Introduction to Azure Databricks — Big Data Analytics Powered by Apache Spark

Introduction to Azure Databricks

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Title slide by James Serra, Big Data Evangelist at Microsoft

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About Me

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James Serra bio — Microsoft Big Data Evangelist, 30 years IT experience, MCSE certifications, PASS presenter, former SQL Server MVP, author

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Agenda

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Topics: Big Data Architectures, Why data lakes, Top-down vs Bottom-up, Data lake defined, Hadoop as data lake, Modern Data Warehouse, Federated Querying, Solution in the cloud, SMP vs MPP

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The Modern Data Estate

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Hybrid architecture: on-premises (building icon) and cloud (cloud icon) both supporting operational databases, data warehouses, and data lakes. Data types: LOB, CRM, Graph, Image, Social, IoT

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• •Six data type icons
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The Microsoft Offering

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SQL Server (on-prem) ↔ Hybrid ↔ Azure Data Services. SQL Server: industry leader, #1 TPC-H, T-SQL over any data. Azure: 70% faster than Aurora, 2x global reach vs Redshift, No Limits Analytics 99.9% SLA

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Big Data & Advanced Analytics in Azure

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Section divider for the Azure big data chapter

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Knowing the Various Big Data Solutions

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Spectrum from Control to Ease of Use: Azure Marketplace (IaaS, any Hadoop) → Azure HDInsight (managed clusters) → Azure Databricks (frictionless Spark) → Azure Data Lake Analytics (job-as-a-service). Storage: Azure Data Lake Store and Azure Storage

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• •Control ↔ Ease of Use spectrum
• •Storage layer at bottom

Big Data & Advanced Analytics at a Glance

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End-to-end pipeline: Ingest (Data Factory, Kafka, Event Hub/IoT Hub) → Store (Blobs, Data Lake) → Prep & Train (Databricks, HDInsight, ML) → Model & Serve (Cosmos DB, SQL Database, SQL DW, Analysis Services) → Intelligence (Predictive apps, Reports, Dashboards)

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Five-phase horizontal pipeline with Azure service icons

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• •Azure service icons
• •Dashed flow arrows
• •Three data source types: Business apps, Custom apps, Sensors

Azure Databricks Powered by Apache Spark

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Section divider for the Azure Databricks deep dive

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Why Spark?

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Open-source engine built for speed, ease of use, sophisticated analytics. 100x faster than Hadoop in-memory. Largest OSS project with 1000+ contributors. Extensible: Scala, Java, Python, Spark SQL, GraphX, Streaming, MLlib

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What is Azure Databricks?

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Fast, easy, collaborative Spark-based analytics optimized for Azure. Best of Databricks + Best of Microsoft. Five key features: Apache Spark founders collaboration, one-click setup, interactive workspace, native Azure integration (Power BI, SQL DW, Cosmos DB), enterprise-grade security (AD, compliance, SLAs)

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• •Databricks logo + Microsoft logo
• •Five feature icons
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Apache Spark Architecture

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Unified framework: Spark SQL (Interactive Queries), Spark MLLib (Machine Learning), Spark Streaming (Stream Processing), GraphX (Graph Computation) — all on Spark Core Engine with Yarn, Mesos, or Standalone Scheduler

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• •Four blue module boxes
• •Spark Core Engine layer
• •Three scheduler options
• •Unifies: Batch, SQL, Real-time, ML, Deep Learning, Graph

Databricks Spark Is Fast

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Benchmark comparisons: 5x faster than vanilla Spark on AWS (11,674 vs 53,783 sec), 8x faster than Presto on AWS (35.3 vs 293 sec), 3x faster than on-premises Impala via Cloudera (1,149,264 vs 3,331,440 sec)

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• •Three benchmark bar charts
• •Red Databricks bars vs grey competitor bars
• •Source citation link

Advantages of a Unified Platform

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Single consistent API (RDDs), mix-and-match processing types, eliminates data movement between engines. Pipeline: Input Streams → Spark Streaming → Spark ML → Spark SQL → NoSQL DB

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• •Vertical blue pipeline flow diagram
• •Blue boxes for each Spark component

Differentiated experience on Azure

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Three pillars: Enhance Productivity (one-click launch, Power BI, collaboration, native Azure integration), Build on Most Compliant Cloud (AD security, fine-grained access, compliance), Scale Without Limits (massive scale, fastest Spark engine)

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Azure Databricks Platform Architecture

