Databricks Platform Introduction — Data, Analytics and AI on One Platform

The Databricks Platform Introduction

सामग्री

Title slide — All your data, analytics and AI on one platform. By Alex Ivanichev, March 2022

लेआउट संरचना

Left-aligned title with author/date, geometric photo collage on right

मुख्य दृश्य तत्व

• •Databricks logo
• •Geometric shapes with team photos
• •Dark teal background
• •Coral/teal accent shapes

What is DataBricks?

सामग्री

DataBricks is a unified & open Data and Analytics Platform. Built on open-source: Apache Spark, Delta Lake, MLflow, and Koalas

लेआउट संरचना

Centered text on coral background with four OSS logos at bottom

मुख्य दृश्य तत्व

• •Apache Spark logo
• •Delta Lake logo
• •MLflow logo
• •Koalas logo

Agenda / Navigation

सामग्री

Visual navigation placeholder showing key sections of the presentation

लेआउट संरचना

Simple agenda layout

मुख्य दृश्य तत्व

• •Section indicators

How the data management looks like today?

सामग्री

Section divider introducing the current state of data management challenges

लेआउट संरचना

Centered white text on green background

मुख्य दृश्य तत्व

• •Green background
• •Question format title

Data management complexity

सामग्री

Three siloed stacks — Data Warehousing, Data Engineering, Data Science/ML — with disconnected systems and proprietary formats. Shows ETL flows between data warehouse, data lake, streaming, and ML pipelines

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Three-column header with architecture flow diagram below

मुख्य दृश्य तत्व

• •Three persona icons
• •ETL flow arrows
• •Tool logos: Redshift, Snowflake, BigQuery, Hadoop, Spark, TensorFlow, etc.

Modern Data Teams

सामग्री

Triangle diagram showing the three key roles in modern data teams: Data Analysts, Data Engineers, and Data Scientists

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Centered triangle with role icons at each vertex

मुख्य दृश्य तत्व

• •Interlocking triangle (coral, teal, gold)
• •Three role icons
• •Clean beige background

Data Warehouse vs. Data Lake

सामग्री

Visual comparison of Data Warehouse (structured, database icon) vs Data Lake (unstructured, waves icon)

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Two large icons side-by-side with labels

मुख्य दृश्य तत्व

• •Database cylinder icon (red)
• •Data lake waves icon (red)
• •vs. diamond badge

Warehouses and lakes create complexity

सामग्री

Three dimensions of complexity: two separate copies of data (Proprietary vs Open), incompatible interfaces (SQL vs Python), incompatible security/governance models (Tables vs Files)

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Three comparison rows with warehouse vs lake columns

मुख्य दृश्य तत्व

• •Three labeled comparison tables
• •Red text for problem labels
• •Clean minimal layout

Data Lakehouse

सामग्री

One platform to unify all data, analytics, and AI workloads. Combines Data Warehouse and Data Lake into a single layer supporting Streaming Analytics, BI, Data Science, and Machine Learning

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Centered architecture diagram with data flow from both warehouse and lake into unified layer

मुख्य दृश्य तत्व

• •Unified data layer with binary/gear icons
• •Four workload types at top
• •Data source icons at bottom
• •Orange chevron arrows

Why choose Databricks?

सामग्री

Side-by-side comparison of Modern Data Stack (Fivetran → Airflow → dbt → Snowflake → Looker/Tableau) vs Databricks Ecosystem (Fivetran + 100 tools → Object Storage → Auto Ingest → Delta + Spark → Delta Live Tables → Databricks SQL)

लेआउट संरचना

Two-column vertical flow comparison

मुख्य दृश्य तत्व

• •Tool logos: Fivetran, Airflow, dbt, Snowflake, Looker, Tableau
• •Databricks ecosystem flow
• •Hand-drawn title style

The data lakehouse offers a better path

सामग्री

Lakehouse architecture stack: Cloud Data Lake at bottom (Azure, AWS, GCP), open format storage, data processing, security/governance, and role-based experiences on top. Benefits: lake-first, AI/ML native, high reliability, multi-cloud

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Stacked architecture diagram on left, bullet points on right

मुख्य दृश्य तत्व

• •Green layered stack
• •Cloud provider logos
• •Bullet point benefits list

The Data Lakehouse Foundation

सामग्री

Section divider for the Data Lakehouse Foundation chapter

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White text on dark teal background with geometric accent shapes

मुख्य दृश्य तत्व

• •Dark teal background
• •Coral/teal geometric shapes
• •Databricks icon

Delta Lake

सामग्री

Delta Lake bridges Data Lake and Data Warehouse — an open approach to data management and governance. Key benefits: better reliability with transactions, 48x faster processing with indexing, fine-grained access control

लेआउट संरचना

Three-column layout: Data Lake icon | Delta Lake logo + benefits | Data Warehouse icon

मुख्य दृश्य तत्व

• •Delta Lake triangle logo
• •Data Lake waves icon
• •Data Warehouse cylinder icon
• •Dark teal side panels

What is Delta Lake?

