Databricks for Beginners: Data Analytics with Python & SQL

Step 1: Sign Up for Databricks Community Edition

1. Visit the Website:
   Go to Community Edition in this lecture (Databricks Free Edition)

2. Fill Out the Signup Form:
   Provide your email address and create a password.
   Agree to the terms and conditions.

3. Submit the Form:
   Click the “Get Started for Free” or similar button to create your account for Free Edition

4. Verify Your Email:
   Check your email for a confirmation message.

Step 2: Log In to Databricks

1. Access the Login Page:
   Revisit the Community Edition page or use the login link in your confirmation email.

2. Enter Your Credentials:
   Use your email and password to log in.

3. Explore the Interface:
   Familiarize yourself with the main dashboard.

Step 3: Create a New Workspace

1. Understanding Workspaces:
   Dashboards to organize notebooks, files, and other resources.

2. Open the Workspace:
   Click on the “Workspace” section in the left sidebar.

3. Create a Folder (Optional):
   Right-click in the workspace area to create folders for organization.

Step 4: Set Up Your First Cluster

1. Go to Clusters:
   Click on “Clusters” in the left sidebar.

2. Create a Cluster:
   Click “Create Cluster.”
   Name your cluster and select settings (e.g., Spark version).

3. Start the Cluster:
   Click “Start” to initiate the cluster.

Step 5: Create a New Notebook

1. Navigate to Notebooks:
   Return to your Workspace.

2. Create a Notebook:
   Click “Create” -> “Notebook.”
   Name your notebook and select the language (Python, SQL, etc.).

3. Attach to Cluster:
   Attach your notebook to the running cluster to execute code.

Step 6: Load and Explore Data

1. Import Data:
   Use options like “Import” or “Upload” to add data files.

2. Start Exploring:
   Write basic queries or data manipulation tasks in your notebook.

Step 7: Visualize Data

1. Using Built-in Tools:
   Use commands and libraries like Matplotlib for visualizations.

2. Run Visualizations:
   Execute cells to display plots inline.

Step 8: Share and Collaborate

1. Sharing Notebooks:
   Use the share button to send links to collaborators.

2. Explore Collaboration Features:
   Utilize comments or interactive discussions for teamwork.

This lesson tours the Databricks workspace UI, explaining the sidebar, workspace browser, Catalog, Search, Compute, and ML/Experiments panels. Students learn how to create and organize notebooks and folders, locate catalogs and tables, manage compute resources, use search to find notebooks/queries/dashboards, and update profile and workspace settings — enabling efficient navigation and workspace management in Databricks.

What students will be able to do after this lecture :

• Navigate the Databricks workspace UI: sidebar, Workspace, Catalog, Search, Compute, and Experiments panels.

• Create, organize, rename, and delete workspace objects (folders, notebooks, experiments).

• Use Search to find notebooks, queries, dashboards, tables, and other workspace assets quickly.

• Understand Catalog/Unity Catalog visibility and locate registered tables and data assets.

• Create and attach notebooks to clusters and manage cluster lifecycle for running code.

• Configure basic user profile and workspace settings (display name, password, account actions).

• Use workspace shortcuts to create tables, upload data, start notebooks, and launch AutoML/experiments.

• Apply workspace best practices: organize projects, manage access, and keep reproducible notebooks.

• Troubleshoot common UI issues (no compute attached, missing permissions, locating uploaded files).

This lesson shows how to create and manage a Databricks compute (cluster) in Community Edition, choose a runtime, and understand basic cluster settings and lifecycle. Students learn practical tips for starting, attaching, and terminating clusters, selecting runtime versions, and using the cluster UI (logs, Spark UI, configuration) so they can run notebooks and jobs reliably.

What students will be able to do after this lecture :

• Create and start a Databricks cluster (compute) in Community Edition and choose an appropriate runtime.

• Attach notebooks to a running cluster and run Spark workloads.

• Understand cluster lifecycle behavior (auto-termination, pending/starting states) and manage start/stop/terminate actions.

• Select or change Databricks Runtime versions and basic Spark configuration options.

• Access cluster metadata (driver logs, Spark UI) when available and locate JDBC/ODBC connection info.

• Add or remove libraries and view cluster configuration JSON.

• Follow best practices: avoid leaving clusters running, use auto‑termination, and pick suitable runtimes for compatibility.

• Troubleshoot common cluster issues: pending state, missing Spark UI, and termination due to idle time.

In this lesson students learn how to create and configure a Databricks notebook, select a default language (Python/SQL/Scala/R), attach the notebook to a running cluster, and run code cells. The module demonstrates notebook basics (cells, run controls, markdown vs code) and shows how notebooks integrate with cluster resources for interactive development.

What students will be able to do after this lecture:

• Create a new Databricks notebook and set its default language.

• Attach the notebook to a running cluster and verify the cluster status.

• Run, interrupt, and navigate code cells (keyboard shortcuts, add/remove cells).

• Use markdown cells for documentation and code cells for executable scripts.

