18 January 2026

#Databricks


Key Concepts


S.No Topic Sub-Topics
1DatabricksWhat is Databricks, Lakehouse concept, Databricks vs Hadoop, Use cases, Architecture overview
2Databricks WorkspaceWorkspace UI, Notebooks, Clusters, Jobs, Repos
3Databricks ArchitectureControl plane, Data plane, Workspace components, Security layers, Execution flow
4Clusters in DatabricksAll-purpose clusters, Job clusters, Autoscaling, Cluster policies, Init scripts
5Databricks RuntimeDBR versions, Photon engine, ML runtime, GPU runtime, Performance tuning
6NotebooksLanguages supported, Notebook workflows, Magic commands, Versioning, Collaboration
7Databricks Utilities (dbutils)File system ops, Secrets, Widgets, Notebook workflows, FS mounts
8Data IngestionBatch ingestion, Streaming ingestion, Auto Loader, File formats, Schema inference
9Delta Lake FundamentalsACID transactions, Delta log, Schema enforcement, Time travel, File compaction
10Delta Lake AdvancedOPTIMIZE, Z-ORDER, Vacuum, Delta constraints, Change Data Feed
11Spark SQL in DatabricksSQL editor, ANSI SQL, Views, CTEs, Query optimization
12DataFrames & DatasetsAPI overview, Transformations, Actions, Lazy evaluation, Performance tips
13Databricks SQL WarehousesServerless SQL, Query execution, Dashboards, Alerts, Access control
14Jobs & WorkflowsJob types, Task dependencies, Scheduling, Retries, Monitoring
15Databricks ReposGit integration, Branching, CI/CD basics, Repo permissions, Best practices
16Security & Access ControlUsers & groups, IAM integration, Table ACLs, Cluster policies, Secrets
17Unity CatalogMetastore, Catalogs & schemas, Data lineage, Fine-grained access, Auditing
18Streaming with DatabricksStructured Streaming, Triggers, Watermarking, Stateful ops, Fault tolerance
19Auto LoaderCloudFiles, Incremental ingestion, Schema evolution, Notifications, Performance tuning
20Databricks ML OverviewML workspace, ML runtime, Experiment tracking, Feature store, Model registry
21MLflow in DatabricksTracking, Projects, Models, Model registry, Deployment
22Feature StoreFeature tables, Offline features, Online features, Reusability, Governance
23Model TrainingDistributed training, Hyperparameter tuning, AutoML, GPUs, Evaluation metrics
24Model DeploymentBatch inference, Real-time serving, Model endpoints, A/B testing, Monitoring
25Performance OptimizationPartitioning, Caching, Broadcast joins, Skew handling, Photon usage
26Monitoring & LoggingSpark UI, Ganglia, Job metrics, Logs, Alerts
27Cost OptimizationCluster sizing, Spot instances, Autoscaling, Job clusters, Usage reports
28Databricks on CloudAWS architecture, Azure architecture, GCP basics, Networking, Storage integration
29CI/CD & DevOpsRepos + pipelines, Databricks CLI, Asset bundles, Environment promotion, Automation
30Real-world Use CasesETL pipelines, Streaming analytics, ML pipelines, Lakehouse design, Interview prep