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Full platform diagram: Data sources (IoT, Cloud storage, Hadoop, Data warehouses) → Azure Databricks (Collaborative Workspace + Deploy Production Jobs & Workflows + Optimized Runtime Engine) → Outputs (ML models, BI tools, Data exports, Data warehouses)

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Three-layer platform diagram with inputs/outputs

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• •Three-tier Azure Databricks platform
• •Three persona icons
• •Spark logo in runtime layer
• •Input/output data source icons

Collaborative Workspace

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Get started in seconds (single click), interactive exploration (R, Python, Scala, SQL notebooks), real-time collaboration with revision history (GitHub, Bitbucket), built-in visualizations (matplotlib, ggplot, D3), PowerBI dashboards

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Feature descriptions on left, platform architecture diagram on right (workspace layer highlighted)

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• •Platform diagram with Collaborative Workspace highlighted
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Deploy Production Jobs & Workflows

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Jobs scheduler, notebook workflows (multi-stage pipelines), run notebooks as resilient Spark jobs, notifications and audit logs, native integration with Azure SQL DW, Cosmos DB, Data Lake Store, Blob Storage, Event Hub

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Feature descriptions on left, platform diagram on right (jobs layer highlighted)

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• •Platform diagram with Jobs & Workflows highlighted

Optimized Databricks Runtime Engine

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DBIO module for optimized I/O performance, fully-managed platform on Azure removes complexity, serverless and elastic cloud service, operate at massive scale globally

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Feature descriptions on left, platform diagram on right (runtime layer highlighted)

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• •Platform diagram with Runtime Engine highlighted

Azure Databricks Core Artifacts

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Five core components: Clusters, Libraries, Workspaces, Jobs, Notebooks — all connected to central Azure Databricks hub

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Hub-and-spoke diagram with Azure Databricks center and five blue boxes

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General Spark Cluster Architecture

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Driver Program (SparkContext) → Cluster Manager → Worker Nodes (Cache + Task) → Data Sources (HDFS, SQL, NoSQL). Driver runs main function, worker nodes read/write data, cache as RDDs, execute on VMs in public clouds

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Hierarchical architecture diagram on right, bullet points on left

मुख्य दृश्य तत्व

• •Driver → Manager → Workers hierarchy
• •Cache and Task boxes in worker nodes
• •Data Sources layer at bottom

Azure Databricks Integration with AAD

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Azure Active Directory integration: no separate user management, AAD users work directly in Databricks, delegated SSO authentication, AAD-based access control for notebooks, clusters, jobs, and data

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Bullet list on left, AAD authentication flow diagram on right

मुख्य दृश्य तत्व

• •Azure Databricks → Access Control → Azure AD → Authentication flow
• •Three user persona icons
• •Azure AD diamond logo

Clusters: Auto Scaling and Auto Termination

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Autoscaling (min/max workers, automatic scale on load) and Auto Termination (idle timeout, auto shutdown). Benefits: no guessing node count, no manual tweaking, no resource waste, pay only when used

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Description text on left, Azure Portal Create Cluster screenshot on right

मुख्य दृश्य तत्व

• •Azure Portal cluster creation UI
• •Autoscaling and Auto Termination settings highlighted in red

Jobs

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Jobs submit Spark application code for execution on clusters. Execute Notebooks or JARs. Comprehensive GUI tools for creation, management, and monitoring

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Workspaces

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Workspaces organize and share Notebooks, Libraries, and Dashboards. Hierarchical folder structure, private directories per user, fine-grained access control for secure collaboration

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Bullet list on left, two Azure Portal workspace screenshots on right

मुख्य दृश्य तत्व

• •Workspace folder browser screenshots
• •Import/Export/Permissions menu

Azure Databricks Notebooks Overview

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Notebooks for authoring and running Spark applications directly on clusters. Support fine-grained permissions, ideal for prototyping and iterative development. Consist of code, data, visualizations, comments, and notes

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Bullet list on left, notebook screenshot with chart on right

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• •Notebook with bar chart visualization
• •Population vs Price chart
• •Comment thread sidebar

Libraries Overview

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Libraries hold Python, R, Java/Scala libraries within workspaces. Immutable after import. Customizable via Init Scripts. Manageable via Library API. Supports PyPI, Maven, JAR, R CRAN sources

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Bullet list on left, three Azure Portal Create Library screenshots on right

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• •Three library creation UI screenshots
• •PyPI, JAR, Maven, R Library source options

Visualization

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Built-in visualization: Bar, Scatter, Map, Line, Area, Pie, Quantile, Histogram, Box plot, Q-Q plot, Pivot. All notebooks regardless of language support Databricks visualizations. Matplotlib renders as images. PySpark SQL code example with US state map visualization