सामग्री

Open source project for Lakehouse architecture on data lakes. Storage layer with ACID transactions for Spark. Key features: ACID Transactions, Scalable Metadata, Time Travel, Open Format, Change Data Feed, Unified Batch/Streaming, Schema Enforcement/Evolution, Audit History

लेआउट संरचना

Title with bullet points in two columns

मुख्य दृश्य तत्व

• •Two-column feature bullet list
• •Blog reference link

Delta Lake solves challenges with data lakes

सामग्री

Three challenges solved: Reliability & Quality → ACID transactions, Performance & Latency → Advanced indexing & caching, Governance → Governance with Data Catalogs

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Three rows with arrows from challenge to solution

मुख्य दृश्य तत्व

• •Teal bold category labels
• •Arrow connectors
• •Clean minimal layout

Delta Lake key feature - ACID transaction

सामग्री

Transaction log structure: Add File, Remove File, Update Metadata, Set Transaction, Change Protocol, Commit Info. Shows _delta_log directory with JSON commits and parquet file add/remove operations

लेआउट संरचना

Bullet list at top, two code/diagram panels below

मुख्य दृश्य तत्व

• •File tree diagram
• •Transaction log structure
• •Color-coded add (green) and remove (red) operations

State Recomputing With Checkpoint Files

सामग्री

Delta Lake generates checkpoint files every 10 commits in Parquet format. Shows checkpoint file structure, listFrom version mechanism, and concurrent read/write handling with optimistic concurrency

लेआउट संरचना

Two diagram panels showing file structure and read/write flow

मुख्य दृश्य तत्व

• •File tree with checkpoint.parquet
• •Spark logo for caching
• •User 1/User 2 concurrent access diagram

Building the foundation of a Lakehouse

सामग्री

Bronze-Silver-Gold medallion architecture: Bronze (raw ingestion/history) → Silver (filtered, cleaned, augmented) → Gold (business-level aggregates). Sources: Kafka, Kinesis, CSV/JSON, Spark. Consumers: Streaming Analytics, BI, Data Science/ML

लेआउट संरचना

Left-to-right data flow with three database tiers

मुख्य दृश्य तत्व

• •Three-tier database icons (bronze, silver, gold colors)
• •Source logos: Kafka, Kinesis, Spark
• •Quality arrow gradient

But the reality is not so simple

सामग्री

Complex real-world data pipeline diagram showing tangled dependencies between multiple sources, processing stages, and consumers. Maintaining quality and reliability at scale is complex and brittle

लेआउट संरचना

Messy pipeline diagram with many crossing dashed lines

मुख्य दृश्य तत्व

• •Tangled pipeline connections
• •Multiple database icons at each stage
• •Source logos: Kafka, Kinesis

Modern data engineering on the lakehouse

सामग्री

Data Engineering on Databricks Lakehouse Platform: Data ingestion → Data transformation → Data quality management → Automatic deployment & operations → Observability, lineage, and pipeline visibility. Scheduling & orchestration. Delta Lake open format storage at foundation

लेआउट संरचना

Layered platform diagram with data sources on left and consumers on right

मुख्य दृश्य तत्व

• •Layered teal platform blocks
• •Data Sources list
• •Data Consumers list
• •Delta Lake logo

Data Science & Engineering Workspace

सामग्री

Section divider for the workspace features chapter

लेआउट संरचना

Centered text on coral background

मुख्य दृश्य तत्व

• •Coral background
• •Light text

Databricks Workspaces: Clusters

सामग्री

Clusters provide computation resources for Data Analytics, Data Science, or Data Engineering workloads. Shows cluster configuration UI with Databricks Runtime 7.5 ML, Spark 3.0.1, Community Optimized driver type

लेआउट संरचना

Description text at top, full-width UI screenshot below

मुख्य दृश्य तत्व

• •Databricks cluster configuration screenshot
• •Sidebar navigation
• •Runtime version selector

Databricks Workspaces: Notebooks

सामग्री

Web interface for writing and executing code with runnable cells for files, tables, visualizations, and narrative text. Shows notebook with sampling strategies content and revision history

लेआउट संरचना

Description text at top, notebook screenshot below

मुख्य दृश्य तत्व

• •Notebook UI with code cells
• •Cluster attachment dropdown
• •Revision history panel