• Execute simple Python commands (e.g., print statements) and read cell outputs.

• Understand notebook UI controls: run all, publish, attach/detach cluster, and save.

• Use notebooks as interactive development environments for data engineering, EDA, and prototyping Spark jobs.

In this lesson students learn end-to-end table creation in Databricks: downloading datasets (e.g., from Kaggle), uploading CSVs to DBFS, creating tables via the Databricks UI, inspecting schema and sample data, and running SQL queries in notebooks. The module teaches practical steps to prepare data for analysis and sets up a reproducible workflow for downstream EDA and reporting.

What students will be able to do after this lecture:

• Download datasets from web sources (e.g., Kaggle) and prepare CSV files for upload.

• Upload files to Databricks File System (DBFS) and locate them under /FileStore/tables/.

• Use the Databricks “Create Table” UI to import CSVs, set schema options (header, types), preview data, and create managed tables.

• Attach notebooks to clusters and verify table creation in the Catalog/Unity Catalog.

• Run basic SQL queries in notebooks (SELECT, LIMIT) to inspect and sample data.

• Troubleshoot common issues: missing headers, incorrect schema inference, cluster not attached, and file-path errors.

• Apply best practices: preview/schema validation, using descriptive table names, and documenting steps with Markdown for reproducibility.

• Move from raw data to a queryable table so they can perform aggregation, visualization, and more advanced analyses.

In this lesson students learn how to ingest CSVs into Databricks Community Edition, create tables via the UI, run SQL queries, and build visualizations from query results. The module walks through notebook setup, using dbutils and the table UI, defining schemas, querying with SQL, and saving interactive charts — enabling reproducible, documented EDA within Databricks.

What students will be able to do after this lecture:

• Sign in to Databricks Community Edition, create/attach notebooks to clusters, and navigate workspace panels.

• Upload CSV files to DBFS and locate their paths (dbfs:/FileStore/tables/) using the UI and dbutils.fs.

• Create tables with the Databricks “Create Table” UI and specify/adjust schemas.

• Run SQL queries in notebooks to explore data (SELECT, COUNT, GROUP BY, ORDER BY) and retrieve results.

• Annotate notebooks with Markdown (%md) to document steps, findings, and reproducible analysis.

• Build and customize visualizations from SQL query outputs using the visualization editor (bar charts, scatter, etc.) and save them.

• Aggregate and summarize data (e.g., count distinct titles by release_year) and export results for further analysis.

• Share or publish notebooks and visualizations for collaboration and reporting.

• Troubleshoot common issues: cluster not attached, incorrect paths, schema mismatches, and performance considerations for large datasets.

• Apply this workflow as a repeatable EDA pipeline before advancing to statistical tests, ML modeling, or production pipelines.

In this lesson students learn to locate files on DBFS and load them into Databricks notebooks using Python and Spark. The module covers listing directories with dbutils.fs, discovering file paths, reading CSVs into Spark DataFrames, and basic notebook/cluster setup so learners can reliably access and ingest data for downstream analysis.

What students will be able to do after this lecture :

• Create or open a Databricks notebook and attach it to a running cluster.

• List and inspect files on DBFS using dbutils.fs.ls and iterate to find exact file paths.

• Load CSV files from DBFS into Spark DataFrames with spark.read.csv and display results.

• Understand common file-location patterns (dbfs:/FileStore/tables) and how uploaded files are stored.

• Verify file paths and metadata before loading to avoid path/key errors.

• Convert Spark DataFrames to pandas (when needed) for local analysis or plotting.

• Troubleshoot common issues: cluster not attached, incorrect path, missing headers, and large-file read delays.

• Apply these steps as a repeatable workflow to ingest new datasets into Databricks for EDA and modeling.

In this lesson students learn to compute group-level summaries in Spark/pandas and create clear comparative visualizations to show average IMDb scores by production type. The module covers grouping and aggregation, converting Spark results to pandas, plotting bar charts with Seaborn, and customizing axes, limits, and layout to produce publication-ready visuals that reveal differences between Movies and Shows.

What students will be able to do after this lecture :

• Group data and compute aggregate metrics (mean, count) by category in Spark and pandas.

• Convert aggregated Spark DataFrames to pandas for visualization.

• Build and customize bar charts with Seaborn/matplotlib (titles, labels, y-limits, grid, tight_layout).

• Interpret group-level comparisons and explain what differences in means imply.

• Validate results by checking sample sizes and variability before making business recommendations.

• Translate visualization findings into business insights (content strategy, commissioning, recommendation tuning).

• Extend the analysis with additional summaries (median, std, counts) and visual checks (error bars, boxplots) to assess robustness.

In this lesson students learn to create and interpret boxplots to compare distributions across categories. Using Spark-to-pandas workflows and seaborn, the module covers selecting relevant columns, cleaning numeric scores, and plotting distributional summaries (median, IQR, whiskers, outliers) to compare IMDb ratings between content types.

What students will be able to do after this lecture :

• Select and sample relevant columns from Spark and convert to pandas for visualization.