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Bullet list on left, PySpark code + map visualization on right, plot type menu below

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• •US choropleth map visualization
• •PySpark code snippet
• •Plot type selection menu

Databricks File System (DBFS)

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Distributed file system layered over Azure Blob Storage. Mount Azure Storage buckets, cache locally on SSD, available in Python/Scala/CLI/dbutils, data persists after cluster termination, pre-installed on Spark clusters

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Bullet list on left, DBFS architecture diagram on right

मुख्य दृश्य तत्व

• •DBFS tree structure diagram
• •Python/Scala/CLI/dbutils access points
• •Azure Blob Storage at bottom
• •db.fs.mount() connectors

Spark SQL Overview

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Distributed SQL query engine for structured data. Query external databases, files, Hive tables. SQL or HiveQL. Bindings in Python, Scala, Java. Built-in structured streaming. Uses Catalyst optimizer and Tungsten execution

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Bullet list on left, Spark SQL architecture diagram on right

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• •Spark SQL layered architecture diagram
• •Data source connector logos: Parquet, JSON, Hive, JDBC, CSV, Cassandra, MongoDB, MySQL, etc.

Databases and Tables Overview

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Databases as collections of tables, defined via GUI or APIs/Notebooks. Databricks uses Hive metastore. Supports partitioned tables and partition pruning for performance

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Bullet list on left, Azure Portal databases/tables UI on right

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• •Database and table browser UI
• •Movies/ratings/users example tables

Spark Machine Learning (ML) Overview

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Parallelized ML algorithms: MMLSpark, Spark ML, Deep Learning, SparkR. Model selection via cross-validation. DataFrame-based API (Spark 2.0+). MLlib pre-installed. 3rd party: H2O, SciKit-learn, XGBoost

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Bullet list on left, Spark ML pipeline diagram on right

मुख्य दृश्य तत्व

• •Apache Spark ML logo
• •ML pipeline flow: Data Ingestion → Cleaning → Training → Model Selection → Validation → Deployment

Spark Structured Streaming Overview

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Unified streaming + batch API for exactly-once stateful stream processing. Runs on Spark SQL with DataFrame API. Incremental, continuous updates. Supports event-time windows, stream-to-batch joins, deduplication. Sources: Kafka, file (JSON, CSV, Parquet)

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Bullet list on left, two diagrams on right (unbounded table + incremental execution)

मुख्य दृश्य तत्व

• •Data stream → Unbounded Table diagram
• •Incremental execution flow with triggers

Apache Kafka for HDInsight Integration

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Structured Streaming integrates with Apache Kafka on HDInsight. Enterprise-grade streaming ingestion. No additional gateways needed. Kafka and Databricks clusters must be in same Azure Virtual Network

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मुख्य दृश्य तत्व

• •Kafka ↔ Spark Structured Streaming diagram
• •Azure Virtual Network boundary
• •Kafka and Spark logos

Spark GraphX Overview

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APIs for graph and graph-parallel computation. Unifies ETL, exploratory analysis, and iterative graph computation. Algorithms: PageRank, Connected Components, Label Propagation, SVD++, Triangle Count. Scala and RDD APIs only

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Three-panel layout: features, algorithms list, PageRank benchmark charts

मुख्य दृश्य तत्व

• •Algorithms list box
• •PageRank benchmark bar charts (Twitter + UK-Graph)
• •GraphX vs competing systems comparison

Databricks CLI

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Easy-to-use interface built on REST API. Two sub-CLIs: Workspace CLI and DBFS CLI. Implements DBFS API and Workspace API

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Hierarchy diagram: Databricks CLI → Workspace CLI + DBFS CLI

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• •Three colored boxes (dark blue, purple, green)
• •Hierarchical tree structure

Databricks REST API

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Seven APIs: Cluster API (create/edit/delete clusters), DBFS API, Groups API, Instance Profile API, Job API, Library API, Workspace API (import/export notebooks)

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• •Blue funnel icon
• •Seven-row API reference table

Use Cases

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Modern Big Data Warehouse

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Architecture: Unstructured data (logs, files, media) → Data Factory → Azure Storage → Azure Databricks (Spark) → Azure SQL Data Warehouse. Structured data (business apps) → Data Factory → Polybase → SQL DW → Analytical dashboards

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Five-phase pipeline with two data source streams

