the quickest possible data access.
It offers a notebook-oriented Apache Spark as-a-service workspace environment which makes it easy to explore data interactively and manage clusters.
It is secure cloud-based machine learning and big data platform.
It is supporting multiple languages such as Scala, Python, R, Java, and SQL.

Also read: Azure SQL Database is evergreen, meaning it does not need to be patched or upgraded, and it has a solid track record of innovation and reliability for mission-critical workloads.

What is Apache Spark?
Spark is an integrated processing engine that can analyze big data using SQL, graph processing, machine learning, or real-time stream analysis.
Spark ML offers high class and finely tuned machine learning algorithms for handling big data.

Read: Azure Stream Analytics

Microsoft Azure Databricks Architecture & Diagram
When we launch a cluster via Databricks, a “Databricks appliance” is deployed as an Azure resource in our subscription.
Then we specify the types of VMs to use and how many, but Databricks handle all other elements.
A managed resource group is deployed into the subscription that we populate with a VNet, a storage account, and a security group.
Once these services are ready, we will control the Databricks cluster over the Databricks UI.

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What Is Azure Databricks Workspace?
Databricks Azure Workspace is an analytics platform based on Apache Spark.
For the big data pipeline, the data is ingested into Azure using Azure Data Factory.
This data lands in a data lake and for analytics, we use Databricks to read data from multiple data sources and turn it into breakthrough insights.

Read: Azure Data Lake Overview for Beginners

Azure Databricks Pricing
Pay as you go: Azure Databricks cost you for virtual machines (VMs) manage in clusters and Databricks Units (DBUs) depend on the VM instance selected.
A DBU is a unit of the processing facility, billed on per-second usage, and DBU consumption depends on the type and size of the instance running Databricks.

Why is Azure Databricks for Data Engineers?

1) Optimized Environment

Databricks Azure was optimized automatically from the ground up for cost-efficiency and performance in the cloud.
Auto-scaling and auto-termination of Spark clusters, no doubt it minimizes costs automatically.
Optimizations including indexing, caching, and advanced query optimization, which can enhance performance by as much as 10-100x over conventional Apache Spark deployments in the cloud.

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2) Persistent collaboration

Notebooks on Databricks are live and easy to share, with real-time teamwork.
Dashboards allow business users to call a current job with new parameters.
Databricks integrates closely with PowerBI for hand-on visualization.

3) Simple to use

Azure Databricks comes with notebooks that let you run machine learning algorithms, connect to common data sources, and learn the basics of Apache Spark to get started rapidly.
It also a unified debugging environment features to let you analyze the progress of your Spark jobs from under interactive notebooks, and powerful tools to examine past jobs.
No need to install common analytics libraries, such as the Python and R data science stacks, which are preinstalled.

Read :  The Architecture of Azure synapse

Create A Databricks Instance And Cluster

Note: To create a DataBricks Instance and Cluster, make sure that you have Azure subscription. If you don’t have one, create a free microsoft account before you begin.

1) Sign in to the Azure portal.

2) On the Azure portal home page, click on the + Create a resource icon.

3) On the New screen page, click in the Search the Marketplace text box, and type the word Databricks.

Read : Batch processing vs stream processing

4) Click Azure Databricks in the list that appears.

5) In the Databricks blade, click on Create.

Read: Azure Data Engineer.

6) On the Azure Databricks Service page, create an Azure Databricks Workspace with the following settings.

7) In the Azure Databricks Service blade, click on Create

 

Read: Microsoft Certified Azure Data Engineer Associate

8) Click on Go to resource, in the awdbwsstudxx screen, click on the button Launch Workspace.

9) Under Common Tasks, click New Cluster. In the Create Cluster screen, under New Cluster, create a Databricks Cluster with the
following settings.

Read: Azure Well-Architected Framework

Real-Time Use Cases of Azure Databricks
As mobile apps and other advances in technology continue to upgrade the way users choose and utilize information, recommendation engines are becoming an essential part of applications and software products.
Churn analysis also known as customer defection, customer attrition, or customer turnover, is the loss of clients or customers. Forecasting and restricting customer churn are vital to a range of businesses.
Intrusion detection is required to track network or system activities for malicious activities or policy violations and generate electronic reports to a management station.s