Now that everything and everyone have moved to the cloud, it’s a little bit difficult to distinguish capabilities between tools that can boost your performance as a data leader, analyst, scientist, or engineer!

In this overview, we will be highlighting one of Microsoft’s Azure tools, that is Azure Databricks, so you can determine what parts of the platform might make sense to add to your organization’s data stack.

What is Azure Databricks?

Azure Dtatbricks is an Apache Spark-based analytics platform and was built on top of the one and only Microsoft Azure.

Azure Databricks is used mainly to process large workloads of data that allows collaboration between data scientists, data engineers, and business analysts to drive actionable insights with a one-click setup, streamlined workflows, and an interactive workspace.

Why use Azure Databricks?

There are four main reasons why you should consider Azure Databricks and why it’s a great analytics tool for big data workloads:

• Azure Databricks makes big data collaboration and integration a lot easier! With native integration, useful data analysis, and storage tools on the Microsoft Cloud Platform.
• Since it’s based on Apache spark, you can leverage its features, therefore, it’s fast and optimized for maximum performance.
• The system is predesigned since it’s being fully managed by Azure, and there is no need for maintenance. You can also easily scale up and down along with the “drag and drop” interface.
• The next level of security makes it the safest big data analytics platform that uses the enterprise-grade compliance and security that is available on the Azure platform.

Databrick components

Collaborative Workspace

This is a notebook based environment that has the following features:

• Code collaboratively in real-time!
• SQL, Python, Scala, and R support.
• Built-in version control and integration with Git/GitHub.
• Visualized queries
• Enterprise level security
• You can create and schedule ETL/Data Science Workloads from various data sources to be run as jobs.
• Tracking and managing the machine learning lifecycle from development to production.

Managed infrastructure

This is one of the main properties of Azure Databricks, and it takes the form of managed clusters.

Now, what’s a Cluster exactly? In simple words, it’s a group of virtual machines that divide up the work of a query in order to return results faster.

All you have to do is fill out 5-10 fields and then click a button! And now you can spin up a Spark cluster that is optimized beyond the open-source Spark, include many common data science and data analytical libraries, and auto-scale to meet the needs of the workloads.

Spark

Spark is the core here, and to put it into simple words, it’s an open-source distributed processing engine that processes data in memory, and that’s exactly what makes it a very popular asset for big data processing and machine learning.

Workloads and queries are executed by Spark on the Databricks platform.

Delta

This is an open-source file format that was specially built to deal with the limitations of traditional data lake file formats.

Delta is composed of Parquet, a columnar format optimized for big data workloads with added metadata and transaction tags.

How can you make use of it? Well, Delta offers the following key features that might be limitations in other file formats such as Parquet and ORC:

• ACID Transactions
• Ability to perform upserts
• Indexing for faster queries
• Unifies streaming and batch workloads
• Schema validation and expectations

Read more: What Is Azure Data Lake?

ML Flow

This too is open source and we can define it by saying: it’s a machine learning framework that was built to manage ML lifecycle.

In data science, it can be very challenging to get machine learning into production! And ML Flow addresses the challenges with the following features:

• Projects - Packaging format for reproducible runs on any computing platform
• Models - General model format that standardizes deployment options
• Tracking - Recore and query experiments
• Model Registery - Centralized and collaborative model lifecycle management

In addition to those components, you’ll be able to use the additional benefits on the Databricks platform:

1. Workspaces
2. Jobs
3. Big Data Snapshots
4. Security for the entire ML lifecycle
5. Quick deployment of ML models to a rest endpoint for testing

SQL analytics

Designed to give SQL analysts a home within Databricks.

By switching views in the traditional Databricks workspace, the SQL Analytics Workspace gives an experience similar to the traditional SQL workbench.

As a user, with SQL analytics you can:

• Write SQL queries against the data lake
• visualize queries inline
• build dashboards and share with the business
• create alerts based on SQL queries

This feature is powered by SQL Endpoints, which are Spark clusters for SQL workloads.

When to use Databricks?

Modernize Data Lake

If you’re working with Data Lake and you feel like it’s turning into a swamp and now you’re facing challenges around performance and reliability, then it might be beneficial to use Databricks to modernize your Data Lake.

Production Machine Learning

If you’re a data scientist, then Databricks will help you get work from Development to Production into the hands of business users.

Big Data ETL

If you’re thinking about performance and how much it’s going to cost, then Databricks is the most cost-effective solution for you.

Opening Data Lake for BI users

No need to build pipelines every time you want to access new data. You can open Data Lake to BI users through a tool like SQL Analytics within Databricks.

Curious about the solution?

Ctelecoms is a proud Microsoft partner in Saudi Arabia, working to deliver the best-in-class solutions to clients especially those interested in Azure.

Get in touch with our team for full support regarding deployment at: https://www.ctelecoms.com.sa/en/Form15/Contact-Us