मुख्य दृश्य तत्व

• •Azure service icons
• •Dual-stream pipeline
• •Polybase connector

Advanced Analytics on Big Data

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Architecture: Unstructured → Data Factory → Azure Storage → Azure Databricks (Spark MLlib, SparkR, SparklyR) → Azure Cosmos DB → Web & mobile apps. Structured → Polybase → SQL DW → Analytical dashboards

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Five-phase pipeline with ML-focused processing and Cosmos DB serving

मुख्य दृश्य तत्व

• •Azure Cosmos DB for model serving
• •ML libraries listed
• •Web & mobile output

Real-time analytics on Big Data

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Architecture: Unstructured data → Azure HDInsight (Kafka) → Azure Databricks (Spark) ↔ Azure Storage → Polybase → Azure SQL Data Warehouse → Analytical dashboards

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Five-phase pipeline with Kafka streaming ingestion

मुख्य दृश्य तत्व

• •HDInsight Kafka for ingestion
• •Bidirectional Spark ↔ Storage
• •Real-time streaming focus

Pricing & Product Guidance

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Section divider for pricing and comparison chapter

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मुख्य दृश्य तत्व

• •Microsoft blue background

Big Data OSS - Comparison

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Three-column comparison: Azure HDInsight (Hadoop/HDP, PaaS, Ranger security, priced vs AWS EMR), Azure Databricks (Spark, SaaS, AD security, priced vs Databricks on AWS), 3rd Party Offerings (Cloudera/MapR/Hortonworks, IaaS, vendor pricing)

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Three-column comparison cards

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• •Three product cards with What/Pricing/Use When sections

Looking Across the Offerings

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Detailed comparison: Azure HDInsight (Hortonworks, big data engines, VNET, Ranger, OMS, Data Factory orchestration, 27 regions), Azure Databricks (Spark-first, single engine, AAD OAuth, RBAC, auto-scaling, serverless, SQL DW integration), Azure ML (first-party ML, Python/R, experimentation, model management, IDE integration)

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Three-column detailed feature comparison

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• •Three detailed product cards
• •What It Is, Features, Guidance sections

Demo

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Section divider for live demo

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मुख्य दृश्य तत्व

• •Microsoft blue background

Azure Databricks - service home page

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Azure Portal screenshot showing Azure Databricks (preview) service page in Marketplace > Data + Analytics. Unified analytics platform description, Databricks workspace UI preview

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Full-width Azure Portal screenshot

मुख्य दृश्य तत्व

• •Azure Marketplace navigation
• •Databricks service description
• •Workspace preview screenshot

Azure Databricks - creating a workspace

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Azure Portal: Create Azure Databricks Service form with workspace name, subscription, resource group, and location (West US) fields

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Full-width Azure Portal creation form screenshot

मुख्य दृश्य तत्व

• •Workspace creation form
• •Resource group selection
• •Location dropdown

Azure Databricks - workspace deployment

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Azure Portal Dashboard showing deployed resources including Databricks Service, with Quickstart tutorials for VMs, App Service, Functions, SQL Database

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Full-width Azure Dashboard screenshot

मुख्य दृश्य तत्व

• •Dashboard with resource list
• •Deploying Azure Databricks tile
• •Quickstart tutorial links

Azure Databricks - launching the workspace

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Azure Portal resource overview page with Launch Workspace button, managed resource group details, subscription info, and quick-start tiles: Documentation, Getting Started, Import Data, Notebook, Admin Guide

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Full-width resource detail page screenshot

मुख्य दृश्य तत्व

• •Launch Workspace button
• •Resource overview with URL
• •Six quick-start tiles

Azure Databricks - workspace home page

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Databricks workspace home page showing Featured Notebooks (Apache Spark Intro, Data Scientists, Structured Streaming), New items (Notebook, Job, Cluster, Table, Library), Documentation links, Open Recent

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Full-width Databricks workspace screenshot

मुख्य दृश्य तत्व

• •Databricks logo and sidebar
• •Featured Notebooks with Python/Spark icons
• •New item creation menu

How to get started

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Section divider for getting started guidance

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मुख्य दृश्य तत्व

• •Microsoft blue background

How to get started

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Three steps: Sign up for preview, Engage Microsoft experts for workshops, Learn more at azure.com/databricks

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Three icon-labeled steps on left, business meeting photo on right

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• •Three blue circle icons
• •Business meeting photo
• •URL links

Q & A

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Closing Q&A slide with contact info: James Serra, Big Data Evangelist — email, Twitter @JamesSerra, LinkedIn, blog at JamesSerra.com

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• •Dark teal background
• •Contact information links