Databricks Workspaces: AutoLoader

सामग्री

Auto Loader incrementally processes new data files from cloud storage (GCS, DBFS). Before/After comparison: eliminates complex Notification Service + Message Queue + Airflow setup. Includes Scala code example

लेआउट संरचना

Description, before/after diagrams, and code snippet

मुख्य दृश्य तत्व

• •Before/After architecture comparison
• •Scala code snippet
• •Auto Loader icon
• •Supported format list

Databricks Workspaces: Jobs

सामग्री

Jobs run notebooks on scheduled basis for ETL, Model Building, etc. Shows a DAG workflow: Clicks_Ingest → Sessionize + Orders_Ingest → Match → Build_Features → Persist_Features + Train

लेआउट संरचना

Description text at top, DAG workflow diagram below

मुख्य दृश्य तत्व

• •Job DAG with duration labels
• •Sequential pipeline steps
• •Parallel branches

Databricks Workspaces: Delta Live Tables

सामग्री

Framework for declaratively defining, deploying, testing, and upgrading data pipelines. Shows DLT pipeline UI with graph view of table dependencies and event log

लेआउट संरचना

Description text at top, full-width DLT pipeline screenshot

मुख्य दृश्य तत्व

• •Pipeline graph visualization
• •Table schema panel
• •Flow progress event log

Databricks Workspaces: Repos

सामग्री

Repository-level integration with GitHub, GitLab, Bitbucket, and Azure DevOps. Developers can clone, manage branches, push/pull changes directly from the workspace

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Description text at top, Repos UI screenshot with file browser

मुख्य दृश्य तत्व

• •Repos file browser UI
• •Branch selector
• •Git hosting integration

Databricks Workspaces: Models

सामग्री

MLflow Model Registry for managing the entire lifecycle of ML models. Shows model versioning, stage transitions (Archived, Production, Staging), and pending requests

लेआउट संरचना

Description text at top, Model Registry screenshot with version table

मुख्य दृश्य तत्व

• •MLflow Model Registry UI
• •Version table with stages
• •Model description field

Governance requirements for data are quickly evolving

सामग्री

Section divider for the governance chapter

लेआउट संरचना

Centered text on coral background

मुख्य दृश्य तत्व

• •Coral background

Governance is hard to enforce on data lakes

सामग्री

Diagram showing complexity: 4 data types (Structured, Semi-structured, Unstructured, Streaming) × 3 clouds × separate security policies × multiple output copies

लेआउट संरचना

Flow diagram from data types through clouds through security to outputs

मुख्य दृश्य तत्व

• •Color-coded data flow lines
• •Cloud icons (3 clouds)
• •Lock/security icons
• •Document output icons

The problem is getting bigger

सामग्री

Enterprises need to share and govern diverse data products: Files, Dashboards, Models, and Tables

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Four icons in a row with labels

मुख्य दृश्य तत्व

• •Four red line-art icons
• •Clean minimal layout

Unity Catalog for Lakehouse Governance

सामग्री

Centrally catalog, search, and discover data/AI assets. Unified cross-cloud governance model. Integration with existing Enterprise Data Catalogs. Secure live data sharing with Delta Sharing

लेआउट संरचना

Unity Catalog screenshot on left, four bullet points on right

मुख्य दृश्य तत्व

• •Unity Catalog UI screenshot with schema/lineage views
• •Four feature bullet points

Delta Sharing on Databricks

सामग्री

Open protocol for secure data sharing: Data Provider → Delta Lake Table → Delta Sharing Server → Delta Sharing Protocol → Data Recipient (Power BI, Tableau, Spark, pandas, Java)

लेआउट संरचना

Left-to-right flow diagram from provider to recipient

मुख्य दृश्य तत्व

• •Delta Lake Table icon
• •Sharing Server icon
• •Recipient tool logos: Power BI, Tableau, Spark, pandas, Java, Linux Foundation

Machine Learning Workspace

सामग्री

Section divider for the ML workspace chapter

लेआउट संरचना

Centered text on green background

मुख्य दृश्य तत्व

• •Green background

ML Architecture: Data Warehouse VS Data Lakehouse

सामग्री

Side-by-side comparison: Data Warehouse (BI Apps → SQL Engine → Proprietary Storage) vs Data Lakehouse (ML Apps + BI Apps → Python/R + SQL Engine → Cloud Storage with OSS formats: PNG, UTF-8, Parquet)

लेआउट संरचना

Two architecture diagrams side by side

मुख्य दृश्य तत्व

• •Two architecture block diagrams
• •Proprietary vs Open storage formats
• •Red divider line

Data Science and Machine Learning

सामग्री

Data-native collaborative solution for full ML lifecycle: Collaborative Multi-Language Notebooks → Model Training/Tuning, Model Tracking/Registry, Model Serving/Monitoring, Automation/Governance → Open Multi-Cloud Data Lakehouse and Feature Store

लेआउट संरचना

Three-tier stacked platform diagram

मुख्य दृश्य तत्व

• •Three teal platform layers
• •Four ML lifecycle icons
• •Delta Lake logo at bottom

What Does ML Need from a Lakehouse?

