Azure Databricks

Azure Databricks is a fully managed Apache Spark based analytics platform optimized for Azure. It provides a unified environment for data engineering, d...

• What is Azure Databricks, and how does it integrate with the Azure ecosystem?

  Azure Databricks is a fully managed Apache Spark based analytics platform optimized for Azure. It provides a unified environment for data engineering, data science, and analytics. Key integrations include: Azure Active Directory (AAD) : Secure authentication and RBAC Azure Data Lake Storage (ADLS) Gen2 and Blob Storage : Scalable data storage Azure Synapse Analytics : Advanced analytics and data warehousing Azure Machine Learning : End to end ML model lifecycle

• Can you explain the architecture of Azure Databricks?

  Azure Databricks architecture consists of two planes: Control Plane (managed by Databricks): Hosts the Workspace , notebook server, job scheduler, and cluster manager Data Plane (in your Azure subscription): Runs the Spark clusters within your VNet, processing data against your storage accounts DBFS : An abstraction over Azure Blob Storage providing a distributed file system interface Jobs : Automated workflows for running notebooks, JARs, or Python scripts on a schedule or trigger

• How does Azure Databricks handle data security and compliance?

  Azure Databricks ensures security and compliance through multiple layers: Authentication : Integration with Azure Active Directory for SSO and RBAC Encryption : Data encrypted at rest (via Azure managed or customer managed keys) and in transit via TLS Network isolation : VNet injection deploys clusters into your private virtual network with NSG controls Compliance : Certified for GDPR , HIPAA , SOC 2 , and other industry standards

• What are the different cluster modes available in Azure Databricks, and when would you use each?

  Azure Databricks offers three cluster modes: Standard Mode : General purpose for most ETL and analytics workloads, supporting a single user or automated jobs High Concurrency Mode : Optimized for multiple concurrent users sharing one cluster with fine grained resource sharing and table access control Single Node Mode : Runs driver and executor on one VM; ideal for lightweight development, testing, or small data ML experimentation

• How can you optimize the performance of a Spark job in Azure Databricks?

  To optimize Spark job performance in Azure Databricks : Partition data effectively to enable parallel processing and reduce shuffle overhead Cache intermediate DataFrames with .persist() or .cache() for reuse across multiple actions Right size clusters by selecting appropriate VM types (memory optimized vs. compute optimized) and worker counts Use broadcast variables for small lookup tables to avoid expensive shuffle joins:

• What is Delta Lake, and how does it enhance data reliability in Azure Databricks?

  Delta Lake is an open source storage layer that brings reliability to data lakes. In Azure Databricks , it provides: ACID transactions : Ensures data consistency during concurrent reads and writes Schema enforcement and evolution : Prevents bad data from entering your tables and supports adding new columns with mergeSchema Time travel : Query previous versions of data using VERSION AS OF or TIMESTAMP AS OF for auditing and rollback Optimized performance : Supports Z ordering , data compaction (

• How do you handle version control and collaboration in Azure Databricks?

  Azure Databricks supports version control and collaboration through: Databricks Repos : Native Git integration with GitHub , Azure DevOps , GitLab , and Bitbucket for branching, committing, and pull requests Collaborative notebooks : Real time co authoring with inline comments and revision history Workspace permissions : Granular access controls (Can Read, Can Run, Can Edit, Can Manage) on notebooks, folders, and clusters

• Can you explain the concept of Databricks Jobs and how they are used for automation?

  Databricks Jobs enable scheduling and orchestration of automated workflows: Task types : Run notebooks, Python scripts, JAR files, SQL queries, or Delta Live Tables pipelines Multi task workflows : Chain multiple tasks with dependencies in a DAG using the Jobs Orchestrator Parameterized runs : Pass dynamic parameters via widgets or job configurations for flexible execution Scheduling and triggers : Run on cron schedules, file arrival triggers, or via REST API calls