Interview question

What is Databricks and what are its core components?
What are the key features of the Databricks Lakehouse Platform?
What is the Databricks Lakehouse architecture?
What is the difference between a data lake, data warehouse, and lakehouse?
What is Delta Lake and why is it important in Databricks?
What are the key features of Delta Lake?
What is Delta Lake ACID transaction support?
What is schema enforcement in Delta Lake?
What is schema evolution in Delta Lake?
What is the Delta Lake transaction log?
What is time travel in Delta Lake?
What are Delta Lake table versions?
What is the difference between managed and external tables in Databricks?
What are Unity Catalog and its main capabilities?
What is the three-level namespace in Unity Catalog?
What are catalogs, schemas, and tables in Unity Catalog?
What is data lineage in Unity Catalog?
What are Unity Catalog access control mechanisms?
What are external locations and storage credentials in Unity Catalog?
What are Delta Sharing and its use cases?
What are Databricks workspaces?
What are Databricks clusters and how do they work?
What are the different types of Databricks compute resources?
What are all-purpose clusters and job clusters?
What are serverless compute resources in Databricks?
What are cluster policies in Databricks?
What are Databricks Runtime versions?
What is Photon in Databricks?
What are Databricks SQL warehouses?
What are the differences between SQL warehouses and Spark clusters?
What is Apache Spark and how is it used by Databricks?
What are Spark DataFrames and how are they used in Databricks?
What are Spark RDDs and when would you use them?
What is lazy evaluation in Apache Spark?
What are Spark transformations and actions?
What is Spark Catalyst Optimizer?
What is Spark Adaptive Query Execution?
What are narrow and wide transformations in Spark?
What is data partitioning in Databricks?
What is data skew and how can it be handled in Databricks?
What is repartitioning and coalescing in Spark?
What is caching in Spark and when should it be used?
What are broadcast joins and when should they be used?
What are Databricks jobs and workflows?
What are job tasks and dependencies in Databricks Workflows?
What are job parameters and dynamic task values?
What are Databricks notebooks and how are they used?
What are notebook workflows and reusable notebooks?
What are Databricks Repos and how do they support Git integration?
What is Databricks Asset Bundles?
What is Databricks CLI and how is it used?
What are Databricks REST APIs and what can they automate?
What is Auto Loader in Databricks?
What are the advantages of Auto Loader over traditional file ingestion?
What are streaming checkpoints in Databricks?
What is Structured Streaming in Databricks?
What are streaming triggers in Spark Structured Streaming?
What is exactly-once processing in Databricks Structured Streaming?
What is the difference between batch processing and streaming processing?
What is Delta Live Tables and how does it support data pipelines?
What is Lakeflow Declarative Pipelines in Databricks?
What are expectations and data quality rules in Databricks pipelines?
What are medallion architecture layers in Databricks?
What are Bronze, Silver, and Gold tables?
What are common ETL and ELT patterns in Databricks?
What are incremental data processing strategies in Databricks?
What are Change Data Capture patterns in Databricks?
What is Delta Change Data Feed?
What are MERGE operations in Delta Lake?
What are OPTIMIZE and Z-ORDER in Databricks?
What is liquid clustering in Databricks?
What is VACUUM in Delta Lake?
What is data skipping in Databricks?
What are Delta Lake performance optimization techniques?
What are Databricks SQL query optimization techniques?
What is the difference between partitioning, Z-ORDER, and liquid clustering?
What is Databricks MLflow and how is it used for machine learning?
What is the Databricks Machine Learning platform?
What are MLflow experiments, runs, and models?
What is the Databricks Feature Store?
What is Model Serving in Databricks?
What is Mosaic AI in Databricks?
What is Databricks Vector Search?
What are vector indexes in Databricks Vector Search?
What is Retrieval-Augmented Generation and how can it be implemented in Databricks?
What is the role of Databricks in building RAG applications?
What are embeddings and how are they stored or searched in Databricks?
What is a vector database and how does Databricks support vector search?
What are AI Gateway capabilities in Databricks Model Serving?
What are foundation models in Databricks?
What is Model Serving for LLM applications in Databricks?
What is prompt engineering and how can it be integrated with Databricks AI workloads?
What are AI agents and how can they be developed on Databricks?
What is Agent Framework in Databricks?
How can Databricks support multi-agent AI architectures?
How can Unity Catalog govern AI models, features, and vector indexes?
How can Databricks be used to build an enterprise RAG pipeline?
How can Databricks be integrated with LangChain for AI applications?
How can Databricks be integrated with LangGraph for agentic AI workflows?
How can Databricks be used with MCP-based AI agents?
How can Databricks support real-time AI inference pipelines?
How can Databricks handle large-scale LLM training and fine-tuning workloads?
How can Databricks be used to monitor and evaluate production AI agents?
How can Databricks implement AI governance, security, and responsible AI practices?
How can Databricks be integrated with Azure OpenAI for enterprise AI applications?
How can Databricks be used to build an end-to-end Agentic AI data platform?
What are the best practices for designing a scalable Databricks platform for AI and data engineering?

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