सामग्री

Four requirements: Access to Unstructured Data (images, text, scale to petabytes), Open Source Libraries (TensorFlow, scikit-learn, R), Specialized Hardware (GPUs, cloud elasticity), Model Lifecycle Management (artifacts, lineage, productionization)

लेआउट संरचना

Four-quadrant layout with bullet points

मुख्य दृश्य तत्व

• •Four labeled sections
• •Clean text layout

Three Data Users

सामग्री

Business Intelligence (SQL, BI tools, Big Data, Warehouse), Data Science (R, SAS, Python, statistical analysis, small datasets), Machine Learning (Python, deep learning, GPUs, big datasets, unstructured data). ML section highlighted with teal border

लेआउट संरचना

Three-column comparison with role icons

मुख्य दृश्य तत्व

• •Three role icons (analyst, scientist, engineer)
• •Teal highlight border on ML column
• •Red dot dividers

How Is ML Different?

सामग्री

ML operates on unstructured data, requires massive datasets, uses open source DataFrames (not SQL), outputs models (not reports), and sometimes needs special hardware (GPUs)

लेआउट संरचना

Bullet list on left, large gear/person icon on right

मुख्य दृश्य तत्व

• •Red gear-person icon
• •Bold emphasized keywords
• •Vertical divider line

MLOps and the Lakehouse

सामग्री

Open tools for both training and operating models on the lakehouse. Models are data too. MLflow for MLOps: track/manage model data, lineage, inputs; deploy models as lakehouse services

लेआउट संरचना

Bullet list on left, delivery truck icon on right

मुख्य दृश्य तत्व

• •Red delivery truck icon
• •MLFlow bold emphasis
• •Italic emphasis on training/operating

Feature Stores for Model Inputs

सामग्री

Tables manage structured model input but lack: upstream lineage, downstream lineage, model caller integration, and real-time access. Feature stores solve these gaps

लेआउट संरचना

Bullet list on left, database icon on right

मुख्य दृश्य तत्व

• •Red database/feature store icon
• •Nested bullet points
• •Yellow accent dots

SQL Analytics Workspace

सामग्री

Section divider — Query data lake data using ANSI SQL with built-in query editor, alerts, visualizations, and interactive dashboards

लेआउट संरचना

Centered text on green background with description

मुख्य दृश्य तत्व

• •Green background
• •ANSI SQL bold emphasis

Databricks Workspaces: Queries

सामग्री

SQL query editor with autocomplete, table browser, and endpoint connection. Shows NYC taxi dataset query with column suggestions

लेआउट संरचना

Description text at top, query editor screenshot below

मुख्य दृश्य तत्व

• •SQL editor with autocomplete popup
• •Table schema browser
• •NYC taxi dataset

Databricks Workspaces: Dashboards

सामग्री

SQL dashboards combining visualizations: Cash Generated ($16M), Number of Sales (103K), Monthly Growth (13%), Average Basket ($154), Customer Summary map, Amount per Day chart, Product Category pie chart

लेआउट संरचना

Full-width dashboard screenshot with multiple widget types

मुख्य दृश्य तत्व

• •KPI cards
• •Geographic map
• •Time series chart
• •Pie chart
• •Sales dashboard

Databricks Workspaces: Alerts

सामग्री

Alerts notify when scheduled query results meet thresholds. Shows alert creation UI, alert list with triggered/OK states, and Generic Alert configuration with destinations (Pops, Platform, Test Webhook)

लेआउट संरचना

Description text at top, four UI screenshots in grid

मुख्य दृश्य तत्व

• •Alert creation form
• •Alert list with status badges
• •Alert configuration with destinations
• •Green-bordered notification panel

Databricks Workspaces: Query History

सामग्री

Query history showing SQL queries performed via SQL endpoints with execution details: duration breakdown (optimizing 35%, execution 64%, fetching 1%), rows returned, bytes read

लेआउट संरचना

Description at top, sidebar + query list + detail panel screenshot

मुख्य दृश्य तत्व

• •Query list with timestamps
• •Query detail popup with execution summary
• •Duration breakdown bar