Microsoft Azure Databricks Service
Tags: webdev, programming, devops, cloud
Published: 2023-04-03 18:54:21

## Introduction:
For developing, deploying, sharing, and managing enterprise-grade data solutions at scale, Azure Databricks offers a unified set of tools. The Azure Databricks Lakehouse Platform manages and deploys cloud infrastructure on your behalf and connects with cloud storage and security in your cloud account.
## What purposes serve Azure Databricks?
Businesses use Azure Databricks to process, store, clean, share, analyze, model, and monetize their datasets with solutions ranging from business intelligence to machine learning. With the Azure Databricks platform, you can create and distribute data engineering workflows, machine learning models, analytics dashboards, and more.
Most data activities have a uniform interface and toolset in the Azure Databricks workspace, including:
Workflow management and scheduling for data processing.
1. Working in SQL
2. Generating dashboards and visualizations
3. Data ingestion
4. Managing security, governance, and HA/DR
5. Data discovery, annotation, and exploration
6. Compute management
7. Machine learning (ML) modeling and tracking
8. ML model serving
9. Source control with Git

You can connect with Azure Databricks programmatically using the following tools in addition to the workspace user interface:

1. REST API
2. CLI
3. Terraform

## The managed blending of open source
At Databricks, there are strong ties to the open-source community. Databricks updates the open-source integrations in the Databricks Runtime versions. The open-source projects were founded by Databricks personnel.
1. Delta Lake
2. Delta Sharing
3. MLflow
4. Apache Spark and Structured Streaming
5. Redash

Azure Databricks maintains several proprietary tools that integrate and expand these technologies in order to improve performance and usability, such as the following:

1. Workflows
2. Unity Catalog
3. Delta Live Tables
4. Databricks SQL
5. Photon

## How does Azure integrate with Azure Databricks?
There are two main components to the Azure Databricks platform architecture:
the setting up, setting up, and administration of the platform and services by Azure Databricks.
The management of the customer-owned infrastructure is done in collaboration with your company and Azure Databricks.
Unlike many enterprise data companies, Azure Databricks does not need you to migrate your data into exclusive storage systems to use the platform. Instead, you configure an Azure Databricks workspace, set up a secure integration between the Azure Databricks platform and your cloud account, and Azure Databricks then deploys compute clusters using cloud resources in your account to process and store data in integrated services you control, like object storage.
This relationship is strengthened by Unity Catalog, which enables you to control data access permissions from within Azure Databricks using familiar SQL syntax.
The networking and security features of Azure Databricks workspaces have satisfied some of the largest and most security-conscious businesses in the world. With Azure Databricks, new users can get started with ease. It eliminates many of the challenges and concerns related to working with cloud infrastructure without restricting the modifications and controlling experienced data, operations, and security team’s demands.

## Create a corporate data lakehouse.

The data lakehouse combines the advantages of enterprise data warehouses and data lakes to expedite, clarify, and unify enterprise data solutions. It is also simpler to construct, manage, and synchronize many remote data systems because data engineers, data scientists, analysts, and production systems can all access consistent data fast through the data lakehouse.

## Data science, AI, and machine learning

Only two of the tools that Azure Databricks machine learning adds to the platform's basic functionality to better meet the demands of data scientists and ML developers are MLflow and the Databricks Runtime for Machine Learning. See the Introduction to Databricks Machine Learning.

## Analytics, warehousing, and BI

Azure Databricks offers a robust platform for executing analytical queries by fusing user-friendly user interfaces with cost-effective compute resources and endlessly scalable, economical storage. End users can run queries on scalable compute clusters without being concerned about any of the challenges of working in the cloud because administrators have configured them as SQL warehouses. SQL users can use the SQL query editor or notebooks to conduct queries against the data in the lakehouse. In addition to SQL, Python, R, and Scala are supported by notebooks, which also let users include links, photos, and markdown-based commentary along with the same visualizations seen in dashboards.

## Data management and safe data sharing

Unity Catalog provides a standardized data governance system for the data lakehouse. When cloud administrators create and integrate Unity Catalog's coarse access control permissions, Azure Databricks administrators can manage rights for groups and individuals. Access control lists (ACLs), which may be maintained with either user-friendly UIs or SQL syntax, allow database managers to more easily secure access to data without having to scale on cloud-native identity access management (IAM) and networking.

## DevOps, CI/CD, and task orchestration
ETL pipelines, analytics dashboards, and ML model development lifecycles all present challenges. Thanks to Azure Databricks, all your users can access a single data source, reducing duplication of effort and out-of-sync reporting. By offering a set of common tools for versioning, automation, scheduling, code deployment, and production resources, you can reduce your overhead for monitoring, orchestration, and operations. SQL queries, arbitrary code, and Azure Databricks notebooks are all scheduled by workflows.