• Clean and convert score columns to numeric, handling invalid values (NaNs).

• Produce boxplots with seaborn to compare distributions across categories (e.g., Movie vs Show).

• Read boxplot components: median, IQR, whiskers, and outliers, and explain their significance.

• Compare central tendency and variability between groups to draw data-driven observations.

• Use boxplots to detect skewness and outliers and decide whether further cleaning or robust statistics are needed.

• Translate distribution insights into simple business implications (content quality, consistency, and target strategy).

• Extend analysis by adding counts, overlaying swarm/strip plots, or computing group summary statistics (mean, median, std, n).

In this lesson students learn to sample Spark tables , convert to pandas for plotting, clean and standardize categorical labels, convert and validate numeric scores, and build clear comparative visualizations (barplots and boxplots) to summarize average IMDb ratings by age certification. Emphasis is on practical EDA steps and reproducible plotting in Databricks.

• What students will be able to do after this lecture :
  

  • Select relevant columns and sample data from Spark DataFrames for fast analysis.

  • Convert Spark DataFrames to pandas safely for visualization.

  • Inspect and standardize categorical labels (e.g., shorten/clean age_certification).

  • Convert text to numeric (pd.to_numeric) and drop or handle invalid values (NaNs).

  • Compute group-level summary statistics (means, counts, std) by category.

  • Create clear comparative charts (barplots with means, boxplots to show spread/outliers) using seaborn/matplotlib.

  • Annotate plots with sample sizes or error metrics to assess reliability.

  • Diagnose common data issues (small category counts, misparsed labels, outliers) and know when to question results.

  • Translate visualization findings into business insights (audience targeting, content strategy, personalization, licensing decisions).

In Section 3 — Working With Data in Databricks: Data Analysis on tracking IMDb Score Trends over the years with Seaborn, students learn hands-on methods for loading messy CSVs into Spark, extracting and assigning correct headers, detecting and fixing misnamed or malformed columns, converting types with Spark-safe methods, filtering invalid values and outliers, aggregating at scale, and preparing results for visualization with pandas and Seaborn. The module emphasizes practical debugging of real-world issues (misparsed headers, Spark vs pandas syntax, cast vs to_numeric, and corrupted ranges) so learners can produce reliable trend analyses.

What students will be able to do after this lecture

• Load raw CSVs into Spark and correctly extract/assign header names.

• Inspect datasets (columns, head, descriptive stats) to spot anomalies.

• Rename misnamed columns to avoid KeyErrors and downstream failures.

• Convert and validate numeric columns in Spark (cast) and pandas (to_numeric).

• Filter out invalid values and remove outliers to prevent distorted aggregates.

• Use Spark DataFrame APIs (.filter, withColumn, groupBy, agg) correctly instead of pandas boolean indexing.

• Convert aggregated Spark results to pandas for plotting with Seaborn/matplotlib.

• Create clear time-series visualizations (line plots with proper ticks, labels, layout).

• Diagnose and fix common data issues that produce nonsensical trends (e.g., scores outside 0–10 or years that are non-numeric).

Why Data Cleaning Matters : Identify common data quality issues : extreme values and invalid entries,
address invalid IMDb scores, understanding how proper cleaning prevents flawed visualizations and misleading conclusions, learning systematic approaches to efficiently detect, filter, and correct data problems

Working With Data in Databricks: Data Analysis, students learn practical data-cleaning and exploratory analysis techniques using Spark and pandas within Databricks. The lesson walks through inspecting datasets, identifying and fixing invalid or misnamed columns, handling missing values and outliers, converting types correctly in Spark, performing aggregations, and preparing cleaned data for visualization with matplotlib/seaborn. Real-world debugging examples (KeyErrors, incorrect ranges, Spark vs pandas syntax differences, and cast vs to_numeric) are used to demonstrate common pitfalls and solutions.

What students will be able to do after this lecture

• Inspect and diagnose dataset issues: list columns, view sample rows, and interpret descriptive statistics to spot anomalies.

• Correctly identify and rename misnamed columns so downstream code runs without KeyErrors.

• Clean numeric columns: convert types safely in Spark (using cast) and pandas (to_numeric), handle invalid conversions, and filter unrealistic values.

• Remove or mitigate outliers using IQR or threshold-based rules to prevent distorted analysis.

• Use Spark DataFrame APIs (.filter, withColumn, F.col) instead of pandas boolean indexing when working at scale.

• Aggregate data in Spark (groupBy + agg) and convert results to pandas for plotting.

• Produce clear, readable visualizations (line plots with appropriate ticks, labels, and layout) that reflect cleaned data.

• Troubleshoot common errors: ValueError from mixing Spark/pandas indexing, AttributeError for non-existent Spark functions, and logical bugs from incorrect header handling.

Suggested next steps (brief)

• Apply these cleaning steps to a sample dataset in Databricks notebook.

• Compare results before and after cleaning to see the impact on aggregated metrics and plots.

• Extend the lecture by adding automated validation checks (row counts, range assertions) to prevent regressions.